Challenges for Qualitative Spatial Reasoning in Linked Geospatial Data
Manolis Koubarakis and Kostis Kyzirakos and Manos Karpathiotakis and
Charalampos Nikolaou and Michael Sioutis and Stavros Vassos
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens
Dimitrios Michail
Dept. of Informatics and Telematics, Harokopio University of Athens
Themistoklis Herekakis and Charalampos Kontoes and Ioannis Papoutsis
Inst. for Space Applications and Remote Sensing, National Observatory of Athens
fkoubarak,kkyzir,mk,charnik,sioutis,stavrosvg@di.uoa.gr
ftherekak,kontoes,ipapoutsisg@space.noa.gr
fmichailg@hua.gr
Abstract
Linked geospatial data has recently received atten-
tion, as researchers and practitioners have started
tapping the wealth of geospatial information avail-
able on the Web. We discuss some core research
problems that arise when querying linked geospa-
tial data, and explain why these are relevant for the
qualitative spatial reasoning community. The prob-
lems are presented in the context of our recent work
on the models stRDF and stSPARQL and their ex-
tensions with indefinite geospatial information.
1 Introduction
Linked data is a new research area which studies how one can
make RDF data available on the Web, and interconnect it with
other data with the aim of increasing its value for everybody
[Bizer et al., 2009]. The resulting “Web of data” has recently
started being populated with geospatial data. A representa-
tive example of such efforts is LinkedGeoData1 where Open-
StreetMap data are made available as RDF and queried using
the declarative query language SPARQL [Auer et al., 2009].
With the recent emphasis on open government data, some of
it encoded already in RDF2, portals such as LinkedGeoData
demonstrate that the development of useful Web applications
might be just a few SPARQL queries away.
We have recently developed stSPARQL, an extension of
the query language SPARQL for querying linked geospa-
tial data [Koubarakis and Kyzirakos, 2010]3. stSPARQL has
been fully implemented and it is currently being used to query
1http://linkedgeodata.org/
2http://data.gov.uk/linked-data/
3The paper [Koubarakis and Kyzirakos, 2010] presents the lan-
guage stSPARQL that also enables the querying of valid times of
triples. Here, we omit time and discuss only the geospatial subset of
stSPARQL.
linked data describing sensors in the context of project Sem-
sorGrid4Env4 [Kyzirakos et al., 2010] and linked earth obser-
vation (EO) data in the context of project TELEIOS5.
In the context of TELEIOS we are developing a Virtual Ob-
servatory infrastructure for EO data. One of the applications
of TELEIOS is fire monitoring and management led by the
National Observatory of Athens (NOA). This application fo-
cuses on the development of techniques for real time hotspot
and active fire front detection, and burnt area mapping. Tech-
nological solutions to both of these cases require the integra-
tion of multiple, heterogeneous data sources, some of them
available on the Web, with data of varying quality and vary-
ing temporal and spatial scales.
In this paper we show how well-known approaches to qual-
itative spatial representation and reasoning [Renz and Nebel,
2007] can be used to represent and query linked geospatial
data using RDF and stSPARQL. Thus, we propose linked
geospatial data as an interesting application area of qualita-
tive spatial reasoning techniques, and discuss open problems
that might be of interest to the qualitative spatial reasoning
community. In particular, we address the problem of repre-
senting and querying indefinite geospatial information, and
discuss the approach we adopt in TELEIOS.
The organization of the paper is as follows. Section 2 in-
troduces the kinds of linked geospatial data that we need to
represent in the NOA application of TELEIOS, shows how to
represent it in stRDF, and presents some typical stSPARQL
queries. Then, Section 3 shows how the introduction of qual-
itative spatial information in the stRDF data model enables
us to deal with the NOA application more accurately. The
same section introduces the new model stRDFi which al-
lows qualitative spatial information to be expressed in RDF
and gives examples of interesting queries in the new model.
