The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-1/W2-2023
13 Dec 2023
 | 13 Dec 2023


A. Sabir, M. Jameela, and A. W. Malik

Keywords: Climate Change, Satellite Imagery, Semantic Segmentation, Land Coverage, Deep Learning, Computer Vision

Abstract. Pakistan has a unique landscape geographically due to its strategic geo-political importance. It has played a vital role in global climate and politics. There are various semantic segmentation studies performed on remote sensing high-resolution imagery of various urban and rural areas into major classes of buildings, vegetation, water, and roads. These analyses have supported the land coverage study, which can facilitate urban infrastructure management, forestry, disaster management, and climate challenges. Recent climate reports have confirmed the importance of these studies, especially for Pakistan. It’s a critical location for the global south to observe the climate catastrophe. This research will focus on three major cities of Islamabad, Karachi, and Quetta and semantically segment the satellite imagery to study the land coverage. Our research contributes the dataset from major cities of Pakistan and compare the performance of state-of-the-art semantic segmentation networks to evaluate the dataset. Benchmark can help in selecting a highly effective deep learning network and generalizing those networks on our prepared dataset. Dataset can be downloaded from here: