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As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.
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ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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Geo-Information Harvesting from Social Media
Data
Xiao Xiang Zhu, Yuanyuan Wang, Mrinalini Kochupillai, Martin Werner, Matthias Ha¨berle, Eike Jens Hoffmann,
Hannes Taubenbo¨ck, Devis Tuia, Alex Levering, Nathan Jacobs, Anna Kruspe, Karam Abdulahhad
AbstractThis is the pre-acceptance version, to read the final
version please go to IEEE Geoscience and Remote Sensing
Magazine on IEEE XPlore.
As unconventional sources of geo-information, massive im-
agery and text messages from open platforms and social media
form a temporally quasi-seamless, spatially multi-perspective
stream, but with unknown and diverse quality. Due to its
complementarity to remote sensing data, geo-information from
these sources offers promising perspectives, but harvesting is not
trivial due to its data characteristics. In this article, we address
key aspects in the field, including data availability, analysis-
ready data preparation and data management, geo-information
extraction from social m edia text messages and images, and the
fusion of social media and remote sensing data. We then showcase
some exemplary geographic applications. In addition, we present
the first extensive discussion of ethical considerations of social
media data in the context of geo-information harvesting and
geographic applications. With this effort, we wish to stimulate
curiosity and lay the groundwork for researchers who intend to
explore social media data for geo-applications. We encourage the
community to join forces by sharing their code and data.
Index TermsSocial media, geo-information, remote sensing,
machine learning, ethics, data fusion
I. INTRODUCTION
Geodetically accurate remote sensing (RS) data acquired by
Earth observation (EO) satellites serves as a high quality refer-
ence database for global geo-information retrieval. Beyond the
temporal resolution of EO satellites, typically days, the con-
textual embedding of space into meanings, perceptions, and
dynamic changes in human settlement due to daily life routines
can only be indirectly assessed by ground-level measurement,
such as social media data. Taking building function prediction
as an example, building fac¸ades and detailed building func-
tional information retrievable from ground-level social media
imagery are not accessible from satellites. Such information
can also be utilized to generate training sets for supervised
classification with satellite images.
A new era of Earth observation (EO) has certainly ar-
rived, when we consider the social media data (photos, text
messages) uploaded by individuals as a valuable additional
information source of Earth “observation.” As of this writing
(April, 2022), around 3.96 billion people use social networking
X. Zhu is with the Chair of Data Science in Earth Observation, Technical
University of Munich (TUM), Germany. Email: xiaoxiang.zhu@tum.de (cor-
responding author). YW, MK, MW, MH, EJH, AK are with TUM, Germany;
HT and KA are with German Aerospace Center, Germany; DT is with
Ecole Polytechnique Fed erale de Lausanne, Sion, Switzerland; AL is with
Wageningen University, the Netherlands; NJ is with W ashington University
in St. Louis, USA)
sites [1], such as Facebook. As shown in Fig. 1, a subset
of selected social media platforms already provided 3 billion
daily photo-uploads in 2015 and estimates suggest hundreds
of petabytes of them are available in total. Apart from internet
images and text messages, 2D geographic information systems
(GISs), digital cadastral databases, and municipal utility infor-
mation are widely available for most well developed countries.
Besides paid services, openly available sources of 2D GIS
data include Natural Earth, Geocommons, MapCruzin, Open-
StreetMap (OSM), and many more. Exploiting and extracting
the valuable information from these data sources enables a
revolutionary complement to satellite remote sensing. The
extracted geo-information from these observations will support
cartographic applications, civil security, and city planning,
among many other domains, and hence change the way we
manage our cities.
Research fields in social media data mining and outsourcing
sensing tasks to the general public are rapidly emerging,
especially for 3D urban reconstruction from social media
imagery [2][5], people dynamics monitoring using airborne
sensor and mobile phone data [6], [7], flood damage map-
ping using governmental and crowdsource data [8], [9], and
crowdsourcing for mapping, image analysis, and geographic
information collection [10][13] (and the list goes on). The
exponential increase of social media data ignites a new means
of remote sensing that involves the community, also known as
community remote sensing. The real strength of social media
in remote sensing is its complementarity in data characteristics
and population base.
Only a handful of contributions have addressed the problem
of fusing social media and RS data for geo-information
retrieval. As [14] mentions, few studies on social media text
messages are linked to remote sensing data. Most of them
focus on result-level spatial merging. The current research has
not addressed the real challenge of handling the heterogeneous
big data delivered by EO satellites and social media. The
seemingly unrelated remote sensing science and daily social
life happen to coincide by their nature as “big data.” Many
studies have shown that processing hundreds of thousands of
online images and millions of online text messages is now
possible [3], [15]. Consequently there is an impulse to develop
a sophisticated system that effectively mines their information
and coherently fuses them.
In this article, we discuss key aspects of geo-information
harvesting from social media data, including social media data
availability (Section II), social media data pre-processing and
management (Section III), geo-information extraction from
ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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social media text messages (Section IV) and social media
images (Section V), and the fusion of social media and remote
sensing data (Section VI). Section VII showcases exemplary
geographic applications. For the first time, we also extensively
discuss ethical consideration of research with social media data
(Section VIII) in the context of geo-information harvesting and
geographic applications. Our aim is to inspire more researchers
to explore social media data as an unconventional data source
of geo-information, and provide a good basis for doing so.
II. SOCIAL MEDIA DATA AVAILABILITY
Social media data is the information collected from social
networks representing how users share, view, or engage with
internet content and with each other. It is mainly composed
of large quantities of photos, videos, and text messages, but
also exists in many other forms, such as emoticons, product
ratings, volunteered geographic information, and so on.
Despite the large quantity of social media data created every
day, most of it is not available for geo-information extraction,
mainly due to the license terms, the data crawlability, and
the availability of geo-tags. Social media data is spread out
on various online platforms that have different terms of use.
For example, photos posted on Facebook usually do not
have an open-access license, making massive crawling and
processing of the photos on Facebook impossible. The same
conditions apply to many other social media platforms, such
as Instagram. In contrast, text messages posted on Twitter are
by default open unless users restrict access; hence they can
be crawled extensively. Twitter also provides an official API
that permits massive download of tweets. Another issue is the
geographic location contained in the data, as it is the key to
linking the data to geographic applications. For this reason,
this paper focuses on geographically harvestable social media
data. We require that photos and text messages are either geo-
tagged, or it is possible to infer their geographic locations
with reasonable precision (in a building block level). Tabel I
summarizes the available harvestable social media based on
these s requirements. The list is of course not exhaustive. It is
intended to give readers the most common sources of social
media data for geo-information retrieval. Most freely available
social media data is under the creative commons license, often
the CC-BY license. The license of their images vary from
platform to platform. We intend to give readers guidance on
what type of social media sites and what data licenses are
suitable for scientific research. For example, although CC-BY
allows free use of the data, we note that a CC-BY license
does not permit sublicensing. This means that posting or
publishing such data on certain websites or journals requires
special attention such as a sublicense or even the transfer
of ownership, which is sometimes required by the journal
publisher.
Our extensive research shows that among all these options,
Twitter and Flickr are the most generous in terms of the
total data volume and data harvestability, because of their
license terms and the functionality of the APIs. Volunteered
geographic information such as OSM is also accessible in
large volume. However, they are not strictly social media data,
hence not the focus of this paper. We will focus on tweets and
Flickr images in the following.
Twitter: Twitter offers several API packages with different
pricing levels. In this article, we focus solely on the freely
available Twitter API, which allows the user to stream ap-
proximately 1% of the daily Twitter stream of an area of
interest (AOI). Further, the API offers several techniques to
query tweets. If a researcher is interested in a special hashtag1
or keyword like #COVID19, it is possible to submit such a
request to the API, and receive tweets with matching contents
as a result. An AOI can be specified via a bounding box of a
city, region, or country. It is also possible to receive the 1%
stream without any keyword or hashtag filtering. A further
method to receive tweets is to download a user’s timeline
or “hydrate” tweet IDs, i.e., retrieve tweet content based on
IDs. This is particularly relevant in research as data sets are
usually only shared in this format due to Twitter’s license
terms prohibiting sharing of complete tweet data.
Flickr: Flickr, a social media image platform, offers a
powerful free API allowing arbitrary spatio-temporal queries.
It can therefore be used to create comprehensive worldwide
data sets, and is therefore often the first choice for image data.
Flickr images have been used in various studies across all
disciplines, e.g., [16][19]. In contrast, Facebook used to have
an open API in its early days, but changed towards a more
privacy-preserving one as it became more popular and allowed
users more fine-grained options on the visibility of posts.
Others: Instagram closed their API in April 2018 and
redesigned it to be used by businesses and external apps
to curate the profile of a user. All access to query data
specifically and randomly by time, location, or tags has been
removed. Snapchat’s API never offered any query features but
targeted creation and publication of advertisements. Beyond
these popular examples, there are several other platforms
providing user-contributed images with geo-reference. Among
them are Google Places and Foursquare, databases of points-
of-interest with user images and reviews, Geograph, a platform
systematically acquiring landscape photos across Great Britain
and Ireland, and Mapillary, which aims to build a catalogue of
street view imagery based on volunteers driving around with
their own cameras. Until November 2016, Panoramio was a
valuable source of geo-tagged images on a global scale and
its data was used for several studies (e.g., [20]). This service
has since been shut down, but its imagery is still available as
a part of Google Maps.
