The document summarizes three research projects conducted by the Information Technologies Institute at the Centre for Research and Technology Hellas: FIRESENSE, BIO_SOS, and OUTLAND. FIRESENSE developed an early warning system to detect and monitor forest fires using sensors, cameras, and wireless networks. BIO_SOS aimed to develop ecological modeling and monitoring of biodiversity in Natura 2000 sites using earth observation data. OUTLAND focused on land cover and land use change detection. The projects utilized remote sensing, image processing, and modeling techniques.

1. Research on Remote Sensing and
Detection/Management of Forest Fires in
the Information Technologies Institute:
Research Projects FIRESENSE, BIOSOS
and OUTLAND”
Dr. Nikos Grammalidis and Dr. I. Manakos,
Centre for Research and Technology Hellas /
Information Technologies Institute
www.firesense.eu
www.biosos.eu
www.outland-project.eu

2. Outline

Introduction

Firesense project

BIO_SOS project

OUTLAND project

Conclusions
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

3. Introduction: CERTH-ITI


The Information Technologies Institute (ITI) was founded
in 1998 under the auspices of the General Secretariat of
Research and Technology of the Greek Ministry of
Development. Since March 2000, it is part of the Centre
for Research and Technology Hellas (CERTH)
It became a European Centre of Excellence in 3D and
Stereoscopic Imaging and Multimedia in 2001
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

4. Overview



Personnel: 16 affiliated professors, 7 researchers, 35
postdoctoral, more than 160 postgraduate researchers, 8
administration staff
More than 140 R&D projects funded by European
Commission Programmes, more than 60 R&D projects
funded by National Programmes and 100 Consulting
Subcontracts with the Private Sector (I&T Industry)
More than 350 journal papers, 850 conference
papers, 120 book chapters
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

5. Research areas
Geoscience and
Remote sensing
Biomedical and
Bioinformatics
Image and Signal
Processing and Coding
Environment
Human-Computer
Interaction
Computer Vision
Social Network Analysis
Security and
Surveillance
ITI
Virtual and
Augmented Reality
Patten Recognition and
Machine Learning
Multimedia
analysis
Integrated
Commercial Solutions
e-Government
Artificial Intelligence
Cultural and
Educational Technology
Communications and
Networking
Databases and
Information Systems
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

6. FIRESENSE project






FIRESENSE (Fire Detection and Management through
a Multi-Sensor Network for the Protection of Cultural
Heritage Areas from the Risk of Fire and Extreme
Weather Conditions)
ENV.2009.3.2.1.2: Technologies for protecting cultural
heritage assets from risks and damages resulting from
extreme events, especially in the cases of fires and
storms
Grand Agreement n°: 244088, STReP Project
Project start: December 1st, 2009
Project duration: 36 months
Project total cost: 3 609 027 €
EC contribution: 2 697 092 €
www.firesense.eu
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.biosos.eu
www.outland-project.eu

7. FIRESENSE partners
Netherlands
CWI
Italy
CNR
Belgium
Turkey
BILKENT
BOGAZICI
TITAN
XENICS
Tunisia
SUPCOM
Greece
CERTH
HMC
ArcFuelESSC,Conference, Thessaloniki, 18 December 2013
Final Thessaloniki, Greece, 9-14 May 2011
www.firesense.eu
www.biosos.eu
www.outland-project.eu

8. Why FIRESENSE?

Need to protect cultural heritage and archeological
sites


Majority of these sites in the Mediterranean region are
covered or surrounded by vegetation and this exposes
them to an increased risk of fire.
Ancient Olympia (Aug. 2007), Marathon (Aug. 2009), the
ancient Kameiros, Rhodes Island in 2008, the temple of
Epikouros Apollo in 1998, three Monasteries of Mount
Athos in 1990 etc.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

9. Why FIRESENSE?

Early fire warning is the only way to
avoid or minimize damages
 Need to combine state of art sensing
technologies in an integrated
surveillance system
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

