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Over the last decade there has been an increasing frequency and intensity ofwildfires across the globe, posing significant threats to human and animallives, ecosystems, and socio-economic stability. Therefore urgent action isrequired to mitigate their devastating impact and safeguard Earth's naturalresources. Robust Machine Learning methods combined with the abundance ofhigh-resolution satellite imagery can provide accurate and timely mappings ofthe affected area in order to assess the scale of the event, identify theimpacted assets and prioritize and allocate resources effectively for theproper restoration of the damaged region. In this work, we create and introducea machine-learning ready dataset we name FLOGA (Forest wiLdfire Observationsfor the Greek Area). This dataset is unique as it comprises of satelliteimagery acquired before and after a wildfire event, it contains informationfrom Sentinel-2 and MODIS modalities with variable spatial and spectralresolution, and contains a large number of events where the corresponding burntarea ground truth has been annotated by domain experts. FLOGA covers the widerregion of Greece, which is characterized by a Mediterranean landscape andclimatic conditions. We use FLOGA to provide a thorough comparison of multipleMachine Learning and Deep Learning algorithms for the automatic extraction ofburnt areas, approached as a change detection task. We also compare the resultsto those obtained using standard specialized spectral indices for burnt areamapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Ourbenchmark results demonstrate the efficacy of the proposed technique in theautomatic extraction of burnt areas, outperforming all other methods in termsof accuracy and robustness. Our dataset and code are publicly available at:https://github.com/Orion-AI-Lab/FLOGA.

FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2