=Paper=
{{Paper
|id=None
|storemode=property
|title=Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies
|pdfUrl=https://ceur-ws.org/Vol-914/paper_28.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/KyzirakosKGNBSPHMKK12
}}
==Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies==
https://ceur-ws.org/Vol-914/paper_28.pdf
Real Time Fire Monitoring Using Semantic Web
and Linked Data Technologies?
K. Kyzirakos1 , M. Karpathiotakis1 , G. Garbis1 , C. Nikolaou1 , K. Bereta1 , M.
Sioutis1 , I. Papoutsis2 , T. Herekakis2 , D. Michail3 , M. Koubarakis1 , and C.
Kontoes2
1
National and Kapodistrian University of Athens
2
National Observatory of Athens
3
Harokopio University of Athens
koubarak@di.uoa.gr
1 Introduction
Fire monitoring and management in Mediterranean countries such as Greece is
of paramount importance. Almost every summer massive forest fires break out,
causing severe destruction and even human life losses. Thus, European initiatives
in the area of Earth Observation (EO), such as GMES SAFER4 , have supported
the development of relevant operational infrastructures. In the context of the
European project TELEIOS5 , we aim at developing a fire monitoring service,
that goes beyond operational systems currently deployed in various EO data
centers, by building on Semantic Web and Linked Data technologies.
In this demonstration we present the fire monitoring service that we have
implemented using TELEIOS technologies focusing on its Semantic Web related
functionality. The service implements a processing chain where raw satellite im-
ages are analyzed and hotspots (pixels of the image corresponding to geographic
regions possibly on fire) are detected. The products of this analysis are encoded
in RDF, so they can be combined with auxiliary linked geospatial data (e.g.,
GeoNames, OpenStreetMap). By comparing detected hotspots with auxiliary
data their accuracy can be determined. For example, hotspots lying in the sea
are retrieved and marked as invalid. Additionally, we can combine diverse infor-
mation sources and generate added-value thematic maps which are very useful
to civil protection agencies and firefighting teams during emergency situations.
In the rest of this demo paper we first describe in short the contributions of
project TELEIOS. Then, we present the developed fire monitoring service and
its advances compared to relevant deployed services. Finally, we describe how
we plan to present this service through a live demonstration.
?
This work has been funded by the FP7 project TELEIOS (257662).
4
http://www.emergencyresponse.eu/
5
http://www.earthobservatory.eu/
2 TELEIOS Contributions
TELEIOS is a recent European project that addresses the need for scalable access
to PBs of EO data and the effective discovery of knowledge hidden in them.
TELEIOS started on September 2010 and it will last for 3 years. In the first
18 months of the project, we have made significant progress in the development
of state-of-the-art techniques in Scientific Databases, Semantic Web and Image
Mining and have applied them to the management of EO data.
We have developed SciQL [6], a new SQL-based query language for scien-
tific applications with arrays as first-class citizens. This allows us to store EO
data (e.g., satellite images) in the database, and express low level image process-
ing (e.g., georeferencing) and image content analysis (e.g., pixel classification)
in a user-friendly high-level declarative language that provides efficient array
manipulation primitives. SciQL is implemented on top of the state of the art
column-store DBMS MonetDB6 , which offers capabilities for scalable querying.
We have also developed the model stRDF, an extension of the W3C stan-
dard RDF for representing time-varying geospatial data [1, 2]. The accompanying
query language, stSPARQL, is an extension of the query language SPARQL 1.1
and it has been implemented in the semantic geospatial DBMS Strabon7 , which
offers scalability to billions of stRDF triples [4]. In applications, such as the
fire monitoring service presented here, stRDF is used to represent satellite im-
age metadata (e.g., time of acquisition), knowledge extracted from satellite im-
ages (e.g., spatial extent of hotspots), and auxiliary geospatial data encoded as
linked data (e.g., GeoNames). So, rich user queries that cannot be expressed with
database technologies of EO data centers can be expressed in stSPARQL. This
is illustrated in this demonstration, but also in [3] where some of the knowledge
discovery techniques pioneered by TELEIOS are also discussed.
3 The NOA Fire Monitoring Application
The National Observatory of Athens (NOA) operates an MSG/SEVIRI satellite
acquisition station, and has developed a real-time fire hotspot detection service
for effectively monitoring a fire-front. We present this service graphically in Fig-
ure 1 and explain below in some detail the improvements that we have achieved
by using TELEIOS technologies.
On a regular basis (5 or 15 minutes) satellite images arrive at the acquisition
station and are stored as arrays in MonetDB. The arrays are processed with a
series of SciQL queries (for cropping, georeferencing, and hotspot detection) and
shapefiles describing the detected hotspots are generated for each acquisition.