Our recommendation is that readers use Flickr and Twitter
for large-scale applications. Mapillary, Google Places, and
Foursquare are good for study of specific locations. All of
them provide official APIs.
III. SOCIAL MEDIA DATA MANAGEMENT
With respect to data management, a key aspect of social
media data streams is that they represent a source of big
data. Of the defining properties of big data volume, variety
1Hashtags are keywords to tag a tweet with a certain topic, event, celebrity,
etc. and consist of a word or word sequence without trailing spaces and a
leading “#”. For example #RemoteS ensingIsGreat.
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TABLE I
AVAILABLE SOCIAL MEDIA DATA
Platform
Type
Description
License
Crawlability
Geotag
Google image search
Image
search engine
Partly CC or free to use
Third-party crawlers
partly
Flickr.com
Image
photographer website
Partly CC
Official API
partly
Unsplash.com
Image
photographer website
All CC or free to use
Official API
mostly
Pexels.com
Image
photographer website
All CC or free to use
Official API
mostly
Magdeleine.co
Image
photographer website
All CC0 or CC-BY
Official API
few
Twitter.com
Text and image
social sharing website
posts public by default
Official API 1% of all
partly
Instagram.com
Text and image
social sharing website
posts public by default
Third-party crawlers
partly
OpenStreetMap.org
vector and text
Online map service
Open Data Commons Open Database
Official API and DB dumps
all
Mapillary.com
Image
Online map service
CC BY-SA
Official API with limited quota
all
Google Places
Text and image
POI service
Proprietary
Official API
all
Foursquare
Text and image
POI service
Proprietary
Official API
all
Geograph.org.uk
Text and image
Online map service
CC BY-SA
Official API and DB dumps
all
Fig. 1. Daily number of photos updated on selected platforms (© KPCB).
and velocity [21] velocity is the most obvious. However,
the other two facets are also observed when working with
location-based social media streams. The high velocity at
which messages are generated leads to a huge number of
messages, leading to significant volume. While these messages
are often small, they may have bigger data assets associated
with them, including images and videos for increased volume.
Furthermore, there is a high degree of variety, from a technical
point of view, in terms of the nature and quality of the location,
time, and other metadata, from image and attachment file
formats and semantics. The variety is also high from a user
perspective. Including messages from professional marketing
agencies, ethically sound information dissemination bots, bots
actively engaging in specific topics to produce an impact on the
perception (which can be considered unethical), users tweeting
with their “professional personality,” users using the network
in a “private” setting, together with the network role and
weight of a user (e.g., influencer vs. small network user), and
even mixtures of all of these.
From a data management perspective, it is first most impor-
tant to handle the big data characteristics. Distributed systems
for taking care of data management are necessary. This implies
that we can have only a single consistent key [22], and in fact
infrastructures using key-value data representations dominate
the field. With respect to key-value stores themselves, readers
are referred to [23], [24].
A key in this context can be either a number or a short string.
Depending on the use case, choosing these keys may require
considerations of data locality versus random hash functions,
which has implications for data access speed and node usage
balancing.
For some geospatial applications on a global scale, query
distribution will be quite uniform (e.g., generating a worldwide
map) where data locality can be fruitful; for others it will
be extremely local (e.g., disaster response) and random data
distribution is a better choice. For example, if all data from the
area of New York can be found in only few nodes, answering
a single query about New York is efficient as only these
few nodes need to be asked and coordinated for a definitive
answer. However, if the majority of queries are relevant to
the New York area only, then from a big distributed system
only a few nodes can contribute and will become a bottleneck.
Therefore, an optimal system needs to be designed with both
data distribution and query distribution in mind.
The keys themselves can be built by a combination of
random information, time, location, topic, hashtags, and other
criteria. Spatial information can be integrated either as a set
of keys (e.g., rectangles) or through the mechanism of space-
filling curves. Such curves enable us to approximately map
between 2D, 3D, or 4D space and a plain integer key which is
used for ordering. In [25] an exemplary keying scheme based
on message timestamps and a hash encoding of geolocations
is presented. The locality of this pure spatiotemporal scheme
is then reduced by introducing random characters.
In summary, managing social media data entails spatio-
temporal data management as well as management of big data.
Therefore, key-value structures are used to guide the low-level
organization of the data and of the mapping of data to nodes.
In current cloud computing infrastructures as well as in HPC-
environments, a good dose of randomness needs to be inserted
to avoid hotspots when managing a global dataset for local
queries or local data for global queries.
IV. GEO-INFORM ATION EXTRACTION FROM SOCIAL
MEDIA TEXT MESSAGES
As discussed in section II, Twitter is the most salient social
media source with a focus on the text domain. In this section,
we discuss various aspects of extracting geo-information from
this source. In the past decade, Twitter has developed into a
major service for sharing small texts which are called tweets
(see Fig. 2). A tweet can include a news headline, an open
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Fig. 2. Two-month world-wide Twitter sample with approximately 3M geo-referenced tweets.
position within a company, traffic information for a city,
tomorrow’s weather, a web URL to an interesting website,
or more personal messages like feelings or opinions. All this
information must be packed within 240 characters.
A tweet can be analyzed to determine whether it includes
(implicit) information about the surrounding environment and
thus, can reveal further data about nearby urban characteris-
tics, demographic information of a region, or events in the
neighborhood. Due to the massive amount of data, automatic
methods from the field of Natural Language Processing (NLP)
are necessary here. This section gives an overview of such
methods commonly used for geo-information extraction from
social media material [26]. The algorithms and techniques
shown here are widely language independent (or alternatives
are depicted) and are applicable to all text-based social media.
A key challenge when using Twitter data for geo-information
extraction is the attachment of geolocations to tweets. The
last part of this section discusses direct availability of such
locations as well as solutions for obtaining tweet locations in
other ways.
A. Twitter Data Format and Pre-processing
Tweets from the Twitter API are usually encoded as json
[27] objects, which include several attributes. One provides
information about the poster, including the user ID, user name,
user language, a user description, sometimes the hometown,
etc. Data about the tweet itself is also attached, such as the
original and unprocessed tweet text. Further attributes are the
tweet ID, the estimated language, a timestamp, a human-
readable time of creation (UTC), and many others.
Before feeding the collected data into NLP or machine
learning algorithms, it needs to be pre-processed because the
raw social media text most likely includes informal spelling,
typos, and creative use of punctuation like emoticons or
emoji.2 Common pre-processing steps include deleting URLs
and user names, stripping punctuation marks and numbers,
removing stop-words (and, the, a, . . . ), setting all characters to
lowercase, and removing emoji. Of course, the choice of these
methods depends on the use case. Subsequent pre-processing
steps may involve lemmatization or stemming which convert
all words into their roots, or normalization where irregular
spellings like yeeeeeeeees are corrected using simple rules or
lexical knowledge. Widely used NLP libraries like spaCy [28],
NLTK [29], or Gensim [30] offer implementations of these
methods.
Many common techniques are particular to space-separated
languages. For others like Japanese or Chinese, different
approaches are necessary. For example, the Python library
Janome [31] can be used to tokenize Japanese strings. It makes
use of the MeCab dictionary [32], [33] including the Japanese
new era (Reiwa) dictionary. For the Chinese language, jieba
[34] has proved a useful tool for tokenizing Chinese text
sequences.Some downstream NLP methods may require their
own preprocessing steps, such as BERT (see section IV-B).
The methods discussed here mark a good starting point
to pre-process Twitter, Weibo, or other (social media) text.
However, there is no golden rule for text pre-processing and
it always depends on the algorithms used for the desired task.
2Emoji are ideograms and pictograms depicting smiley faces with different
sentiments, fruits, activities, items, or flags.
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B. Methods
1) Information Retrieval: Aside from deep learning algo-
rithms (which are discussed later), there are several “classic”
information retrieval algorithms that are based in statistics. A
widely used unsupervised algorithm to assign documents to
certain topics in text documents is latent Dirichlet allocation
(LDA, [35]). A topic can be seen as a word pattern that occurs
in several documents (e.g., in a string or a tweet) and is
represented as a bag-of-words. Documents with the same or
similar word patterns are assumed to be related and therefore
are clustered to a certain topic. Recently, [36] introduced an
extension to LDA called Archetypal-LDA (A-LDA) which
specializes in short texts like tweets using anchor words.
Anchor words can be seen as a seed to “guide” the LDA topic
inference. Hashtags were used as anchor words to work out
topics around certain hashtags and therefore support the topic
inference. This method could be useful in geo-spatial research,
e.g., event detection, where certain hashtags are related to a
specific event. Tweets without that hashtag could therefore be
utilized as well.
LDA has been used in various studies in the geo sciences.
