Implementation and pretrained models. For details, see * Paper

This paper comprehensively explores state-of-the-art fully convolutional networks such as U-Net, ResU-Net, SegNet, FC-DenseNet, and two DeepLabv3+ variants on monitoring deforestation in the Brazilian Amazon. The networks’ performance is evaluated experimentally in terms of Precision, Recall, F1-score, and computational load using satellite images with different spatial and spectral resolution: Landsat-8 and Sentinel-2. We also include the results of an unprecedented auditing process performed by senior specialists to visually evaluate each deforestation polygon derived from the network with the highest accuracy results for both satellites. This assessment allowed estimation of the accuracy of these networks simulating a process “in nature” and faithful to the PRODES methodology. We conclude that the high resolution of Sentinel-2 images improves the segmentation of deforestation polygons both quantitatively (in terms of F1-score) and qualitatively. Moreover, the study also points to the potential of the operational use of Deep Learning (DL) mapping as products to be consumed in PRODES

We selected a portion of the Amazon forest in Acre and Amazonas states, Brazil, as a study site (see Figure below). This area extends over approximately 12,065 km2, covering around 0.3% of the total Brazilian Amazon forest.

This codebase has been developed with python version 3.6 and CUDA 11.0. The exact arguments to reproduce the models can be found in the arguments file. For training please run:

python run.py