Benjamin HerfortUniversität Heidelberg · Institute of Geography
Benjamin Herfort
Master of Science
About
41
Publications
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Citations since 2017
Introduction
Publications
Publications (41)
Natural hazards threaten millions of people all over the world. To address the risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed build...
OpenStreetMap (OSM) has evolved as a popular geospatial dataset for global studies, such as monitoring progress towards the Sustainable Development Goals (SDGs). However, many global applications turn a blind eye on its uneven spatial coverage. We utilized a regression model to infer OSM building completeness within 13,189 urban agglomerations home...
OpenStreetMap (OSM) has been intensively used to support humanitarian aid activities , especially in the Global South. Its data availability in the Global South has been greatly improved via recent humanitarian mapping campaigns. However, large rural areas are still incompletely mapped. The timely provision of map data is often essential for the wo...
Accurate and complete geographic data of human settlements is crucial for effective emergency response, humanitarian aid and sustainable development. Open-StreetMap (OSM) can serve as a valuable source of this data. As there are still many areas missing in OSM, deep neural networks have been trained to detect such areas from satellite imagery. Howe...
Accurate and complete geographic data of human settlements is crucial for effective emergency response, humanitarian aid and sustainable development. Open- StreetMap (OSM) can serve as a valuable source of this data. As there are still many areas missing in OSM, deep neural networks have been trained to detect such areas from satellite imagery. How...
In the past 10 years, the collaborative maps of OpenStreetMap (OSM) have been used to support humanitarian efforts around the world as well as to fill important data gaps for implementing major development frameworks such as the Sustainable Development Goals. This paper provides a comprehensive assessment of the evolution of humanitarian mapping wi...
Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of...
Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of...
This map of COVIDー19 research shows clinical trials related to coronavirus based on data from the WHO International Clinical Trials Registry Platform
This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (mm‐cm per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stabl...
This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the ground surface in 3D point clouds. The web-based tool was used to quan...
Reliable techniques to generate accurate data sets of human built-up areas at national, regional, and global scales are a key factor to monitor the implementation progress of the Sustainable Development Goals as defined by the United Nations. However, the scarce availability of accurate and up-to-date human settlement data remains a major challenge...
Although built-up areas cover only a small proportion of the earth's surface, these areas are closely tied to most of the world's population and the economic output, which makes the mapping of built-up areas a vital challenge. Thanks to the generous contribution of volunteers, OpenStreetMap shows great capability in addressing this challenge, while...
In this paper, we propose a method to crowdsource the task of complex three-dimensional information extraction from 3D point clouds. We design web-based 3D micro tasks tailored to assess segmented LiDAR point clouds of urban trees and investigate the quality of the approach in an empirical user study. Our results for three different experiments wit...
Geodata is missing to populate maps for usage of local communities. Efforts for filling gaps (automatically) by deriving data on human settlements using aerial or satellite imagery is of current concern (Esch et al., 2013; Pesaresi et al., 2013; Voigt et al., 2007). Among semi-automated methods and pre-processed data products, crowdsourcing is anot...
Zusammenfassung
Mit den steigenden Online-Partizipationsmöglichkeiten, die sich im Zuge des Web 2.0 seit geraumer Zeit ergeben, werden immer mehr Daten im Allgemeinen und Geodaten im Speziellen produziert. Durch Entwicklungen in GPS- und Satellitenbildtechnologie, können auch Laien ihre Umgebung und ferne Orte auf einfache Weise digital erfassen. D...
![Figure 2. Space-time bin for data aggregation (adapted from [33]).](profile/Hongchao-Fan/publication/316684737/figure/fig8/AS:670516968951812@1536875111221/Space-time-bin-for-data-aggregation-adapted-from-33_Q320.jpg)
Tourism is one of the most economically important industries. It is, however, vulnerable to disaster events. Geotagged social media data, as one of the forms of volunteered geographic information (VGI), has been widely explored to support the prevention, preparation, and response phases of disaster management, while little effort has been put on th...
Geographic information crowdsourcing is an increasingly popular approach to derive geographic data about human settlements
from remotely sensed imagery. However, crowdsourcing approaches are frequently associated with uncertainty about the quality of the information produced. Although previous studies have found acceptable quality of crowdsourced i...
In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, a...
In the past few years, crowdsourced geographic information (also called volunteered geographic information) has emerged as a promising information source for improving urban resilience by managing risks and coping with the consequences of disasters triggered by natural hazards. This chapter presents a typology of sources and usages of crowdsourced...