10. FIRESENSE main objectives
 Development
of an automatic early
warning system to remotely monitor areas
of archaeological and cultural interest from
the risk of fire.
 Taking advantage of recent advances in
multi-sensor surveillance technologies
using a wireless sensor network, optical
and infrared cameras as well as local
weather stations on the deployment sites
 Improved fire propagation estimation and
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visualization
www.biosos.eu
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.outland-project.eu

11. System architecture
External Weather
Forecast
Sensor Polling
Video-based Fire Detection
IR Data Processing
Weather Data Processing
Data fusion
Alarm Levels 1,2 ,..
Estimation of Fire Propagation
Area Fuel Model
GIS
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

12. Visible Cameras
Smoke Detection
Fire Detection
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www.firesense.eu
www.biosos.eu
12
www.outland-project.eu

13. Video-based detection / Software platforms


Several new algorithms were developed or extended for
flame/smoke detection using visible data
Increased detection rates and lower false positive ratios
are achieved (compared to the literature)
Offline Software Platform
Online Software Platform
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

14. MULTI-SPECTRAL IMAGING
Optical
SWIR
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LWIR
www.firesense.eu
www.biosos.eu
www.outland-project.eu

15. Fire detection using IR sensors
 Fire detection algorithms based on several types of IR sensors
were developed:
• LWIR image processing
• SWIR image processing
• Covariance features based IR Video flame detection
 A Bimodal approach combining flame
detection in LWIR with smoke detection in
optical camera also yields promising
results.
Other sensors
 PIR system for flame detection
 Seismic system for wildfire detection
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

16. Wireless Sensor Network
• We also used Wireless (temperature, humidity etc.) sensor
networks for detecting sudden local variations that should raise
a fire alarm
• The network architecture is shown below
• Each node communicates via a zigbee USB dongle.
• Cluster-heads form an
infrastructure WiFi mesh
backhaul, and each governs
up to 20 end-nodes that are
immediately accessed (0
hop) via zigbee.
•Cluster-heads are directly
connected to the main
gateway which
communicates with the
Control Center using HTTP
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

17. Control Centre
 Different alarm types
 Locations and the status of
sensors on the map
 Statistical information/history from
the database
 Selection of one or more cameras
from the main screen (zoom in/out
etc).
 The optical and IR cameras rotate
automatically to the area of interest
in case of a fire alarm.
 Estimation of the fire’s propagation
using the present conditions (ignition
point, weather conditions etc)
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

18. Fire propagation estimation


To help the forest fire management and identify critical
situations, after detecting a wildfire, it is also important to
estimate of the propagation direction and speed.
Factors effecting Fire Propagation




Ignition point
Topology (Slope and Aspect)
Fuel Model
Meteorological Conditions
• Wind
• Fuel moisture – which
may depend on temperature,
humidity, time of day, etc.
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

19. Fire propagation estimation

Estimation of fire propagation (EFP) is based on the
popular BEHAVE fire behavior algorithm
implementations (fireLib, Fire Behavior SDK).

A grid of cells is defined and a fire growth modeling
algorithm is recursively applied.

Additional EFP extensions have also been implemented.

Ignition point(s) may automatically be provided by the
detection software
 Topography parameters (slope
and aspect) are extracted from a
Digital Elevation Model (DEM):
Freely available data from CGIARCSI SRTM with a resolution of 90m
www.firesense.eu
are used.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.biosos.eu
www.outland-project.eu

20. Fuel Models
Three sources of fuel data were used:
 The CORINE Land Cover (CLC) map that records 44 land
cover and land use classes which represent the major
surface types across Europe.
 Very high resolution satellite images
(QuickBird) are used for vegetation
classification.
 Ground truth (or site survey) is often
required for developing and testing
satellite image processing algorithms and
fuel modeling. Surveys for Kabeirion are
(near Thebes) were made from a) G.
Xanthopoulos and b) OMIKRON.
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

21. Vegetation Estimation Results
(based on SVM classification)
 Ground truth classified image
Post-filtered (Observation)
Classified image
Forest
Trees
Grass
Water Shadow Road
Built
Lawn
Bare soil Bare soil
1
2
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

22. Fire propagation visualization
A user-friendly 3D visualization software was
developed using GIS information from Google EarthTM
using C++ and third party libraries (Qt, Google Earth
COM API, fireLib, Fire Behaviour SDK). Supports:









