Earth Observation Data for Enterprise Business
Enterprise Resource Planning (ERP) is referring to a complex software system
which helps to optimize business decisions for companies. The foundation for good
business decisions is the right information and this raises the questions where to ﬁnd
this valuable information and how to integrate it into business processes. Companies
are collecting and processing all kinds of data to ﬁnd the right information to
improve and make an effective business decision.
Earth observation data is becoming more and more important for business
analysts all over the world. The value of earth observation data is not just measured
by their resolution and visual spectrum of imagery. Satellite images contain much
more information in the non-visual spectrum stored in multiple bands which require
domain speciﬁc knowledge for new disruptive business models. Furthermore,
satellites are continuously monitoring our planet which ensures a full coverage ratio
of it and which allows to collect and archive historical data. Especially the temporal
aspect of geo-data is extremely useful for business analytics such as time series and
change detection queries.
Nevertheless, earth observation data analysis is still a very scientiﬁc and compli-
cated topic and most of the business users are not able to consume raw imagery
mainly because of infrastructure and complexity challenges. To simplify the
information ﬂow, an abstraction layer needs to handle the complexity transparently.
It contains solutions for pre-ﬁltering relevant images, such as cloud free imagery,
and built-in algorithms created with expert knowledge in order to provide valuable
and easy to understand information for the end user. This information can be
delivered through micro-services to all kinds of clients such as GIS systems,
business applications or custom apps. Developers can easily consume information
and embed it into reports and analytic applications if they understand the value and
H. Gildhoff ()
SAP, Heidelberg, Germany
© The Author(s) 2018
P.-P. Mathieu, C. Aubrecht (eds.), Earth Observation Open Science and Innovation,
ISSI Scientiﬁc Report Series 15, https://doi.org/10.1007/978-3-319-65633- 5_13
272 H. Gildhoff
meaning of it and the interface is state of the art. Such an abstract interface opens the
door for earth observation data to a broader audience—the business and developer
community. Finally, users may not even know that speciﬁc KPI’s, values or other
indicators are coming from a complex temporal image processing chain.
Business industries such as public, utility, security and insurance have already
been leveraging earth observation data for a longer time. They have some knowledge
about satellite data but are still facing a lot of challenges such as managing IT
infrastructure, service level agreements, missing standards and expert knowledge.
Other industries such as the retail industry are completely new to imagery, and
most of them are not using it because of insufﬁcient experience and knowledge
in conjunction with high complexity challenges.
But now, by using the information provided by the cloud service SAP HANA
Earth Observation Analysis, customers can focus on their business and still integrate
earth observation data without having to care about its complex processing. In the
scenario below, we will describe how an insurance company can use a simple REST
API for analyzing natural disasters. We are focusing on wildﬁres but it could easily
be extended to ﬂoods, storms etc.
Wildﬁre events have certain important characteristics such as ﬁrst time detected,
duration of active ﬁre, radius, temperature, burned area (called footprint) and so on.
Since all this information can be extracted from raw satellite imagery—non-stop
and for the whole planet—satellite information are an ideal source for establishing
a historical database of occurred ﬁre and wildﬁre events. Very often, the ﬁrst step
for GIS experts is analyzing such past events of natural disasters (see Fig. 1).
Importantly, experts then also have the chance to learn from historical informa-
tion. They need to understand: what are the characteristics of large ﬁre events, what
Fig. 1 Historical ﬁre events in the USA
Earth Observation Data for Enterprise Business Applications 273
Fig. 2 Change detection based on NDVI
makes up critical areas, what are the circumstances for natural disasters, and how
are these changing over time and space. Subsequently, such knowledge can be used
for risk mitigation, a crucial task for an insurance company.
Each time a new ﬁre event is detected, earth observation data can be used to
assess the area and inform the insurance company about the wildﬁre footprint so
they can apply their knowledge and business data to determine affected customers
and simulate the maximum exposure (see Fig. 2).
In order to calculate the area affected by a wildﬁre, the footprint, for example, the
insurance company can use a simple REST call to a service as mentioned above with
parameters for the area of interest, two timestamps (before and during the event), a
threshold parameter and the return type. This call will then return a representation as
speciﬁed of how the area of interest has changed in the time between both provided
timestamps (see Fig. 2). By regularly checking the earth observation catalogue of
the service for any new images of the affected area, the insurance company can
analyze how much the wildﬁre has changed, and store each footprint in GeoJSON,
Well-Known-Text (WKT), or another representation of vector data. This leads to
a continuously calculated accurate footprint of the wildﬁre which the insurance
company can use to calculate intersections with their business data and buffer
queries to ﬁnd customers near the risk area.
With the help of historical data, it is even possible to analyze the current risk
status of an area of interest. In order to achieve this, neural networks can be trained
with data sets from the past 30 up to 60 days, for example. The network calculates
a prediction score between 0.0 and 1.0 for the next 15 days which describes the
probability for a ﬁre in that area around that time.
274 H. Gildhoff
Fig. 3 Wild ﬁre ris k map
This information can then be displayed as a hazard map (see Fig. 3). Here, the
area is divided into grid cells, and each cell is categorized somewhere between very
low and very high according to the probability of a ﬁre event occurring in this area.
Different kinds of neural network technologies can be used such as a Long-Short-
Term Memory (LSTM) or Convolutional Neural Network (CNN). Most important
is the trainings data which can all be derived from satellite data. For wildﬁres,
companies can use surface information about vegetation and ﬁres like Normalized
Differenced Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Absorbed
Photosynthetic Active Radiation (FAPAR), Dry Matter Productivity (DMP) and
Burned area (BA).
For the insurance company, this kind of risk analysis is important for better loss
prediction, reduced accumulation losses, optimized portfolio steering, improved
claims management and risk mitigation.
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