For example, [37] used tweets and Flickr images for a multi-
label land-use classification in New York and San Fran-
cisco. They applied LDA to extract relevant topics related to
Foursquare venues in specified clusters. The relevant clusters
were calculated by HDBSCAN [38] to identify local hot spots
in the Flickr images. Before they applied LDA, the text was pre-
processed by removing stop-words and applying lemma-
tization. Only those tweets within the clusters detected before
were used. LDA revealed the relevant topics for each cluster,
which led to good classification performance. LDA was also
used in [39] to identify relevant topics of the Olympics Games
2012 in London within the context of city planning. First, the
tweets were pre-processed with the aforementioned methods;
thereafter, they used LDA to filter the tweets that were about
the Olympic Games and transportation. Tweets that were re-
lated to the mentioned topics were used to perform a sentiment
analysis and a spatial-temporal analysis. [40] investigated the
tracking and monitoring of Twitter topics related to a disaster
over time. Since LDA is not suitable for tracking topics over
time, they used a dynamic topic model [41] (DTM), which is
based on LDA.
Another classic, but still very popular [42] algorithm is
term frequency-inverse document frequency (TF-IDF) [43].
Basically, TF-IDF measures the relevancy of a term (e.g., a
word) in a document and weights the document’s importance,
for example for a search query. This weighing process
involves the combination of two different steps. First, the
standard way of determining the term-frequency (TF) of a
certain term t in a document d is to calculate the quotient
of the total count of t appearing in d by the complete count
of all terms in d. Second, a stop-word like “the” would
distort the document-weighting because it is likely to appear
very frequently within a document. Therefore, the inverse
document frequency (IDF) decreases the importance of the
frequently recurring terms. This is achieved by taking the
logarithm of the quotient of the total number of documents
and the count of d including that certain term t. The final TF-
IDF score for a term is computed by the product of TF and
IDF.
2) Sentiment Analysis: As pointed out in Section II,
social media data can also contain emotions. One rule-based
algorithm to determine sentiments in English (but not
limited to) Twitter text data is Valence Aware Dictionary for
sEntiment Reasoning (VADER) [44]. The authors compiled
a list of sentiment-loaded terms. Those terms can be words
like ”happy” or emoticons like ”:-)”. Furthermore, the authors
added support for emoji sentiment detection. The terms are
validated and ranked by humans (Amazon Mechanical Turk).
In the end, the list comprises 7520 of such terms. Every term
in the list comes with a mean sentiment intensity ranging from
4 (very negative) to 4 (very positive) and ten independent
human ratings. Next, a qualitative approach was used to
detect the main textual drivers of the perceived sentiment
intensity. With this, five heuristics incorporating grammatical
and syntactical clues are derived to determine the sentiment
intensity of a string or possible changes of the sentiment. For
example, exclamation points add some sentiment intensity to
a string, as does using all-caps to stress words that express
the intended sentiment. Furthermore, booster words like
”very” also contribute to the computation of the sentiment
intensity. VADER achieved good performance and was able
to outperform human reviewers in some cases.
3) Embeddings: Today, the field of Natural Language
Processing (NLP) has seen a strong shift towards machine
learning, and particularly deep learning, approaches, which
in many cases now outperform previous methods based in
linguistics [45]. The topic of social media analysis is no
exception here, and most large-scale systems now employ deep
neural networks [46].
A crucial issue that caused the relatively late introduc-
tion of deep learning to text data analysis (as opposed to
other forms of data, like images) lies in how to represent
words numerically. Traditionally, this has been done with one-
hot encodings that do not capture semantic meaning. The
key development here was the introduction of word
embeddings. These embeddings are neural networks them-
selves, and are part of the complete classification network.
Some very successful early approaches that are still in use
today are word2vec [47], GloVe [48], and fastText [49]. One
of the earliest, word2vec is a neural-based architecture that
can embed large vocabularies from huge text corpora into
an n-dimensional feature space very quickly. It can preserve
semantic and syntactic features of words and embeds similar
word close together in the feature space. This development
was followed by GloVe, which is a statistics-based approach.
To solve the issue of out-of-vocabulary (OOV) words, [49]
introduced the fastText algorithm, which divides words into
subwords. This process facilitates the approximation of OOV
word vectors by composing a word vector based on the word’s
subwords, which is particularly useful for irregular text as it is
often found on social media. In each case, it is very common to
use pre-trained embeddings, such as those provided for fastText
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in 157 languages [50].
Urban areas are multilingual spaces [51][53] and the set
of languages discovered in social media posts in cities is
diverse [54] (of course, English is dominant on Twitter [55]
and Chinese on Weibo). However, multilingual approaches are
rare in earth sciences and so offer interesting research oppor-
tunities, such as ensemble models covering all languages to
classify building functions in urban areas [56] or investigating
the information density of Japanese or Chinese social media
postings with respect to English [57] within the context of
urban land use tasks.
As applied in [58], embeddings that represent not just
words but whole sentences are also becoming used more
widely, e.g., the Universal Sentence Encoder (USE) [59].
Here, the sentence embeddings are trained using either a
Transformer-based approach [60] or a deep averaging network
(DAN) [61]. In earth sciences, [62] showed the applicability
of multilingual sentence embeddings. The researchers used a
multilingual variant [63] of the universal sentence encoder [59]
to conduct a sentiment analysis on geo-referenced European
tweets related to the COVID-19 pandemic by fine-tuning the
pre-trained sentence embedding with tweets. The application
of multilingual sentence representations to classify sentiments
by using a simple feed-forward neural network enhanced
the results compared to a monolingual baseline. Of course,
embeddings adapted specifically to tweets also exist, e.g., [64],
[65].
In the past two years, BERT embeddings [66] and their
various offshoots, which take complex contexts into account,
have become very popular. BERT language models are based
on the Transformer architecture [60] and are trained by
using the so-called masked language task where a certain
percentage of words in a sequence is masked. During
training, BERT’s goal is to predict the masked words, which
increases context ”awareness” of the model. BERT has been
applied in studies focusing on urban areas such as sentiment
analysis on energy-related complaints on Twitter [67] and the
classification of flood-related tweets in Indonesia using the
multilingual version of BERT [68].
4) Neural Networks: Embeddings usually serve as the
input layer to a deep neural network, which can then be
trained to solve various tasks like classification of tweets.
Recurrent Neural Networks (RNNs) are commonly used for
sequential data like texts, but due to the short-context nature
of tweets, Convolutional Neural Networks (CNNs) are often
more successful here and easier to train, with the architecture
presented by Kim in [69] being used frequently. CNNs are
also suitable for processing text that is represented at the
character level [70]. Challenges like different languages or
misspellings can be approached when working at a character
level since no intuition of the network about words, seman-
tics, or syntax of individual languages is needed [70]. For
example, [71] shows the applicability of the character level
approach by generating an English tweet and achieving good
performance in various Twitter related tasks such as sentiment
categorization. As pointed out earlier, since this approach is
language independent, it could be an interesting technique
for text classification tasks including social media texts in
character based Asian languages such as Chinese or Japanese. It
should be pointed out that most of the tasks described in the
above sections about classical algorithms can now also be
solved with neural networks, e.g., sentiment analysis [62].
Further usages of neural network-based approaches are
presented in section VII.
C. Geolocations of Tweets
As discussed above, nearly all geo-related applications
of Twitter data require information about the geolocation
where each tweet was posted or refers to. Around 1% of all
tweets are already geo-tagged explicitly [72]: that is around
500M [73] tweets are published per-day, and 5M of them
are geo-tagged. Each geo-referenced social media post could
easily be resolved/decoded to a specific location on Earth.
By aligning the geo-referenced social media content with
an openly available Geographic Information System (GIS),
such as OSM, we have a valuable, easily accessible, and
cheap source of information. In this context, social media data
contributes to Volunteered Geographical Information (VGI),
and, consequently, empowers “citizens as sensors” [74], [75].
When working with geo-tagged social media data, we can
differentiate between two types of geo-locations. The first is
the geo-location of the person/app who posted or created the
content, for example, the GPS coordinates of the phone from
which a tweet is posted. The second is the geo-location that
is cited within the social media post, e.g., a Point Of Interest
(POI). While the second is increasingly supported by social
media channels, the first is less and less supported, ostensibly
due to privacy issues. Twitter stopped supporting the first type
of locations in mid-2019 [76], [77], but still supports the
second one via different mechanisms, like explicitly tagging a
tweet using one of the nearby “Twitter Places”, or implicitly
mentioning a POI within the content of the tweet.
Obtaining exact/named geo-locations from social media is a
challenging issue, especially the precise location of the person
when they create a post on social media. As mentioned, this
kind of location is unsupported in social media apps/sites
and users normally do not give social media apps the right
to access their location to protect their privacy. Moreover,
a tagged POI is potentially useless because of its coarse
granularity, as in most cases users tag a country or a city (see
Table II). In addition, the implicit mention of a POI in a social
media post poses its own challenges. In textual posts, we need
a mechanism to identify named entities and then resolve them
to a precise location. In visual posts (image or video), the
challenge of identifying the POI that appears in the scene is
even greater. Above all, the volume of available data, and the
fact that social media data is unstructured, heterogeneous, and
noisy, makes social media a challenging source of information
[75]. Based on our experience with geo-referenced Twitter
data, the main data challenges can be summarized as follows.
a) Precise Locations are Unsupported: In tweets, precise
locations refer to the geo-coordinates of the person/app who
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TABLE II
GRANULARITY AND SHARE OF TAGGED PLACES OF GEO-TAGGED TWEETS
COMING FROM NATIVE TWITTER APPS. NATIVE APPS ARE: “TWITTER FOR
IPHONE”, “TWITTER FOR IPAD”, “TWITTER FOR ANDROID”, AND “TWITTER
WEB CLIENT”.
