CVPR MultiEarth 2023 Deforestation Estimation Challenge:SpaceVision4Amazon
This addresses deforestation monitoring for environmental researchers, but it is incremental as it adapts existing architectures to multi-sensor data.
The paper tackles deforestation estimation by developing an attention-guided UNet model using EO and SAR satellite imagery, achieving a test pixel accuracy of 84.70% with an F1-Score of 0.79 and IoU of 0.69.
In this paper, we present a deforestation estimation method based on attention guided UNet architecture using Electro-Optical (EO) and Synthetic Aperture Radar (SAR) satellite imagery. For optical images, Landsat-8 and for SAR imagery, Sentinel-1 data have been used to train and validate the proposed model. Due to the unavailability of temporally and spatially collocated data, individual model has been trained for each sensor. During training time Landsat-8 model achieved training and validation pixel accuracy of 93.45% and Sentinel-2 model achieved 83.87% pixel accuracy. During the test set evaluation, the model achieved pixel accuracy of 84.70% with F1-Score of 0.79 and IoU of 0.69.