Because of the low spatial resolution of the SEVIRI instrument, possible errors
in image georeferencing, and potential weaknesses of the algorithms in [5], the
derived products have limited accuracy for specific scenarios. We increase their
accuracy by combining them with linked geospatial data.
6
http://www.monetdb.org/
7
http://www.strabon.di.uoa.gr/
Front End
Linked
Semantic stSPARQL Geospatial
Annotation Data
SQL NOA Ontology
Hotspots SciQL
Detection
Fig. 1. The NOA fire monitoring service
The main problem with the product accuracy is the existence of false alarms
in the fire detection technique. For example, hotspots shown to be occurring
in the sea or in locations with inconsistent land use (e.g., urban areas) should
be considered false alarms instead of forest fire emergency situations. To query
generated data using stSPARQL and combine it with linked data, we derive
stRDF triples from the generated shapefiles. The derived triples mainly hold
information about the coordinates of detected fire location, the date and time,
and the confidence level of the detection for each hotspot. We execute stSPARQL
updates which compare the hotspots with two RDF datasets and mark as false
positives the hotspots that lie in the sea or in locations with inconsistent land use.
The datasets that we use are: (i) a dataset describing the coastline of Greece8 ,
and (ii) a dataset describing the Greek environmental landscape9 .
Another problem is spatial and temporal inconsistencies in hotspots gener-
ated by the processing chain due to using a single image acquisition and not
using information from previous acquisitions. A simple heuristic we use is re-
trieving hotspots that were detected at least once during a specific time period
(e.g., half hour) but they were not detected in the last acquisition. In this case
we add a virtual hotspot for the last acquisition with a confidence level equals
to the average confidence level of the real detections during the last half hour.
Finally, the need to generate added-value thematic maps is addressed. The
Linked Open Data Cloud supplies an abundance of datasets, in addition to inter-
nal EO data, that cover a large variety of geospatial entities, ranging from fine-
grained geometric objects like fire stations, to coarser ones like countries. So, in-
stead of manually combining heterogeneous data, a user can pose an stSPARQL
query for each layer that she wants to depict in a map and overlay the retrieved
data using the ability of Strabon to expose data in KML or GeoJSON. Although
this service has been designed for Greece, it can be applied to any geographic area
due to the generality of the used technologies(e.g., RDF, linked data, KML).
8
This dataset has been compiled in the context of TELEIOS and is available from
http://geo.linkedopendata.gr/coastline_gr/.
9
http://geo.linkedopendata.gr/corine/
Fig. 2. The NOA fire monitoring application GUI
4 Demonstration
The demonstration consists of three parts. First, the user will start an instance
of the processing chain described above and browse its results in the GUI of the
application (shown in Figure 2). The user can also use the search functionality or
pose stSPARQL queries to retrieve fire products of previously executed instances
of the processing chain. Second, the demonstration focuses on the improvement
of the accuracy of the fire products. We will demonstrate how stSPARQL update
statements and linked geospatial data are used in order to increase the accuracy
of derived fire products. Finally, the creation of added-value thematic maps by
combining information from different data sources will be demonstrated.
References
1. Koubarakis, M., Karpathiotakis, M., Kyzirakos, K., Nikolaou, C., Sioutis, M.: Data
Models and Query Languages for Linked Geospatial Data. In: Reasoning Web. Se-
mantic Technologies for Advanced Query Answering. LNCS, vol. 7487, pp. 290–328.
Springer (2012)
2. Koubarakis, M., Kyzirakos, K.: Modeling and querying metadata in the semantic
sensor web: The model stRDF and the query language stSPARQL. In: ESWC (2010)
3. Koubarakis, M., Sioutis, M., Kyzirakos, K., Karpathiotakis, M., Nikolaou, C., Vas-
sos, S., Garbis, G., Bereta, K., Dumitru, O.C., Molina, D.E., Molch, K., Schwarz, G.,
Datcu, M.: Building Virtual Earth Observatories using Ontologies, Linked Geospa-
tial Data and Knowledge Discovery Algorithms. In: ODBASE (2012)
4. Kyzirakos, K., Karpathiotakis, M., Koubarakis, M.: Strabon: A Semantic Geospatial
DBMS. In: Proceedings of the 11th International Semantic Web Conference (2012)
5. Sifakis, N., Iossifidis, C., Kontoes, C., Keramitsoglou, I.: Wildfire Detection and
Tracking over Greece Using MSG-SEVIRI Satellite Data. Remote Sensing 3, 524–
538 (2011)
6. Zhang, Y., Kersten, M.L., Ivanova, M., Nes, N.: SciQL: bridging the gap between
science and relational DBMS. In: IDEAS (2011)