April 2019
Country City Admin
3.9% 84.3% 10.1%
Neighborhood
0.1%
POI
1.6%
May 2019
3.8%
84.4%
10.1%
0.1%
1.6%
July 2019
3.8%
84.2%
10.6%
0.1%
1.3%
August 2019
3.5%
84.6%
10.4%
0.1%
1.4%
September 2019 3.3% 84.8% 10.4% 0.1% 1.4%
TABLE III
SHARE OF TWEETS COMING FROM NATIVE AND THIRD-PARTY APPS AMONG
ALL GEO-TAGGED TWEETS. NATIVE APPS ARE: “TWITTER FOR IPHONE”,
“TWITTER FOR IPAD”, “TWITTER FOR ANDROID”, AND “TWITTER WEB
CLIENT”.
April 2019
Native apps (%)
84.1%
Third-party apps (%)
15.9%
May 2019
84.7%
15.3%
July 2019
85.7%
14.3%
August 2019
86.2%
13.8%
September 2019 87.2% 12.8%
created the tweets, at the creation time. According to a Twitter
announcement,3 this type of location was well-supported be-
fore mid-2019, but has not been supported since then. This
seems to be a general tendency in the other social media
channels as well, to better protect the privacy of users. This
restriction already prevents many geo-information applications
from using social media data. For example, the building
function classification downstream task, which enables the
function of a building (e.g., commercial, residential, etc.) to
be determined based on the topics referenced near it, requires
the precise geo-location of each tweet. Consequently, the only
remaining type of supported geo-locations in Twitter data is
the geo-location of hot spots, such as POI, neighborhood,
city, country, etc., which are explicitly mentioned by users
within the tweets or implicitly assigned by the Twitter app. In
fact, even before mid-2019, the number of tweets that were
precisely geo-tagged and that came from the native Twitter
app accounted for only 8% to 25% of all geo-tagged tweets
[76].
b) Arbitrary Coordinates and Insufficient Metadata:
Each tweet consists of textual content and metadata, where
metadata is used to encode information about the tweet, such
as user ID, geo-coordinates, place type and name. The Twitter
website and Twitter native apps provide fairly rich metadata
for each tweet. However, a considerable share of all geo-
referenced tweets, around 15% (see Table III), come from third-
party apps like Instagram and Foursquare; for these tweets,
metadata is either missing or inaccurate. To sum up, although
there are many geo-referenced tweets, in many cases the exact
type of those coordinates cannot be detected; that is, we not
know if they refer to precise locations or to certain POIs.
3https://twitter.com/TwitterSupport/status/1142130343715078144?s=20
c) Granularity of Geo-locations: It is not only the avail-
ability of geo-referenced social media data that makes the
difference, but the granularity of their related coordinates.
For example, to classify buildings or to identify hot spots in
a city, we need geo-coordinates on a scale fine enough to
identify an individual building. After exploring Twitter data,
we found that most of the tweets are assigned city- or country-
level coordinates. It seems that if the user does not specify a
location, the platform fills the metadata field with the user’s
city- or country-level coordinates (see Table II). In addition,
a considerable number of tweets are coming with polygon
coordinates rather than a point coordinate, and the polygon is
sometimes too big to extract useful information.
Before proceeding with research that depends on such geo-
tagged data, we need to first consider two questions. First, are
the geo-locations that we need available? And if so, are they of
acceptable quality? Second, what is the available granularity
of geo-locations (e.g., building, neighborhood/polygon, city,
country)? Concerning the first question, it seems that social
media sources are increasingly providing POI locations instead
of the exact location of the person who creates the post. For the
second question, researchers need to focus on an acceptable
level of granularity, i.e., avoiding fine-granularity geo-tagged
location like buildings coordinates. Therefore, research design
needs to consider the different possibilities of increasing the
quality of the available data, looking for new sources of data,
or re-adapting the downstream applications to be compatible
with what the data offers.
As mentioned earlier, more than 99% of tweets are not geo-
tagged, which means that most tweets, in their original state, are
not usable for geo-applications. On the other hand, it means that
there is a great opportunity to increase the amount of available
geo-tagged data if these tweets [78] could be geo-tagged, as in
the example in Fig. 3. Fig. 4 shows that using a pre-trained
basic NER algorithm [79], we are able to identify
“Location” entities in 6% of all non geo-tagged tweets.
Furthermore, we were able to identify “Organization” and
“Person” entities in 13.5% and 13.3% of tweets, respectively,
where many of “Organization” and “Person” entities could be
geo-encoded to a certain location (see Fig. 3).
V. GEO-INFORMATION EXTRACTION FROM SOCIAL MEDIA
IMAGES
To gain spatial knowledge from social media images, they
must have a geo-tag that allows a precise localization of where
the image was taken. Most social media platforms enable their
users to upload images and set a location to let others know
where their images were taken. A set of such localized images
can provide deep insights into the surrounding area where
they were taken, e.g., activities, landmarks, and land cover.
Extracting knowledge from this vast variety of images requires
a structured approach: Fig. 5 sketches a general pipeline, from
data acquisition to generating machine learning-driven models,
for extracting geo-spatial knowledge.
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Fig. 3. NER for geo-coding of non geo-tagged tweets.
Fig. 4. The share of identified NER-tags when applied to the non geo-tagged
tweets that are collected between August 3 and 9, 2021.
A. Social Media Image Pre-processing
While most social media platforms let users tag their photos
with a location, external access to this information is limited.
Furthermore, the accuracy of these geo-tags must be treated
with care, since manual tagging is error-prone and GPS sensors
have limited accuracy when signals are distorted [80]. If an
image is taken with a smartphone camera it will automatically
be tagged based on the signal from a built-in GPS sensor.
Otherwise, geo-tags can be entered manually while creating
the social media post. In this case photographers tend to
tag batches of images and put their geo-reference to the
neighborhood where they were taken instead of a precise
location, which is presumably unknown.
Manual tagging also involves pictures that share no clear
relation to the location. Especially art-related images with geo-
reference do not show anything about the surrounding area
where they were taken, but draw the attention to a certain
motif. Due to their vast variety, finding a subset of images
that is helpful for a given task is crucial.
To cope with these intrinsic issues, feature extraction and
filtering are at the core of every methodology to extract geo-
information from social media images. An initial filtering step
can be based on visual content screening [81] or keywords
from the metadata of each image [82]. Moreover, pre-trained
models for image classification and object detection help in
understanding the image contents and eliminate the need to
train on data with uncontrollable quality. Models trained on the
ImageNet classification task have been successfully applied to
obtain general and abstract image features from hidden layers
of neural networks [83]. Object detection models provide an
intuitive insight into items present at certain locations, which
can be aggregated on a spatial level afterwards to classify
urban land use [84].
Putting the extracted information into the spatial domain
is mostly done by aggregating it into raster-based formats.
This eliminates smaller errors from GPS sensors and set the
information into a larger spatial context. On the other hand,
this approach might lead to vanishing information if signals
from different images are highly diverse.
Relating objects in images to spatial objects requires more
than a geo-tag. If a geo-tag and a compass direction are
present, a line-of-sight can be calculated and related to the
spatial objects that it intersects [85]. More sophisticated ap-
proaches can make use of the camera’s angle-of-view to define
the area depicted in an image. However, as the standard for
metadata does not take into account information about roll
and pitch, such algorithms have to assume that the camera’s
position was nearly orthogonal to the ground.
B. Methods
Geo-referenced social media images have been used for land
cover and land use classification on various spatial levels, from
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Get restricted access
Accept
uncontrollable data
quality
Manage images and
metadata at the same
time
Enable efficient
random data access
Analyze image
content to extract
information
Handle sp arse and
uneven spatial data
distribution
Estimate spatial
influence of features
Deal wit h unclear
target variables
Tackle class
imbalance
Fig. 5. Data processing chain for extracting geo-spatial knowledge from geo-tagged social media images.
the parcel down to the building instance level [83], [86][93].
Additionally, they can be used to automatically identify land-
marks and points-of-interest due to spatial clusters, resulting
from multiple users taking photos in one area with the same
motif [94]. Zhu et al. [93] use Flickr images to predict one
of 45 pre-defined land-use classes on a parcel level. They
train their model on global Google and Flickr weakly-labeled
images, testing with Flickr images in San Francisco. They
jointly fine-tune two CNN networks. The first network, called
the object stream, is a ResNet101 architecture pre-trained
on ImageNet, in which the last layer is replaced by a 45-
class classification head. The second network, called the scene
stream, is also a ResNet101 architecture pre-trained on the
Places365 dataset, in whcih the last layer is also replaced by a
45-class classification head. The final classification decision is
a late average fusion of the outputs of the two sub-networks.
