Deforestation Detection

Deforestation detection research focuses on accurately identifying forest loss from satellite imagery to support conservation efforts and climate change mitigation. Current approaches leverage various deep learning architectures, including U-Net, Vision Transformers, and recurrent convolutional networks, often incorporating multimodal data (optical and SAR) and advanced techniques like superpixel segmentation and band selection to improve accuracy and efficiency. This research is crucial for timely and reliable monitoring of deforestation, informing policy decisions and providing critical data for assessing the impact of deforestation on global ecosystems. The development of robust and efficient methods is particularly important given the challenges posed by cloud cover, data imbalance, and the vast scale of affected areas.