Multiple layers
Multiresolution
Wind interpolation
Multiple ign. points
Variable weather
Prob. of crown fire
Physical models
etc.
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www.firesense.eu
www.biosos.eu
www.outland-project.eu

23. EFP Evaluation: Field data for the Isthmia
fire (near Corinth, 30/7/2008)
Real Burned area
Fuel maps (mapped by expert forestry researchers)
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24. Simulation results
Simulation results are seen to
be consistent with the real
observations/burned area (at
the same time instants).
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www.biosos.eu
www.outland-project.eu

25. Pilot sites

Five demonstrators were developed in four different countries
MonteferratoGalceti
Park, Prato, It
aly
Dodge
Hall, Bogazici
University, Istanbul
, Turkey
Thebes,
Boeotia,
Greece
Temple of
Water, Djebel
Zaghouan, Tunisi
a
ArcFuel Final Conference, Thessaloniki, 18 December 2013
Antalya, T
urkey
www.firesense.eu
www.biosos.eu
www.outland-project.eu

26. Conclusions

FIRESENSE developed an automatic early warning
system to remotely monitor areas of archaeological and
cultural interest from the risk of fire.

The FIRESENSE system is a powerful cost-efficient
approach that can be used for the protection of cultural
heritage providing:



High reliability: The system utilizes different sensing
technologies (CCTV cameras, PTZ, IR, temperature sensors).
Early detection of fire: Automatic detection of flame/smoke/rise
in temperature.
Forest fire management: The system provides real-time
information about fire’s extent/location through WSN, while it
also estimates and visualizes its propagation based on the
area’s fuel model, the local weather conditions and ground
morphology.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

27. BIO_SOS project
BIOdiversity Multi-Source Monitoring System: from Space
TO Species
 SPA.2010.1.1-04 - Stimulating the development of
GMES services in specific areas
 Grand Agreement n°: 263435, CP
 Project start: December 2010-November 2013
 Project total cost: 3 159 510 €
 EC contribution:
2 476 363 €
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

28. Biodiversity Multi-Source Monitoring System:
From Space To Species
Main Objective is
The development of a
pre-operational multi-modular
ecological modelling system
suitable for multi-annual
monitoring of NATURA 2000
sites and their surroundings.
1Dec 2010 15 Sept 2011
www.firesense.eu
www.biosos.eu
www.outland-project.eu

29. BIO_SOS working objectives
 The development of pre-operational HR and VHR EO data
processing and understanding techniques to provide as output
- LC/LU and LC Change (LCC) maps
as an improvement of GMES/ Copernicus core services
 The development of an ecological modelling framework at both
habitat and landscape level to combine EO and in-situ data for
site monitoring.
- Habitat maps as GHCs (Bunce, 2008)
- Habitat change maps
- Biodiversity indicators and their trends
as an extension of GMES/ Copernicus downstream–services
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www.biosos.eu
www.outland-project.eu

30. BIO_SOS points of contribution to mapping
Drawbacks of crisp classification (need for fuzzification
approach):
– Noisy input data (i.e. satellite imagery, LiDAR, ancillary data,
etc.)
– Inaccurate rule thresholds
– Intolerance to changes in illumination conditions, seasonality
– Restricted transferability to similar sites, especially in
different geographical regions
Advanced fuzzy expert rules are additionally derived by the field
surveys, for example:
- Adjacency rules & Morphology of the patch area rules
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www.outland-project.eu

31. Robust classification framework
• Rule-based classification framework based on
– Dempster–Shafer theory and
– fuzzy logic
• Application fields
– Detection of targeted entities
• Burned areas
• Flooded areas
• Particular forest types
• Arable areas
• Built or infrastructure areas
– Land cover / use mapping
– Habitat mapping
– Conservation planning
– Site management
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www.outland-project.eu