Die OpenFloodRiskMap (OFRM) ist ein Entscheidungsunterstützungssystem, welches Entscheidungsträger in der Alarm- und Einsatzplanung und im Hochwasserfall in der Identifi- zierung Kritischer Infrastrukturen (KI) und Navigation zu KI unterstützt. Durch die Zusammenarbeit mit verschiedenen Kommunen wurde die OFRM an deren Bedürfnisse im Hochwassermana...
Social networks have been used to overcome the problem of incomplete official data, and to provide a more detailed description of a disaster. However, the filtering of relevant messages on-the-fly remains challenging due to the large amount of misleading, outdated or inaccurate information. This paper presents an approach for the automated geograph...
Floods are considered the most common and devastating type of disasters world-wide. Therefore, flood
management is a crucial task for municipalities- a task that requires dependable information to evaluate risks
and to react accordingly in a disaster scenario. Acquiring and maintaining this information using official data
however is not always feas...
The paper presents results from a validation process of OpenStreetMap (OSM) rapid mapping activities using crowdsourcing technology in the aftermath of the Gorkha earthquake 2015 in Nepal. We present a framework and tool to iteratively validate and update OSM objects. Two main objectives are addressed: first, analyzing the accuracy of the volunteer...
Recent development in disaster management and humanitarian aid is shaped by the rise of new information sources such as social media or volunteered geographic information. As these show great potential, making sense out of the new geographical datasets is a field of important scientific research. Therefore, this paper attempts to develop a typology...
Social networks have been used to overcome the problem of incomplete official data, and provide a more detailed description of a disaster. However, the filtering of relevant messages on-the-fly remains challenging due to the large amount of misleading, outdated or inaccurate information. This paper presents an approach for the automated geographic...
Flood risk management requires updated and accurate information about the overall situation in vulnerable areas. Social media messages are considered to be as a valuable additional source of information to complement authoritative data (e.g. in situ sensor data). In some cases, these messages might also help to complement unsuitable or incomplete s...
Identifying the assets of a community that are part of its Critical Infrastructure (CI) is a crucial task in emergency planning. However, this task can prove very challenging due to the costs involved in defining the methodology and gathering the necessary data. Volunteered Geographic Information from collaborative maps such as OpenStreetMap (OSM)...
In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a standalone in...
Flood risk management requires updated and accurate information about the overall situation in vulnerable areas. Social media messages are considered to be as a valuable additional source of information to complement authoritative data (e.g. in situ sensor data). In some cases, these messages might also help to complement unsuitable or incomplete s...
Recent research has shown that social media platforms like twitter can provide relevant information to improve situation awareness during emergencies. Previous work is mostly concentrated on the classification and analysis of tweets utilizing crowdsourcing or machine learning techniques. However, managing the high volume and velocity of social medi...
The identification of elements at risk is an essential part in hazard risk assessment. Especially for recurring natural hazards like floods, an updated database with information about elements exposed to such hazards is fundamental to support crisis preparedness and response activities. However, acquiring and maintaining an up-to-date database with...
In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This...
Recent research has shown that social media platforms like Twitter can provide relevant information to improve situation awareness during emergencies. Previous work is mostly concentrated on the classification and analysis of tweets utilizing crowdsourcing or machine learning techniques. However, managing the high volume and velocity of social medi...
Projects
Projects (4)
Project website: www.uni-heidelberg.de/loki
LOKI Wiki and Stories: https://git.gfz-potsdam.de/loki/loki_wiki/-/wikis/home
LOKI GitLab Repository: https://git.gfz-potsdam.de/loki
Twitter: https://twitter.com/search?q=LOKI%2C%20BMBF&src=typd&lang=de
The aim of the LOKI project is to develop an interdisciplinary system that enables fast and reliable airborne situation assessments following an earthquake. A central focus is the timely overview and detailed recording of the damage to critical infrastructures. The objectives will be met by combining existing expertise in earthquake research with a variety of new technologies and concepts, including machine learning, crowdsourcing, Unmanned Aerial Vehicles and 3D monitoring.
The project is funded by the Federal Ministry for Education and Research (BMBF). Funding code: 03G0890A.
Deep learning techniques, esp. Convolutional Neural Networks (CNNs), are now widely studied for predictive analytics with remote sensing images, which can be further applied in different domains for ground object detection, population mapping, etc. These methods usually train predicting models with the supervision of a large set of training examples. However, finding ground truths especially for developing and rural areas is quite hard and manually labeling a large set of training data is costly. On the other hand Volunteered Geographic Information (VGI) (e.g., OpenStreetMap (OSM) and MapSwipe) which is the geographic data provided voluntarily by individuals, provides a free approach for such big data.
In our project, we study predictive analytics methods with remote sensing images, VGI, deep neural networks as well as other learning algorithms. It aims at deeply learning from satellite imageries with the supervision of such Volunteered Geographic Information.
