Land Cover Classification and Mapping of Philippine Satellite Images Using RAU-Net

Land Cover maps are one of the crucial descriptors in land use planning, management, and development. However, there is a rapid change in land cover due to urbanization and agricultural land development. In this paper, a Residual Attention U-Net Model was trained to classify and map the Land Cover in the Philippines using Satellite Images. The satellite image was given by DOST DATOS - Remote Sensing and Data Science Help Desk which provides free satellite Images for academic use. The image dataset was labeled with eight (8) land cover classes. Data pre-processing and augmentation were used to improve the image and mask dataset. The trained model was evaluated by segmentation metrics namely, Jaccard index and Dice coefficient. After tedious training, the model achieved a performance of $\mathbf{6 2 \%}$ dice coefficient and 45 % jaccard index with a focal loss of 16 %. This shows that the model can predict the majority of the classes however, there are some classes that are predicted incorrectly which is understandable given the performance of the model. A large-scale satellite image was also fed into the model which automatically produced land cover segmentation.