32. Habitat mapping example

Use of Dempster–
Shafer theory

To handle
uncertainty in
expert rules

To handle missing
data – Allowing
multiple classes

Use of fuzzy logic

To handle noisy
data

To handle
inaccurate expert
rules
The work presented herein was partially supported by the European Union Seventh Framework Programme
FP7/2007-2013, SPA. 2010.1.1-04:616 “Stimulating the development of 490 GMES services in specific area”, under
grant agreement 263435, project BIO_SOS: BIOdiversity Multi-Source Monitoring System: from Space To
www.firesense.eu
Species, coordinated by CNR-ISSIA, Bari-Italy.
www.biosos.eu
www.outland-project.eu

33. LCCS to GHC mapping
Example of fuzzification and the D-S uncertainty handling (1/2)
Vegetation adjacent to buildings, but with large area (>0.8ha) will be
most likely (80%) within the Natural category (i.e., TRS, or HER)
Fuzzification example:

True area: 0.82ha

Measured area: 0.76ha

No fuzzification: Non applied rule

Fuzzification: Rule applied by 40%
p
1
0.5
0
p
1
0.76 0.8
x
0.5
0.4
0 0.6
0.8 1
0.76
x
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34. LCCS to GHC mapping
Example of fuzzification and the D-S uncertainty handling (2/2)
20%
Vegetation adjacent to buildings, but with large area (>0.8ha)
will be most likely (80%) within the Natural category (i.e., TRS,
or HER)
___________D-S theory example_____________
30%
B15
Adj. to buildings: [0.5,0.8]………………………………….
-
Area > 0.8ha: 40% (as shown in fuzzification
slide)………………
-
50%
?
B15
B15
or B16
-
-
B16
50%
B15 or B16
40%
Area > 0.8ha
20%
valid
Rule validity:
[0.2,0.32]……………………………………………………….
Natural
30%
12%
maybe valid
80%
68%
invalid
rule
20%
Any other
Confidence in Natural: 80%………………………………
16%
4%
Natural
Any other
Natural: [0.16,0.256] (0,256: 0,16+0,096
Or Natural: [0.16, 0,936] (0,936:0,256+0,68) …….
in the absense of an excluding for the Natural
category rule
9.6%
maybe
natural
2.4%
?
68%
maybe
any other
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35. Improvement of classification results
• Rule expressions:
– A1: Rules with definite
outcome
– A2: Rules with uncertainty
in the outcome
• Methods:
– 6 fuzzy approaches (F1–
F6)
– Crisp classification
approach (F0)
Accuracy improvement
by ≈ 12%
Reference (“A1” and “A2” are referred to as “B1” and “B2”, respectively):
Z. Petrou, V. Kosmidou, I. Manakos, T. Stathaki, M. Adamo, C. Tarantino, V. Tomaselli, P. Blonda, M. Petrou, "A rulebased classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and
fuzzy logic", Pattern Recognition Letters, 2013, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2013.11.002. To
appear.
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36. OUTLAND project




Open protocols and tools for the Education and Training
of Voluntary organisations in the field of Civil
Protection, against Natural Disasters (forest fires) in
Greece and Bulgaria
European Territorial Cooperation Programme GreeceBulgaria 2007-2013 (2012: INTERREG IV A)
Lead partner: Municipality of Thermi
Project duration: February 2012 – February 2014
Project total cost: 1,157,380 € (Funding by EU and
National sources)
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

37. The OUTLAND project

The overall objective of the OUTLAND Project is the
creation of a complete system / framework for the
education and training of the Firest Fire Volunteers
Groups of Civil Protection Agencies in Greece and
Bulgaria. This framework includes:




educational material,
the necessary infrastructure,
tools and mechanisms (with emphasis to novel informatics
applications)
The aim is to establish an educational and training
framework for Civil Protection volunteers that will be
available and useful after the end of the project.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
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www.biosos.eu
www.outland-project.eu

38. CERTH/ITI role in OUTLAND

CERTH develops novel informatics tools within the project
 Development of novel user-friendly informatics applications that are based on
the communication between:


a mobile application for Android Smartphones and
a Control Center with fire simulation capabilities able to support various volunteer training
scenarios.

