Deep semantic segmentation for detecting eucalyptus planted forests in the Brazilian territory using sentinel-2 imagery

This research aims to analyze the use of deep semantic segmentation to detect eucalyptus afforestation areas using Sentinel-2 images. The study compared six architectures (U-net, DeepLabv3+, FPN, MANet, PSPNet, LinkNet) with four encoders (ResNet-101, ResNeXt-101, Efficient-net-b3 and Efficient-net-b7), using 10 spectral bands. Even though the differences were not large among the different models, we found that the Efficient-net-b7 was the best backbone among all architectures, and the best overall model was DeepLabv3+ with the Efficient-net-b7 backbone, achieving an IoU of 76.57. Moreover, we compared the mapping of large satellite images with the sliding window technique with overlapping pixels considering six stride values. We found that sliding windows with lower stride values significantly minimized errors in the frame edge both visually and quantitively (metrics). Semantic segmentation allows an evident distinction between the afforestation and the natural vegetation, being fast and efficient for spatial distribution analysis of afforestation changes in Brazil.