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AUA-Informatics-Lab/eo4wildfires

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@Article{fire7110374,
AUTHOR = {Sykas, Dimitris and Zografakis, Dimitrios and Demestichas, Konstantinos},
TITLE = {Deep Learning Approaches for Wildfire Severity Prediction: A Comparative Study of Image Segmentation Networks and Visual Transformers on the EO4WildFires Dataset},
JOURNAL = {Fire},
VOLUME = {7},
YEAR = {2024},
NUMBER = {11},
ARTICLE-NUMBER = {374},
URL = {https://www.mdpi.com/2571-6255/7/11/374},
ISSN = {2571-6255},
ABSTRACT = {This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1, and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. These data cover 45 countries with a total of 31,730 wildfire events from 2018 to 2022. All of these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test the effectiveness of different deep learning architectures in predicting the size and shape of wildfire-burned areas. This study incorporates both image segmentation networks and visual transformers, employing a consistent experimental design across various models to e

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signal: "permissive"
stated_licence: "cc-by-sa-4.0"
sources: 0