Analysing Urban Environmental Dynamics through Multi-Sensor Remote Sensing and Deep Learning: An Integrated Classification-Regression Framework

초록

Urban centers worldwide are facing changes in terms of environmental standards and an increase in temperatures. Recent significant developments, particularly in developing nations, have led to an increase in Land Surface Temperature (LST) and surface thermal variations in urban areas, accompanied by rising levels of atmospheric pollutants such as nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO2), and aerosols. Despite growing concerns about urban warming and air pollution, very few studies have simultaneously integrated multisource atmospheric pollutants, LST, precipitation, and high-resolution Land Use/Land Cover (LULC) dynamics into an analytical workflow using two independently trained Deep Learning Neural Network (DLNN) models, particularly for rapidly growing cities in developing countries. The novelty of this study lies in two separate DLNN architectures—one optimized for LULC classification using PlanetScope multispectral imagery and another for LST estimation using atmospheric pollutant concentrations—and their outputs were synthesized into a cohesive analytical workflow. This dual-model approach, combined with multi-sensor data integration, enables a comprehensive assessment of the relationships between urban expansion, atmospheric pollution, and thermal environmental changes. The LULC change detection analysis reveals a net increase of 8.34% in urban built-up areas, coupled with increases in atmospheric pollutants and LST. This study utilizes high-resolution multispectral remote sensing data from PlanetScope and atmospheric pollutant concentration data from Sentinel-5P TROPOMI, along with LST data from MODIS, making it a multi-sensor and multi-parametric remote sensing approach to analyze urban environmental changes in Patna city, Bihar, India. The findings provide useful evidence to support urban environmental monitoring and planning decisions in Patna. Copyright © 2026 Korean Society of Remote Sensing. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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