Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/34743
Title: Multimodal Dataset for Wildfire Risk Prediction in Cyprus
Authors: Prodromou, Maria 
Girtsou, Stella 
Leventis, Georgios 
Koumoulidis, Dimitrios 
Tzouvaras, Marios 
Mettas, Christodoulos 
Apostolakis, Alexis 
Kaskara, Mariza 
Kontoes, Haris 
Hadjimitsis, Diofantos G. 
Major Field of Science: Natural Sciences;Engineering and Technology
Field Category: NATURAL SCIENCES;ENGINEERING AND TECHNOLOGY;Civil Engineering
Keywords: Data cube;Wildfires;Cyprus;Fire management
Issue Date: 5-Sep-2024
Source: International Geoscience and Remote Sensing Symposium (IGARSS), 2024, vol.1 no.5
Start page: 1
End page: 5
Project: EXCELSIOR: ERATOSTHENES Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment 
Journal: International Geoscience and Remote Sensing Symposium (IGARSS) 
Abstract: Wildfires detection is a major issue for authorities. There are various causes of fire events with the most common being human influence. A fire risk prediction model through the analysis of geo-environmental and climate data is important for early warning and fire management. In this work, a dataset from multiple modalities, including road density, travelers, forest-agriculture interface, burned areas from historical fire events, metrological data, land cover, vegetation indices from data cube, is generated. Artificial intelligence and machine learning models can use this multimodal dataset to improve forest fire management.
URI: https://hdl.handle.net/20.500.14279/34743
ISBN: 979-8-3503-6032-5
DOI: 10.1109/IGARSS53475.2024.10642963
Rights: Attribution 4.0 International
Type: Article
Affiliation : ERATOSTHENES Centre of Excellence 
Cyprus University of Technology 
National Observatory of Athens 
Funding: The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.
Publication Type: Peer Reviewed
Appears in Collections:EXCELSIOR H2020 Teaming Project Publications

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