Development Mobile Technology (Android SDK) and Fire Simulation
Techniques
 An e-learning platform (based on Moodle) for the education and training of
volunteers was also developed.
 CERTH is also responsible for the OUTLAND web page.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

39. SYSTEM FUNCTIONALITIES
1. Report of Real Fire Incidents:
The volunteer is able to report Fire Incidents (via Internet or SMS) to a
Control Center, which estimates the location of the Fire and informs the
authorities via email about the Fire Incident.
2. Report of Vegetation Types:
The volunteer is able to report (via Internet) the Vegetation Types of an
area to the Control Center. A web tool for editing Vegetation Type was
also developed.
3. Training Scenarios:
The volunteer is able to participate (via Internet) in Training Scenarios
organized by the Control Center.
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40. SYSTEM COMPONENTS

A mobile application for Android Smartphones

A Control Center consisting of 2 main Units :
Unit for receiving Fire Incident Reports
Unit for organizing Training Scenarios
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41. MOBILE APPLICATION
1. Report
Fire
Screen
2. Vegetation
Report
Screen
Main Screen:
Login Screen:
Initial
Screen
The user enters
username &
password and
sends login
request to the
Control Center
The user selects
the functionality
he wants to use
ArcFuel Final Conference, Thessaloniki, 18 December 2013
3. Training
Scenarios
Screen
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42. CONTROL CENTER: Fire Report Unit
 Receives Fire Incidents, estimates the Fire Location and informs the
authorities via email.
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43. CONTROL CENTER: Training Scenarios Unit
 Organizes Training Scenarios based on Fire Simulations.
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44. FUNCTIONALITY 1: Fire Report
(a) Report via Internet (Mobile Application)

The volunteer takes a picture of the Fire and sends to the Control Center via
Internet :
1. The picture of the Fire.
2. The location of his device: values of Latitude / Longitude / Altitude.
3. The rotation angles ( = orientation) of the device camera: values of
Heading / Tilt / Roll angles.
The volunteer sends via Internet the
data for the Report of a Fire Incident to
the Control Center
Mobile Application
Control Center
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45. FUNCTIONALITY 1: Fire Report
(a) Report via Internet (Control Center)

When the Control Center receives a Fire Report:
1. Shows the view of the device camera in
Google Earth, using the location and rotation
values it received from the fire report.
2. Shows the location of the
volunteer in Google Maps.
3. Shows
the
Picture
of the
Fire
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46. FUNCTIONALITY 1: Fire Report
(a) Report via INTERNET (Control Center)
4. Calculates the Latitude and Longitude of the Center of Google Earth Window. This
point is an estimation of the Fire Location.
5. Shows the estimated Fire Location in Google Maps.
6. Sends email to specific email addresses with the report data.
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47. FUNCTIONALITY 1: Fire Report
(b) Report via SMS
1. The volunteer sends an SMS with the data (the picture is not sent) to
another smartphone device which is located in the Control Center.
2. This device acts as a «gateway» between the networks of Mobile
Telephony and the Local Internet of Control Center.
3. The «gateway» device receives the SMS, reads the data and sends it
via Internet to the Server Computer of the Control Center.
The volunteer
sends SMS to the
«gateway» device.
Volunteer
device
Device
«Gateway»
The
«gateway»
device sends
the report
via Internet
to the server
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Server of the Control Center
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48. FUNCTIONALITY 2: Vegetation Report
(Server Application “FuelTypes”)




“Fuel Types” Application
(developed by OMIKRON) is a
plugin in the open source
Quantum GIS software.
Vegetation types from EUNIS
(2004) habitat classification
system were used. Each Level
III habitat type was mapped to
one Scott-Burgan fuel model.
A rectangular grid with 50m x
50m cells is defined. The areas
of interest in Municipality of
Thermi are shown within the
red boundaries.
The software allows editing the
classification/fuel type of a cell.
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49. FUNCTIONALITY 2: Vegetation Report
(Mobile Application)
1. The user selects from the Main Screen
of the application the functionality of
Vegetation Report.
2. The user selects from a list
the Vegetation Type which
best describes the
vegetation of his location.
Selection of Functionality
«Vegetation Report»
Selection of
Vegetation Type from
a list with Scott and
Burgan Vegetation
Types.
3. The user sends the Vegetation Type
and the location coordinates of his
device (latitude, longitude) to the
Control Center.
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Send Vegetation Type and
location coordinates to the
Control Center
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50. FUNCTIONALITY 2: Vegetation Report
(Control Center)

The Control Center receives the Vegetation Report and updates the
records of Vegetation Types in the Database.

