Forest and Water Bodies Segmentation Through Satellite Images Using U-Net
This work addresses environmental monitoring for climate change mitigation, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of monitoring deforestation and flooding by segmenting forest and water bodies from satellite images using a U-Net model, achieving validation accuracies of 82.55% for forest and 82.92% for water segmentation.
Global environment monitoring is a task that requires additional attention in the contemporary rapid climate change environment. This includes monitoring the rate of deforestation and areas affected by flooding. Satellite imaging has greatly helped monitor the earth, and deep learning techniques have helped to automate this monitoring process. This paper proposes a solution for observing the area covered by the forest and water. To achieve this task UNet model has been proposed, which is an image segmentation model. The model achieved a validation accuracy of 82.55% and 82.92% for the segmentation of areas covered by forest and water, respectively.