  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.