Thus, Fuel Maps for the areas of interest are created/updated.

The Fuel Maps allow us to estimate the fire behavior within each cell, in
case of a fire.

Fuel Maps are used by our System for the estimation of the fire spread
and flame length, when we run Fire Simulations to the Control Center.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
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51. FUNCTIONALITY 3: TRAINING
SCENARIOS (Mobile Application)

During Training Scenarios, the volunteer is able to use the mobile
application so as to:
1. Periodically report his location to the Control Center. For this
purpose, he sends periodically - via Internet - his location coordinates to
the Control Center (values of longitude, latitude, altitude).
The volunteer
sends
periodically his
location to the
Control Center
Mobile Application
Control Center
ArcFuel Final Conference, Thessaloniki, 18 December 2013
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52. FUNCTIONALITY 3: TRAINING
SCENARIOS (Mobile Application)
2. Receive from the Control Center and see in his device (using Google
Maps):
i. The positions of all fellow-volunteers involved in the scenario.
ii.
Fire Simulations and Safe Routes, for the safe movement of a
volunteer from a point Α to a point Β.
The user receives from the
Control Center Fire Simulations
and Safe Routes
Mobile Application
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
Control Center
www.outland-project.eu

53. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)

1.
During Training Scenarios, the Control Center:
Receives and shows the positions of the volunteers in Google Maps:
The positions
of volunteers
are displayed
in Google
Maps (blue
markers)
List with the
volunteers of
the scenario
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
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54. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)
2. Runs Fire Simulations

A Fire Simulation Software is used, which
estimates the Fire Spread and shows the
Fire Simulations in Google Earth.

The Fire Simulation Software accepts
various input parameters:

The Fuel Models of the area.

The direction / speed of the Wind.

An Ignition Point, which is the start point of the Fire.
ArcFuel Final Conference, Thessaloniki, 18 December 2013
Fire Simulation
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55. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)

Volunteers’
positions

Fire Simulation
in Google Maps

ArcFuel Final Conference, Thessaloniki, 18 December 2013
Fire Simulation
in Google Earth
www.firesense.eu
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56. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)
3. Calculates Safe Routes for the safe move of the volunteers from a Point
A to another Point B and shows the routes in Google Maps.
Volunteers
Fire
Simulation
Start of the
Route
End of the
Route
Route
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

57. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)
3. Sends the Fire Simulation and the Safe Routes to the mobile devices .
Control Center



Mobile Application
The Safe Routes are calculated with the use of the Routing Software pgrouting
(http://pgrouting.org/).
The calculation of the Routes uses the results of the Fire Simulation in order to
reject the routes which are not safe (are threatened by fire).
We use of existing open source data for the road network (OpenStreetMap).
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
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www.outland-project.eu

58. FUNCTIONALITY 3: TRAINING
SCENARIOS (Control Center)

Routing
Algorithms are
used, in order
to choose the
shortest safe
routes for the
safe movement
of the
volunteers.

Statistical
measurements
can be provided
(time for the
movement
of the
volunteers
from a point Α
to a point Β).
Fire Simulation and Safe Route
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.firesense.eu
www.biosos.eu
www.outland-project.eu

59. Conclusions and Future Work


Novel rule-based fuzzy habitat classification algorithms
have been developed within BIOSOS project
FIRESENSE and OUTLAND projects allowed CERTHITI to develop powerful fire detection and management
tools



We are currently integrating the functionalities of both tools in a
common framework/product
We indent to release of the core of the EFP software
developed in FIRESENSE (with extensions by Dr.
Xanthopoulos) as open source in the future
The interaction and collaboration between forestry and
informatics experts was very fruitful and led to interesting
results.
www.firesense.eu
ArcFuel Final Conference, Thessaloniki, 18 December 2013
www.biosos.eu
www.outland-project.eu

60. Thank you
Questions?
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www.outland-project.eu