FLOGA: A machine learning ready dataset, a benchmark and a novel deep
  learning model for burnt area mapping with Sentinel-2
Maria Sdraka Alkinoos Dimakos Alexandros Malounis Zisoula Ntasiou Konstantinos Karantzalos Dimitrios Michail Ioannis Papoutsis
Over the last decade there has been an increasing frequency and intensity of
wildfires across the globe, posing significant threats to human and animal
lives, ecosystems, and socio-economic stability. Therefore urgent action is
required to mitigate their devastating impact and safeguard Earth's natural
resources. Robust Machine Learning methods combined with the abundance of
high-resolution satellite imagery can provide accurate and timely mappings of
the affected area in order to assess the scale of the event, identify the
impacted assets and prioritize and allocate resources effectively for the
proper restoration of the damaged region. In this work, we create and introduce
a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations
for the Greek Area). This dataset is unique as it comprises of satellite
imagery acquired before and after a wildfire event, it contains information
from Sentinel-2 and MODIS modalities with variable spatial and spectral
resolution, and contains a large number of events where the corresponding burnt
area ground truth has been annotated by domain experts. FLOGA covers the wider
region of Greece, which is characterized by a Mediterranean landscape and
climatic conditions. We use FLOGA to provide a thorough comparison of multiple
Machine Learning and Deep Learning algorithms for the automatic extraction of
burnt areas, approached as a change detection task. We also compare the results
to those obtained using standard specialized spectral indices for burnt area
mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our
benchmark results demonstrate the efficacy of the proposed technique in the
automatic extraction of burnt areas, outperforming all other methods in terms
of accuracy and robustness. Our dataset and code are publicly available at:
https://github.com/Orion-AI-Lab/FLOGA.