Dr. nikos grammalidis (information technologies institute) “research on remote sensing and dete

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  • A robust rule-based (using expert knowledge) classification framework has been developed, based on the principles of Dempster–Shafer theory (mathematical theory of evidence, a generalization of Bayesian theory of subjective probability) and fuzzy logic.The framework may be applied on the detection of targeted entities, such as burned or flooded areas, forest types of interest (e.g., broadleaved, coniferous, mixed forests, etc.), arable or cultivated areas, built or infrastructure areas, etc. It may also be used for Land cover / land use mapping, habitat mapping and in general, applications involving rule-based classification.
  • A robust rule-based (using expert knowledge) classification framework has been developed, based on the principles of Dempster–Shafer theory (mathematical theory of evidence, a generalization of Bayesian theory of subjective probability) and fuzzy logic.The framework may be applied on the detection of targeted entities, such as burned or flooded areas, forest types of interest (e.g., broadleaved, coniferous, mixed forests, etc.), arable or cultivated areas, built or infrastructure areas, etc. It may also be used for Land cover / land use mapping, habitat mapping and in general, applications involving rule-based classification.
  • A robust rule-based (using expert knowledge) classification framework has been developed, based on the principles of Dempster–Shafer theory (mathematical theory of evidence, a generalization of Bayesian theory of subjective probability) and fuzzy logic.The framework may be applied on the detection of targeted entities, such as burned or flooded areas, forest types of interest (e.g., broadleaved, coniferous, mixed forests, etc.), arable or cultivated areas, built or infrastructure areas, etc. It may also be used for Land cover / land use mapping, habitat mapping and in general, applications involving rule-based classification.
  • A robust rule-based (using expert knowledge) classification framework has been developed, based on the principles of Dempster–Shafer theory (mathematical theory of evidence, a generalization of Bayesian theory of subjective probability) and fuzzy logic.The framework may be applied on the detection of targeted entities, such as burned or flooded areas, forest types of interest (e.g., broadleaved, coniferous, mixed forests, etc.), arable or cultivated areas, built or infrastructure areas, etc. It may also be used for Land cover / land use mapping, habitat mapping and in general, applications involving rule-based classification.
  • An example of the use of the classification approach in habitat mappingThe advantages of D-S theory include the ability to i) incorporate and handle uncertainty in the rules provided by the experts (e.g., “if [Condition 1] and [Condition 2] hold, then the resulting class is PROBABLY [ClassA] or less probably [ClassB or ClassC or AnyClass]”) and ii) handle missing data (e.g., if no data exist to check whether [ConditionX] holds to discriminate among potential classes, multiple classes may be naturally returned instead, say classified as “ClassA or ClassB”)The advantages of fuzzy logic include i) handling of noisy data and ii) inaccurate thresholds given by the experts within the classification rules.
  • Dr. nikos grammalidis (information technologies institute) “research on remote sensing and dete

    1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 www.firesense.eu visualization www.biosos.eu ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.outland-project.eu
    11. 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. 12. Visible Cameras Smoke Detection Fire Detection ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu 12 www.outland-project.eu
    13. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    14. 14. MULTI-SPECTRAL IMAGING Optical SWIR ArcFuel Final Conference, Thessaloniki, 18 December 2013 LWIR www.firesense.eu www.biosos.eu www.outland-project.eu
    15. 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. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    17. 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. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    19. 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. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    21. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    22. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    23. 23. EFP Evaluation: Field data for the Isthmia fire (near Corinth, 30/7/2008) Real Burned area Fuel maps (mapped by expert forestry researchers) ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    24. 24. Simulation results Simulation results are seen to be consistent with the real observations/burned area (at the same time instants). ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    25. 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. 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. 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. 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. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    30. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    31. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    32. 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. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    34. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    35. 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. www.firesense.eu www.biosos.eu www.outland-project.eu
    36. 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. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    38. 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. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    40. 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 www.firesense.eu ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.biosos.eu www.outland-project.eu
    41. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    42. 42. CONTROL CENTER: Fire Report Unit  Receives Fire Incidents, estimates the Fire Location and informs the authorities via email. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    43. 43. CONTROL CENTER: Training Scenarios Unit  Organizes Training Scenarios based on Fire Simulations. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    44. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    45. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    46. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    47. 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 ArcFuel Final Conference, Thessaloniki, 18 December 2013 Server of the Control Center www.firesense.eu www.biosos.eu www.outland-project.eu
    48. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 www.firesense.eu www.biosos.eu www.outland-project.eu
    49. 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. ArcFuel Final Conference, Thessaloniki, 18 December 2013 Send Vegetation Type and location coordinates to the Control Center www.firesense.eu www.biosos.eu www.outland-project.eu
    50. 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 www.outland-project.eu
    51. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    52. 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. 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 www.biosos.eu www.outland-project.eu
    54. 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 www.firesense.eu www.biosos.eu www.outland-project.eu
    55. 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 www.biosos.eu www.outland-project.eu
    56. 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. 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 www.biosos.eu www.outland-project.eu
    58. 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. 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. 60. Thank you Questions? www.firesense.eu www.biosos.eu www.outland-project.eu

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