Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO ₂ ) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO ₂ , LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019 to 2022. The study evaluates NO ₂ concentrations vegetation health using indices normalized difference vegetation index, enhanced vegetation index, atmospherically resistant vegetation index, and LST variations. The analysis reveals a notable increase in NO ₂ during both summer and winter, with approximately 0.021 (×10 ³ mol/m ² ) and 0.03 (×10 ³ mol/m ² ) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×10 ³ mol/m ² ) and 0.007 (×10 ³ mol/m ² ) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 °C in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 °C in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO ₂ and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO ₂ and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO ₂ levels and temperatures is critical for policymakers and urban planners. These insights contribute to the sustainable development goal 11, fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.

Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized Difference Built-up Index), and NDWI (Normalized Difference Water Index). The classification models demonstrated high accuracy, with combined values ranging from 96.7% to 98.4%. Our results indicate a significant increase in metropolitan regions, from 608.66 km² (32.43%) in 1994 to 968.50 km² (51.6%) in 2024. This urban expansion correlates with rising land surface temperatures (LST), exacerbating UHI effects. The UTFVI analysis reveals a progressive rise in heat stress from 1994 to 2024. In 1994, most areas ranged from “Good” (0.020), indicating severe urban heat intensification, particularly in central and southern regions providing evidence of a significant increase in urban heat, especially in the center and southern areas of the city. These findings underscore the critical need for sustainable land management strategies to mitigate the adverse effects of Lahore's rapid urbanization on the environment. This study comprehensively explains unplanned urban growth's ecological and climatic consequences. The importance of balanced development in protecting natural ecosystems and enhancing urban resilience is highlighted, offering valuable insights for urban planners and policymakers.

Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This paper suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining Long Short-Term Memory (LSTM) networks with multi-sensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multi-step deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularisation (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multi-sensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.