Rapid Deforestation and Burned Area Detection using Deep Multimodal
  Learning on Satellite Imagery
Gabor Fodor Marcos V. Conde
Deforestation estimation and fire detection in the Amazon forest poses a
significant challenge due to the vast size of the area and the limited
accessibility. However, these are crucial problems that lead to severe
environmental consequences, including climate change, global warming, and
biodiversity loss. To effectively address this problem, multimodal satellite
imagery and remote sensing offer a promising solution for estimating
deforestation and detecting wildfire in the Amazonia region. This research
paper introduces a new curated dataset and a deep learning-based approach to
solve these problems using convolutional neural networks (CNNs) and
comprehensive data processing techniques. Our dataset includes curated images
and diverse channel bands from Sentinel, Landsat, VIIRS, and MODIS satellites.
We design the dataset considering different spatial and temporal resolution
requirements. Our method successfully achieves high-precision deforestation
estimation and burned area detection on unseen images from the region. Our
code, models and dataset are open source:
https://github.com/h2oai/cvpr-multiearth-deforestation-segmentation