Exploring U-Net, FCN, SegNet, PSPNet, Mask R-CNN and Using DeepLabV3+ for Multiclass Semantic Segmentation on Satellite Images of Western Ghats

The Western Ghat (Sahyadri, A mountain range along the western coast of the Indian peninsula) is a hotspot for biodiversity. The region is also subject to land use and land cover (LULC) changes. If areas are to be properly planned and urbanized, then it is important to know what kinds of LULC exist thereon. As the Western Ghats are rich both environmentally and socio-economically, monitoring land cover changes within its domain becomes crucial. This study adopts multiclass semantic segmentation within Western Ghats focusing more on Idukki (Western Ghats) of Kerala. We used high-resolution spatial images obtained from Google satellites through QGIS. The dataset has 1564 pre-processed images and 1564 corresponding masks. We have studied U-Net, FCN, SegNet, PSPNet, Mask R-CNN and DeepLabV3+ and used the DeepLabV3+ Model, a convolutional neural network architecture, to classify the satellite images into four classes: 1. Settlements 2. Forests 3. Water bodies 4. Vegetated area. The segmentation provides insights into the spatial distribution. This research aims at advancing knowledge about LULC dynamics within the Western Ghats. Scope for conservation planning, environmental policy formulation and socio-economic development in the region are possibilities that can also come out as a result from these findings.