Cross Psuedo Supervision Framework for Sparsely Labelled Geo-spatial Images

Land Use Land Cover (LULC) mapping is essential for urban and resource planning and is one of the key elements in developing smart and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution satellite images with a huge diversity in data distributions in different areas from the country of India. Our approach ensures a robust generalization across different types of buildings, roads, trees, and water bodies within these distinct areas. We propose a modified Cross Pseudo Supervision framework to train image segmentation models on sparsely labelled data. The proposed framework addresses the limitations of the popular "Cross Pseudo Supervision" technique for semi-supervised learning. Specifically, it tackles the challenges of training segmentation models on noisy satellite image data with sparse and inaccurate labels. This comprehensive approach enhances the accuracy and utility of LULC mapping for various urban planning applications.