Deep Learning-based Land Coverage Analysis for Pakistan, A Climate Hotspot

Abstract

Pakistan has a unique landscape geographically due to its strategic geo-political im portance. 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 ur ban 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. My research will contribute to developing the dataset from major cities of Pakistan and compare the performance of state-of-the-art semantic segmentation networks to evaluate the dataset. It will help in selecting a highly effective deep learning network and generalizing those networks on my prepared dataset.