Hoffmann et al. [83] address the same problem as Zhu et
al. [93] but at the more fine-grained level of the individual
building. Hoffmann et al. use a classification scheme with 5
classes, and train and test their model using Flickr images
in Los Angeles. They simply assign each geo-tagged Flickr
image to the closest OSM building in Los Angeles, assigning
the label of the building to the image. Then to classify an
image, they first transform the image to a vector of features
using VGG16, a state-of-the-art vision neural network pre-
trained on ImageNet. Next, they pass feature vectors to a 5-
classes logistic regression classifier. Finally, the building-level
predicted class is the majority vote of the predicted classes of
the images that are assigned to that building.
Social media images can provide critical hints during natural
disasters. There are two different datasets supporting this
application: one focusing on image retrieval, i.e., finding all
images related to a disaster in the stream of all incoming
data [95], and the other aiming at image classification so that
the correct situation, like flooding or fire, can be reported to
emergency teams [96]. In the case of flooding, studies have
shown that social media images can be used to estimate the
water level [97], [98]. For example, Chaudhary et al. use multi-
task learning on a dataset that is manually compiled from
Flickr, Google, and National Geographic. As accurate water
levels in meters are cumbersome to collect, their model learns
to jointly predict water levels as a regression tasks and relative
levels as a ranking task. The assessment of water levels is
based on five common objects in urban environments: persons,
cars, buses, bicycles, and buildings/houses, which allows the
ground truth to be estimated by relative comparison. However,
this method does not use any geotags for spatial interpretation.
Another application is environmental monitoring, either
by mapping animal and plant species [99] or by assessing
landscape preferences [100] and aesthetics [81], [101]. Urban
areas can especially benefit from this data by collecting citizen
sentiments and opinions from social media image content
and textual descriptions [102]. For example, in a study of
Copenhagen, Instagram images were collected by hashtag
search and manually assigned to six categories of urban nature
[103]. The researchers then performed a spatial analysis, where
images of certain categories appear, and identified hot spots,
where inhabitants of a city like to be, as well as what attracts
them.
Relating urban green space information with health data
does not show any connections [104], but social media images
do contain information about citizens’ habits. For example,
they are suited to analyze conditions like alcoholism on a
country level [105]. Beyond these insights, other studies have
investigated more latent socio-economic variables like GDP,
ethnicity, population density, and medical indicators [106].
Instead of trying to predict the exact geo-location of a given
image, which is a hard problem to solve, especially in sparsely
photographed places, Lee et al. [106] propose a CNN-based
approach to predict 15 socio-economic attributes, such as
GDP, infant mortality rate, and population density using Flickr
images. They create a labeled dataset of Flickr images, in
Operations
Acquisition
Evaluation
Machine Learning Driven
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which each image is weakly labeled using at least one of the
15 attributes. Then, they fine-tune one of the pre-trained state-
of-the-art ImageNet models by replacing the last layer with a
binary classification head for each attribute. Finally, they have
15 binary classifiers, one classifier per attribute. For example,
the GDP classifier predicts if the input image belongs/shows
a place with high/low GDP, and so on.
Furthermore, social media images have received attention
from the remote sensing community since these images pro-
vide a complementary view on the nadir perspective of satel-
lites. Studies have shown that remote sensing images can be
labeled using social media images [107], [108] or outcomes
of remote sensing models can be verified with social media
images [82]. The latter used a keyword search in Flickr to
obtain a set of images that was used to verify the labels of
GlobCover [109] in western California. Their verification is
based on the classification results of a VGG16 network fine-
tuned on a global set of Flickr images for the given task.
They reported an overall accuracy of 83.80% of GlobCover
from their approach, which is slightly higher than the human
verification results of 80.45%.
Choosing a social platform depends on the use case. Most
studies for disaster response rely on Twitter stream data, as this
is a time-critical application requiring fast information flows.
Flickr is best suited for analysis of spatial distribution, e.g.,
species monitoring or other tasks that are time invariant. While
Instagram and Facebook used to be potential data sources
before 2018, they are no longer applicable, as they closed
their APIs for content crawling.
Recent advances in aerial-to-ground mapping are potential
solutions to this issue [110], [111]. These methods aim at
learning a joint representation of aerial and ground views that
can be helpful for orientation and pose estimation without
having a LiDAR-derived point cloud. Beyond this fusion of
social media and remote sensing data, there are several other
aspects of aerial-to-ground mapping that are covered in the
next section.
C. Geolocations of Social Media Images
Ideally a given social-media image has accurate location and
time metadata. When this is not available, it may be possible
to estimate this information. One approach to estimating the
location of a social media image is cross-view localization.
In this setting, a ground-level image is localized by matching
image features to features extracted from an overhead imagery
source. Early work used traditional image features [112].
Subsequent work explored the use of features from deep
CNNs, either frozen, pretrained networks [113] or networks
optimized for the localization task [114], [115]. Since these
earlier works were published, much work has focused on
localization of panoramic images, leading to models that focus
on the richer geometric models [116], [117].
A key component of localization is the ability to extract
location-discriminative features from overhead imagery. An-
other line of work has focused on instead extracting semantic
features that are derived from social media imagery, and
other sources. These features may not be as discriminative
of location, but are potentially useful for other applications.
Some examples of mapping image features include mapping
scene categories [115], the distribution of objects [118], and
combinations of these features [119]. One potential application
of this work is in using social media as a source of supervision
for training overhead image understanding models, such as the
work by Greenwell et al. [120] that highlights the relationship
between object distributions and landuse categories. Beyond
images, similar approaches have been used to map features
extracted from geo-tagged audio [121].
Most work on metadata imputation/verification has focused
on the localization task. This is in part due to the difficulty of
obtaining sufficient training data. However, several works have
attempted to estimate timestamps. Salem et al. [122] proposed
a novel architecture for predicting object distributions and
scene categories based on the location, time, and satellite
image of the location. While their central focus was on
mapping tasks, they demonstrated the ability to estimate a
probability distribution over the capture time. More recent
work, by Padilha et al. [123] has significantly improved the
performance on both the problems of verifying whether a
purported timestamp is correct and estimating the distribution
over possibly valid timestamps.
VI. FUSION OF SOCIAL MEDIA AND REMOTE SENSING
DATA
Traditional remote sensing imagery is collected by satellite
and airborne platforms. This view provides a large potential for
applications, however, with limitations due to capturing only
physical conditions on the Earth’s surface or in the atmosphere,
infrequent collection, and delays in availability. In contrast, so-
cial media data is not as systematic and standardized as remote
sensing data, but it is collected frequently in many areas, is
often available within seconds of when it was collected, and
provides views not possible from overhead platforms, both
indoors and of exterior walls.
The key advantage of remote sensing imagery is that it
typically densely samples a given area so that the geographic
location imaged by each pixel is known, often with only a
few meters of uncertainty. The collection process for social
media is more variable and less reliable, since there is less
control over the collection process. There is also a bias
in the collected data, both due to the interests of persons
and their interpretation of what they are seeing. Given their
complementary nature, fusing the two data sources has the
potential to help solve problems that are not addressable using
remote sensing data alone.
A. Fusion of Remote Sensing and Social Media Imagery
When working with social media imagery and text, the
their location is less precise and the orientation is very
rarely known. Typically, the orientation is ignored and the
features are extracted from the ground-level imagery by either
averaging over wider areas [93] or using nearest-neighbor
approaches [124]. This limits the value of the ground-level
imagery to capturing coarse properties of an area.
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Early work on fusing ground-level and overhead views
focused on street-level panoramas, such as those in Google
Street View. Unlike social media, street-level panoramas are
collected using carefully engineered platforms to ensure con-
sistent imagery and metadata quality. While still present, the
location and orientation uncertainty is significantly lower than
it is for social media data. The key unknown is the scene
geometry, which means that directly relating ground-level
image features to overhead imagery is a non-trivial problem.
Study also shows that it is possible to use structure-from-
motion techniques to rapidly construct large-scale static 3D
models from social media imagery [125]. However, such mod-
els are typically restricted to areas with large numbers of social
media images. More recently, Li et al. [126] demonstrated the
ability to construct time-varying 3D models from social media
imagery. When geographically registered, such models, both
static and dynamic, have the potential to be integrated with
remote sensing imagery using similar models to those used
for street-level panoramas.
The known location and orientation has enabled approaches
that rely on the known geometric relationships between the
viewpoints, such as Workman et al. [127], which converts
individual panoramas into oriented perspective cutouts and
fuses them with satellite data using kernel-based feature fu-
sion. Wojna et al. [128] propose an approach that projects
features from the ground-level imagery into the overhead
perspective by using building outlines, ignoring cues from the
ground-level imagery, such as occlusions. More recent work
by Workman et al. [129] proposes a geospatial attention model
that enables flexible fusion of features from ground-level and
overhead imagery. They demonstrate significant improvements
over previous works on a variety of urban-area understanding
tasks. Future work on incorporating such imagery will likely
incorporate recent improvements in monocular depth estima-
tion [130] that will allow more precise localization of ground-
level image features in the overhead views.
More studies address the decision-level or feature-level
fusion, instead of the more challenging pixel-level fusion men-
tioned above. [131] compared three different fusion models
of aerial-view and street-view imagery for building function
classification. Rosser et al. [132] propose a Bayesian approach
combining information from Flickr imagery and LANDSAT-8
imagery to estimate flood inundation. Flood extents detected
in the Flickr imagery are converted into the overhead imagery
using viewshed analysis from a LiDAR-derived digital terrain
model. Their results show that using social-media imagery
alone leads to poor performance, but combining the two
works better than using only the satellite imagery. This study
illustrates how unknown viewing direction is one of the key
challenges in working with Flickr imagery. Only four of
205 images include metadata (i.e., the GPSImageDirection
EXIF tag). One possible solution would be to estimate the
viewing direction and other calibration parameters, but this
is a challenging problem, especially in regions with limited
reference imagery. Instead, they assumed that the camera had
a 360 field of view and that if flooding is present in the image,
it is uniformly distributed in the viewshed. These simplifying
assumptions assume that every Flickr image is a full panorama
and that every imaged pixel is covered with water. While these
are clearly invalid simplifying assumptions, it is impressive
that even with these in place the social media imagery is
able to improve performance over the use of satellite imagery
alone. Assumpc¸a˜o et al. [133] provide a broad overview of
the integration of social media, including imagery, video, and
text, with hydrological models and remote sensing data for the
general problem of flood modeling.
B. Fusion of Remote Sensing Data and Social Media Text
Social media text messages seldom contains precise location
or orientation information like those in the metadata of some
social media images. Therefore, the fusion of text messages
and remote sensing data can only be done in a feature- or
decision-level.
Hultquist et al. [134] present a case study on estimating
the extent of a power outage following a major hurricane.
They combine night light remote sensing imagery with the
distribution of tweets that were tagged as “power related”
by a machine-learning model to estimate areas where the
power is out, providing street-level resolution. They find that
the key benefit of the social media text is in increasing the
spatial resolution of the model. Leichter et al. [135] use
gradient boosting to combine features derived from remote
sensing imagery and tweets to estimate local climate zones.
In this approach, no advanced text understanding is performed.
Instead, tweet features are simple values, such as total number
of tweets in an area or the average tweet length. Including
the tweet features improved the average F1 score from 49.9
to 53.1. In [136], the combination of tweets and remote
sensing images improves the overall prediction results of both
individual modalities [136] in building function classification,
demonstrating the complementarity of the two data types.
Additionally, the authors show with a spatial cross-validation
that the models can generalize beyond a certain region. TEST
Figure 6 depicts examples of classification results after the
fusion process documented in Figure 7.
VII. GEOGRAPHIC APPLICATIONS
The methodologies introduced in the previous sections
have enabled various applications in geo-information retrieval.
Where many people live, there is much communication. The
manifold land uses, the contextual embedding of space into
meanings and perceptions, and dynamic changes in urban
space due to daily life routines can only be indirectly assessed
by social media data. Studies show that social media data such
as from Twitter, Flickr, or other geolocated posts has the po-
tential to add the non-physical in objective or subjective form,
e.g., [137], [138], biased by those who use these platforms
[139], [140]. The most commonly addressed application is
land use land cover classification, as images and texts can
give direct information of the building appearance or the
functionality. Urban geography and social science are also
widely studied fields. The applications are diverse, including
public health, predicting socio-economic variables, mobility,
POI, and sentiment analysis, to name a few.
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Fig. 6. Very high resolution aerial imagery with OpenStreetMap building footprints and corresponding Twitter data. The abbreviations in the brackets denote
the wrongly estimated class (com = commercial, res = residential, oth = other). Real names were masked with xxx or yyy to preserve the privacy of the Tw itter
users. Image taken from [136]. Background images ©TerraMetrics 2021, Google.
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https://mocobeta.github.io/janome/en/ [31], https://spacy.io/
http://jaist.dl.sourceforge.net/project/mecab/mecab-ipadic/
TABLE IV
SUMMARY OF GEO-INFORMATION R ETRIEVAL F ROM SOCIA L MEDIA DA TA.
In order to give a concise overview, we sort these applica-
tions into the following four topics: land use and land cover
classification, urban geography and social science, environ-
ment perception and sentiment analysis, and crisis response.
Although land use land cover classification may overlap with
urban geography, we isolate it as a single topic, as it is the
most commonly addressed problem. Table IV summarizes the
applications, the associated literature, and resources. In the
following, we provide an overview of the development in each
application field. In each field, one detailed study will be given
as an example.
A. Land Use and Land Cover
Land use and land cover classification are related, but cover
different aspects. While land cover is measurable, land use
requires the interpretation of data [169]. However, if social
media data is used to predict this geo-information, both cases
require the interpretation of patterns using machine learning
algorithms. There are different aspects of studies in this area,
including scale, data source, spatial granularity, classification
granularity, and method. Concerning the scale, most studies
are performed at the city level, e.g., [83], [88][90], [92],
[93], with [85], [91], [136], [145] as notable examples of
multicultural scales. Flickr is the most frequently used data
source for images [83], [85][90], [93], whereas Twitter is
mainly utilized for text applications [136], [141][145]. The
spatial scale has a high variety: from grids of various sizes as
in [89], [144], [145], to parcels [86], [87], [93], buildings [58],
[85], [131], [136], and images [91]. Since there is no standard
for land use classification schemes, a broad range of them
has been proposed, from generic ones with three classes [85],
[136] to very fine-grained schemes of 45 classes [93]. Early
approaches used handcrafted features [86], [89], [92], while
more recent methods have increasingly applied deep learning
to solve the task [58], [83], [85], [87], [90], [91], [93], [136],
[143]. Among them, some works address the fusion of ground-
level and aerial images for land use land cover classification,
for example [131]. Aerial image scene classification is a highly
related but standalone topic. Readers could refer to articles in
remote sensing [170][172] for more insights.
Social media images can latently encode information about
both land use and land cover. A social media image may,
for example, show a building with parts of the surrounding
garden, thus containing land cover information like grass, and
hints about land use in the building fac¸ ade. The similarity of
both classification tasks enables joint approaches with deep
learning architectures [83], [90]. This co-occurrence of land
cover and land use is found primarily in images, while social
media text predominantly contains information about land use
only because people use Twitter, for example, to tweet about
their activities. Embedding models like fastText (see Section IV-
B) can help detect hidden information by extracting abstract
feature vectors. Used as a feature encoder to a bi-LSTM,
fastText allows the accurate predictions of building func-
tions [58]. Moreover, the combination of tweets and remote
sensing images improves the overall prediction results of both
individual modalities [136]. Figure 7 illustrates the pipline.
To implement this, predictions are generated for individual
tweets and then aggregated on a building level. Afterwards,
this aggregated prediction is fused with predictions obtained
from very high resolution images via CNN-based models.
With the evolution of multimodal networks that combine
image and text seamlessly, we expect to see further approaches
towards this direction in the future. However, since no bench-
mark datasets are available for this task, a comparison of
different methods is only possible at a qualitative level.
B. Urban Geography
In urban geography, thematic applications from the fusion of
remote sensing and social media data are manifold: examples
include the mapping of population [173], the analysis of urban
green space with public health [104], [105], the prediction of
socio-economic variables such as GDP, ethnicity, population
density, and medical indicators [106], [173], the assessment of
cultural ecosystem services [153] or sustainable development
[147], and the determination of house prices [174], among
many others. The most obvious and widespread application,
ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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(
+
2
)
Fig. 7. Fusion framework from [136]. 1) Mapping the text to machine-readable representation. 2) Text classification with stacked bi-directional LSTMs. 3)
Remote sensing image classification with a CNN 4) Decision-level fusion by weight-averaging the prediction probabilities of text and image classification.
Aerial images ©TerraMetrics 2021, Google.
however, is the classification of building usages. A building’s
physical shell is not always clearly described in remote sensing
data, but place, time, frequency, and content statements in
social media data make it possible to assign uses such as office,
residential, industrial or similar with high accuracy [141],
[143], [175]. These variable uses also define the high spatial
dynamics of people’s daily routines. Social media data allows
researchers to analyze activity/mobility patterns [142], [148].
People using smartphones or interacting with social networks
are becoming “citizen sensors” [137], making it possible to
relate static urban space to movement [176].
One successful application shown in [145] is the correlation
between different population groups and their participation in
social media communication in urban contexts. The frequency
and times of tweets in certain urban areas are compared with
those in other areas in order to determine the participation
of these groups in modern communication. Fig. 8 shows an
example in this respect: in [145], based on remote sensing
data, the built-up urban landscape is divided into formal set-
tlement structures and informal settlement, commonly known
as morphological slum areas. The analysis of geolocalized
Twitter frequencies over time then allows us to derive spatial
deviations from the median of the respective city, into so-
called digital centers (hot spots) or digital deserts (cold spots).
By superimposing these datasets it is possible to determine
that in morphological slums the participation in social network
communication is actually lower. These approaches are com-
paratively objective, despite the bias of the basic population
mentioned above. This study also revealed that among Twitter
users, both sides of the economic divide exhibit very similar
temporal behavior patterns.
