Remote Sensing Terrain Segmentation
The Non-Drivable Area Classification project focuses on the semantic segmentation of satellite or aerial imagery to distinguish non-drivable terrain from drivable paths. Leveraging remote sensing data, the goal is to train machine learning models to identify and classify terrain types that are unsuitable for vehicle navigation, such as: Bodies of water (rivers, lakes) Dense vegetation (forests, jungles) Steep slopes and cliffs Sandy, muddy, or rocky surfaces Urban obstacles (buildings, walls, barriers)