CVPR MultiEarth 2023 Deforestation Estimation Challenge:SpaceVision4Amazon
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Abstract (arXiv)
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.
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References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
- 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
- 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
- 2023MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challengecited by 6
- 2019Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Modelscited by 4
- 2022MultiEarth 2022 Deforestation Challenge -- ForestGumpcited by 3
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