In general, however, it must be stated that methods and
applications in areas of tension between urban geography,
remote sensing, linguistics, and other interdisciplinary fields
are still in their infancy.
C. Human Perception and Sentiment
The two previous subsections primarily discussed land
characteristics observed through categories meant to be “ob-
jective.” The debate of the universality of these categories
notwithstanding, their study can be pursued using the ever
growing amount of labeled data provided by authoritative
sources, such as mapping agencies (e.g., Swisstopo, Corine
Land Cover) and scientific datasets (e.g., the IEEE IADF data
fusion contest dataset [177] or the So2Sat LCZ42 dataset
[178]).
However, the thematic content of social media posts, i.e., in-
formation beyond location and time, contains many subjective
estimations, observations, and perceptions. This subjectively
colored information on areas or cities offers an approach
urban atmospheres, perceptions, or emotions [179]. These
applications can be summarized into two categories: human
perception of environment, and sentiment analysis. The per-
ception of the environment is more related to vision tasks using
social media images, for example mapping animal and plant
species [99], and assessing landscape preferences [100] and
aesthetics [81], [101]. Urban areas in particular can benefit
from this data by collecting citizen sentiments and opinions
4
f =argmax λ
1
|T
|
t +(1λ )i
b t Tb
Fusion
1
I am at a friend‘s house right now
Embedding Layer E
35
42
75
(...)
token
at
am
word vector
n Text Sequences for LSTM input
2
Text Classification
Image Cla ssification
Aerial Image Classification Model
3
fully connected
(softmax )
prediction
probabilities Tb
(1:n relationship)
Bi-directional LSTM
Bi-directional LSTM
word_vector
75, word_vector42, word_vector35, word_vectori, (...)
n t
weets
ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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Fig. 8. a) Remotely sensed classification of the urbanized area into formal settlements and non-formal morphologic slums; b) Localization of digital hot spots
and deserts based on tweet quantities; c) Proof that digital deserts are more common in morphological slums.
from social media image content and textual descriptions
[102], [138].
Sentiment analysis is more related to the processing of text
messages. Corpus-linguistic and discourse-analytical methods
aim to analyze language in terms of keywords, choice of
words, designations of self or external references, discourse
topics and subjective evaluations of objects, items, and persons
[180]. Investigating people’s moods could reveal insights into
the problems or challenges of an urban area, potentially impor-
tant information for city planners or municipal governments.
For example, [149] studied the sentiment of Twitter users
of urban green spaces in New York City through human
annotated tweets. The study revealed that tweets in Manhattan
parks exhibit lower sentiment than tweets from the streets.
However, in other city districts, the opposite was the case:
tweets in parks exhibited more positive sentiments. In addition
to that, [150] found that tweets sent from Birmingham, UK,
parks mostly expressed happiness and appreciation of nature’s
beauty. In a recent study, [151] explored the urban Twitter
sentiment within the context of the Women’s March of 2017
in the US. To examine sentiment, they used the lexicon-
based approach VADER, described in subsection IV-B, which
specializes in Twitter text.
We demonstrate an example using the term landscape
beauty in the following paragraphs. When dealing with more
“subjective” topics, like the perceived aesthetics of the land, a
curated dataset can hardly exist, as beauty is in the eye of the
beholder and every person has a different perception. Using
crowdsourcing is an efficient way to gather data and has been
effectively used to gather label points for both objective tasks
and subjective opinion tasks (e.g., city perception [152] or, as
in our case, landscape beauty [154]). In the case of aesthetics,
the advantage of proceeding by crowdsourcing is to develop
the ability of capturing subjectivity and learn something about
how people feel about nature from the responses.
In the application presented here, which the reader can
find in extended form in [155], the ScenicOrNot dataset
(SoN, http://scenicornot.datasciencelab.co.uk) is used to
make the connection between land use (observed through
a series of remote sensing images from the Sentinel-2
satellite) and perceptions of beauty. SoN is a collection of
crowdsourced opinions of a series of more than 200,000
images over Great Britain, organized over a 1 km grid.
The images are from the Geograph project presented above
(https://m.geograph.org.uk); SoN adds perceptive information
to these images, by letting volunteers rate each picture with
a score between 1 (unsightly) and 10 (beautiful). The ability
to predict the scenic quality of an image by using deep
learning algorithms was studied recently [124], [181], which
then enabled connections to be made between color spaces
[154] or objects categories visible in the images [182] and
the perception of beauty.
In the application presented here, we aim to study whether
land use an “objective” characteristic visible on a satellite
image and for which vast quantities of labeled data exist
influences the perception of beauty. To do so, we re-purpose
the model of [182] to predict the scenicness of the landscapes
imaged in a Sentinel-2 patch (see Fig. 9). As in [182], the
model uses two predictive heads: the first predicting land use
as a multi-label problem (i.e., several land use types can be
present in the Sentinel-2 patch, but we are not interested in
their exact location) and using Corine Land Cover as the
ground reference. The outcome of this first predictive head
is used by the second prediction task, which estimates the
average scenicness for the entire patch. Since the footprint
ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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Fig. 9. Intuition behind the interpretable scenicness prediction model in [155]. Photos are obtained from https://www.geograph.org.uk/
of the Sentinel-2 patch is wider than the precise location
of an image, we predict the average scenicness of all the
SoN images located within the patch footprint. By using land
use as semantic interpretation guidance, we are then able to
(1) tell whether certain types of land use lead to prettier
landscapes, and (2) validate the geographical consistency of
the scenicness prediction model. The latter is very important
since it allows us to question the validity of the interpretable
scenicness predictor, for example in cases where the model
predicts mountainous land where there are no mountains.
The scores provided by volunteers on social media allow us
to draw this interpretation map between perceived beauty and
land use: in Fig. 10 we display the maximum possible contri-
bution to the final scenicness score. These weights allow the
model to find land use-related subtle variations of scenicness
around the mean score of the dataset. From the weights one
can see that, on average, urban fabrics contribute negatively to
landscape aesthetics, while open spaces, non agricultural veg-
etated areas, and inland wetlands tend to contribute positively.
1.1 : Urban fabric
1.2 : Industrial, commercial, and transport units
1.3: Mine, dump, and construction sites
1.4: Artificial, non-agricultural vegetated areas
2.1: Arable land
2.2 : Permanent crops
2.3 : Pastures
2.4 : Heterogeneous agricultural areas
3.1: Forests
3.2: Scrub and/or herbaceous vegetation
3.3: Open spaces with little or no vegetation
4.1: Inland wetlands
4.2: Maritime wetlands
5.1 : Inland waters
5.2 : Marine waters
0.25 0.25
Fig. 10. Weights for each class in the final scenicness prediction layer, as in
[155].
Summarizing this application, social media and crowdsourc-
ing tell us something that is generally not available in tradi-
tional land prediction tasks: information about the subjective
reaction of the users of space. This information is contained in
social media data and can be used to learn about perceptions
about space and spatial preferences, and potentially can lead
to better location- (and customer-) based analytics.
D. Crisis Response
During a crisis situation, quickly gaining as much informa-
tion as possible about the progression of events is of crucial
importance. Gathering information is necessary for developing
situational awareness, and can mean the difference between
life and death. Social media is one source of such information
that has started garnering interest in recent years. Twitter users
write about disaster preparations, developments, recovery, and
many other topics [158]. Retrieving this information can lead
to improvements in disaster management strategies. In contrast
to most other information sources, social media posts appear
nearly immediately whenever there is a new occurrence (as
long as telecommunication infrastructure is still intact), and
can therefore deliver information very quickly. Such messages
can also provide new perspectives that would not be available
any other way at this speed, e.g., ground photos. In addition to
factual information, social media can offer personal insights
into the occurrences, as well as a back-channel to users
for relief providers, government agencies, and other official
institutions as well as the media. In a 2010 Red Cross study,
69% of Americans said that emergency response agencies
should respond to calls for help sent through social media
channels [183]. A highly comprehensive overview of social
media usage in crisis situations is given in [184].
The difficulty lies in the retrieval and classification of such
messages. As described in Section III, the incoming volume
of Twitter messages in the live stream is huge. In any given
event, the majority of these posts will not be relevant to the
event, or useful to service providers. The task of finding social
media posts in a crisis may appear clearly defined at first, but
quickly becomes more convoluted when attempting an exact
definition. Existing publications have defined this problem
statement in a variety of ways. As described in [185], research
generally focuses on three qualities of messages: whether they
relate to the crisis event at all, whether they are relevant, or
whether they are informative. All of these questions, but the
last two in particular, depend highly on the users of the system,
be they affected citizens, family members, the government,
news media, or others. In many cases, users are assumed
to be relief organizations. In addition, each of these users
may be interested in a different use case of the system. For
instance, humanitarian and governmental emergency manage-
ment organizations are interested in understanding ”the big
picture,” whereas local police forces and firefighters desire
to find “implicit and explicit requests related to emergency
needs that should be fulfilled or serviced as soon as possible”
[186]. Moreover, some of these use cases may require a high
precision of the detected tweets while possibly missing some
important information; others may be more accepting of false
Sentinel-2 patch
CNN LC
classifier
Class-specific X
confidence
LC-specifi c = Scenicness score
weight
Pasture = 0.8
10 (beautiful)
City = 0.6
Forest = 0.5
....
