Pervasive development across the country has resulted in unprecedented urbanization with a phenomenal change to the urban landscape. This rapid expansion of cities has generated characteristic local urban climates with an increased land surface temperature (LST) and the intensities of urban heat islands (UHI). The study on LST and UHI can be performed to investigate the spatiotemporal variation in dynamics of land surface temperature and satellite-derived indices for enhanced urban planning and development. The study undertakes a comprehensive assessment for the city of Bangalore to understand its impact on environmental quality. The corresponding satellite-derived indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used to express the Built-up area (BUA) to analyse the dynamics of LST on UHI profiles. The results of the study reveal that the mean LST has a positive correlation for impervious surfaces and a negative correlation with the green surfaces, which is common for all Indian cities and it results in degradation of environmental eminence. The results shows that the area has undergone a drastic transformation and has resulted in a rise in overall temperature and UHI intensity due to an increase of urbanized areas by 3% between 2007 and 2020. The urban policy and planning for new town development is being seen from various aspects such as design and architecture, geography, and sociology, with much of the literature concentrating on the substantive challenges of the development process. However the account of its technical challenges arising from the influential actors and factors are lacking in the planning process of smart cities. This works address focuses on the critical assessment of the spatiotemporal dynamics of land surface temperature and satellite-derived indices as key factors and actors involved in development decisions of New town development and suburbanization planning. The discoveries of the study can be endorsed to monitor the impacts of UHI intensity for future studies and can be used for sustainable urban planning of cities.

Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.