In Section 4 we proceed to discuss some open problems in
the stRDFi framework that require new contributions by the
4http://www.semsorgrid4env.eu/
5http://www.earthobservatory.eu/

qualitative spatial reasoning community. Finally, in Section 5
we discuss related work and in Section 6 we draw conclu-
sions.
The paper is mostly informal and uses examples from the
NOA application of TELEIOS. Even in the places where the
paper becomes formal, we do not give any detailed tech-
nical results for which the interested reader is directed to
[Koubarakis et al., 2011].
2 Linked geospatial data in the NOA
application
The NOA application of TELEIOS concentrates on the devel-
opment of solutions for real time hotspot and active fire front
detection, and burnt area mapping. Technological solutions
to both of these cases require integration of multiple, hetero-
geneous data sources with data of varying quality and vary-
ing temporal and spatial scales. Some of the data sources are
streams (e.g., streams of EO images) while others are static
geo-information layers (e.g., land use/land cover maps) pro-
viding additional evidence on the underlying characteristics
of the affected area.
2.1 Datasets
The following datasets are available in the NOA application:
 Hotspot maps. NOA operates a MSG/SEVIRI6 acqui-
sition station and receives raw satellite images every 15
minutes. These images are processed with image pro-
cessing algorithms to detect the existence of hotspots.
The information related to hotspots is stored in ESRI
shapefiles and KML files. These files hold informa-
tion about the date and time of image acquisition, carto-
graphic X, Y coordinates of detected fire locations, the
level of reliability in the observations, the fire radiative
power assessed, and the observed fire area. NOA re-
ceives similar hotspot shapefiles covering the geograph-
ical area of Greece from the European project SAFER
(Services and Applications for Emergency Response).
 Burnt area maps. From project SAFER, NOA also
receives ready-to-use accumulated burnt area mapping
products in polygon format, projected to the EGSA87
reference system7. These products are derived daily us-
ing the MODIS satellite and cover the entire Greek ter-
ritory. The data formats are ESRI shapefiles and KML
files with information relating to date and time of image
acquisition, and the mapped fire area.
 Corine Land Cover data. The Corine Land Cover
project is an activity of the European Environment
Agency which is collecting data regarding land cover
(e.g., farmland, forest) of European countries. The
Corine Land Cover nomenclature uses a hierarchical
scheme with three levels to describe land cover:
6MSG refers to Meteosat Second Generation satellites, and SE-
VIRI is the instrument which is responsible for taking infrared im-
ages of the earth.
7EGSA87 is a 2-dimensional projected coordinate reference sys-
tem that describes the area of Greece.
Figure 1: An example of hotspots and burnt area mapping
products in the region of Attiki, Greece
– The first level consists of five items and indicates
the major categories of land cover on the planet,
e.g., forests and semi-natural areas.
– The second level consists of fifteen items and
is intended for use on scales of 1:500,000 and
1:1,000,000 identifying more specific types of land
cover, e.g., open spaces with little or no vegetation.
– The third level consists of forty-four items and is
intended for use on a scale of 1:100,000, narrow-
ing down the land use to a very specific geographic
characterization, e.g., burnt areas.
The land cover of Greece is available as an ESRI shape-
file that is based on the Corine Land Cover nomencla-
ture.
 Coastline geometry of Greece. An ESRI shapefile that
describes the geometry of the coastline of Greece is
available.
Figure 1 presents an example of hotspots and burnt area
mapping products, as viewed when layered together over a
map of Greece.
2.2 Using semantic web technology
An important challenge in the context of TELEIOS is to de-
velop advanced semantics-based querying of the available
datasets along with linked data available on the web. This
is a necessary step in order to unlock the full potential of the
available datasets, as their correlation with the abundance of
data available in the web can offer significant added value.
As an introduction to Semantic Web technology, we present a
simple example that shows how burnt area data is expressed
in the language stRDF, and then proceed to illustrate some
interesting queries using the language stSPARQL.
Similar to RDF, in stRDF we can express information using
triples of URIs, literals, and blank nodes in the form “subject
predicate object”. Figure 2 shows four stRDF triples that
encode information related to the burnt area that is identified