1 (unsightly)
ZHU ET AL., GEO-INFORMATION HARVEST ING FROM SOCIAL ME DIA DATA , IEEE GRSM, A CCEPTED
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alarms while focusing on a high recall.
Several detection approaches have been presented in the
literature so far, falling into three categories: filtering by
characteristics, crowdsourcing, and machine learning-based.
The most obvious strategy is the filtering of tweets by var-
ious surface characteristics as shown, for example, in [187].
Keywords and hashtags are used most frequently for this task
and often serve as a useful pre-filter. Olteanu et al. developed
a lexicon called CrisisLex for this purpose [159]. However,
this approach easily misses tweets that do not mention the
keywords specified in advance, particularly when changes
occur or the attention focus shifts during the event, or it may
retrieve unrelated data that contains the same keywords [188].
Geo-location is another frequently employed feature that can
be useful for retrieving tweets from an area affected by a
disaster. However, this approach misses important information
that could be coming from a source outside the area, such as
help providers or news sources. Additionally, as described in
Section II, only a small fraction of tweets is geo-tagged at
all, leading to a large quantity of missed tweets from the area
[72].
To resolve these problems, crowdsourcing strategies were
developed, i.e., asking human volunteers to manually label the
data. Established communities of such volunteers can be acti-
vated quickly in a disaster event, e.g., the Standby Task Force
(https://www.standbytaskforce.org/). To facilitate their work,
platforms have been developed over the years. One of the most
well-known systems is Ushahidi (https://www.ushahidi.com).
This platform allows people to share situational information
in various media, e.g., by text message, by e-mail, and of
course by Twitter. Messages can then be tagged with categories
relevant to the event. Other examples include AIDR [188],
which contains automatic analysis tools, or CrisisTracker
[168]. In CrisisTracker, tweets are also collected in real time
and clustered by topics so that volunteers can analyze them
jointly.
In recent years, approaches based on deep learning tech-
niques have come to the forefront of research. Caragea et al.
first employed Convolutional Neural Networks (CNN) for the
classification of tweets into those related to flood events and
those unrelated [160]. In many of the following approaches, a
type of CNN developed by Kim for text classification is used
[69], such as in [189]. This method achieves an accuracy of
80% for the classification into related and unrelated tweets.
The authors of [161] demonstrate how to implement a system
that is able to determine whether a tweet belongs to a class
(i.e. crisis event) implicitly defined by a small selection of
example tweets by employing few-shot models. The approach
is expanded upon in [162].
Once tweets related to a disaster event have been discovered,
further analysis steps are possible. A popular next step is the
classification into semantic or information type classes, e.g.,
affected people seeking various types of assistance, media
reports, warnings and advice etc. (e.g., [190], [191]). In [163],
tweets are clustered by topic at time of publication; these top-
ics develop in real time and can shift. Another way of further
discerning between tweets is to distinguish between levels of
informativeness or priority (e.g., [167]). It is also possible to
perform a more finegrained analysis of particularly interesting
properties, such as the sentiment analysis performed on contin-
uous value and time scales during the COVID-19 pandemic in
[62] (see also Fig. 11). Other research focuses on the detection
of specific events, or types of events (e.g., floods, wildfires,
or man-made disasters, e.g., [192]). This can often be helpful
when social media is used as an alert system. Apart from these
text-based tasks, image analysis can also be a helpful source
of information, e.g., for determining the degree of destruction
in the aftermath of a disaster [193]. Several datasets have been
published for disaster applications to gain insights about the
content of social media and to develop detection methods.
These include, for example, CrisisLexT26 [159], [164], Cri-
sisNLP [165], CrisisMMD [166] (also including images), and
TREC-IS 2019A [167]. The annual TREC Incident Streams
challenge invites researchers to submit novel methods for
classifying disaster-related tweets, and continuously provides
new labeled data (http://dcs.gla.ac.uk/richardm/TREC IS/). A
more detailed overview on the detection of Twitter messages
in crisis events is given in [194].
VIII. ETHICS OF RESEARCH WITH SOCIAL MEDIA DATA
In the world of (business) ethics, the observer effect may
translate to what is known as ethical relativism. Under the
doctrine of relativism (and also in practical reality), ethical
issues questions of right or wrong, good or bad vary
based on who is observing and the context within which the
observation is being made [195]. Ethical issues in the context
of social media data mining have already been flagged in
diverse contexts, notably in the context of medical data. The
primary aim of this segment is to broadly identify which of the
ethical concerns that have been raised in the general context
of social media data mining are likely to apply, in the present
or in the future, to geo-information harvesting from social
media.4
A. Ethical Issues Commonly Flagged in Social Media Data
Harvesting
Significant research has been undertaken to identify ethical
issues that (may) arise from data mining per se, as well as
from the labeling, analysis, and use of the data (e.g., in AI
or machine learning models). The most commonly identified
issues include:
a) Privacy
b) Stigmatization
c) Data veracity
d) Bias (including bias in training data)
e) Transparency and explainability
4The Ethics Guidelines for Trustworthy AI, which provides a “concrete and
non-exhaustive Trustworthy A I assessment list”, also recognizes that the list
will “need to be tailored to the specific use case of the AI system.” The
Guidelines also suggest that in addition to this horizontal framework, a
sectoral approach is needed, given the context-specificity of AI systems,”
and this needs exploration. This article does not address ethical issues in
AI. Nevertheless, as the geo-information data mined from social media
platforms is largely used to train AI/ML models, this observation made by the
Ethics Guidelines for Trustworthy AI is relevant to the overview given here.
References from these guidelines (referred to hereinafter as “Trustworthy AI
Guidelines”) have been made, if relevant, in footnotes.
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Fig. 11. Sentiment development of tweets in various European countries during the first months of the COVID-19 pandemic [62].
f) Security (including in the context of data storage)
g) Accountability and democratic creation of standards
In addition to the above issues, in the context of geo-
information data harvesting for use in EO/RS applications,
we may also need to consider the issues of national security
and national sovereignty. These issues, however, may or may
not arise depending on the use case (end result sought) and
on how other issues such as security and data dissemination
are handled.
The seven issues listed above have been raised in very
diverse contexts (e.g., medical data mining from social media)
in existing literature. In the following sub-section, we look at
each of these briefly in the context of geo-information data
harvested from social media, especially where such data is
intended for use in EO or RS applications.
B. Ethical Issues in Geo-information Data Harvesting
The most important ethical issues that may arise in the
context of geo-information data mining for use in EO or RS
applications are discussed below. However, researchers must
be mindful that this is not a comprehensive list and new issues
are likely to arise as technology evolves and new use cases
emerge.
a) Privacy: Including and beyond the scope of the EU
General Data Protection Regulation (GDPR), privacy of per-
sons whose data is mined from various social media platforms
needs to be protected at all costs and its misuse prevented.
The argument that the data is already public (e.g., as it was
posted on Twitter) may not be adequate to avoid or overcome
ethical concerns of privacy [196], [197]. Yet, the nature of geo-
information data mining for use in EO/RS applications is often
very different from data mining for other purposes: for example,
geo-information data mined with the aim of labeling buildings
or identifying areas where rescue efforts (see Section
VII) need to be expedited. Indeed, in emergency situations,
tweeting individuals may actively want their identity and loca-
tion to be accurately discoverable so that help can reach them
or their community. Yet, to the extent that comments associ-
ated with texts and images (even if these relate to buildings or
geographic regions) can facilitate social, economic, political,
or racial profiling of individuals, precautionary measures such
as anonymization and non-reproduction of verbatim texts are
essential. This is especially true of any applications (such as
building labeling) that do not require the identity e.g. of the
individual tweeter to be known. Further, cultural sensitivities
need to be taken into account while collecting data some
cultures may be less tolerant than others to the very act
of collecting personal (geographic location) information of
individual social media users. These cultural sensitivities might
exist beyond the scope of local/national laws.
b) Stigmatization: Closely linked to the issue of privacy
is the issue of stigmatization [198]. Although most often raised
in the context of medical data mining, in the context of geo-
information data harvesting, ethical concerns may arise at the
more advanced stage, i.e., when the resulting data is used to
create population density maps or for labeling areas that are
currently not labeled in maps, such as slum areas. Here,
labeling (or mislabeling) of social media data such that specific
areas are labeled as slums can lead to stigmatization, not (only)
at individual, but at community or national levels. The issues of
stigmatization also links up to issues of bias and
transparency, as well as data veracity, as discussed in the
following points. From the perspective of ethical opportunities,
on the other hand, responsible and constructive labelling
can support development of economically weaker (rural and
urban) regions, or expansion of urban green areas by directing
government funding to those regions.
c) Data Veracity: Although data volume, variety and
velocity are relevant for data management (compare Section
III), data veracity [199], [200] is equally relevant for accurate
and reliable end results. Undoubtedly, the nature of big data,
and appropriate programming of AI/ML mod