• Tejashri Laxmikant Chate
• S. D. Pingle
• Dr. M. M. Dhobe

The Urban Heat Island (UHI) effect, intensified by rapid urbanization and unplanned growth, presents significant challenges for sustainability and climate resilience in Indian cities. This study, conducted as part of an M.Tech. student's final year project, focuses on a 30-year analysis of UHI expansion in Aurangabad, Maharashtra (India), leveraging machine learning and Python-based geospatial tools. Landsat satellite images spanning three decades provide the land surface temperature data essential for detecting urban heat patterns. By employing shapefiles, we delineate precise boundaries within Aurangabad, enabling targeted analysis of temperature distributions within specific areas. Satellite-specific conversion formulas transform digital values to Celsius, providing accurate temperature readings across the dataset. A Tkinter-based graphical user interface (GUI) enables intuitive interaction with the tool, supporting data processing, visualization, and user navigation. Key features include options for loading data, selecting shapefiles, generating Excel temperature reports, and a weather display for historical and current climate information relevant to UHI analysis. Core functions allow users to initiate data processing, perform comparative temperature analysis between locations, and visualize temperature trends across multiple regions in Aurangabad. An image slideshow offers a temporal view of spatial temperature shifts, with a gradient color map to clearly depict heat patterns, where blues represent cooler areas and reds indicate warmer zones. Temperature data can also be exported to Excel, facilitating further analysis or information sharing. Looking forward, this project incorporates machine learning models to predict UHI trends, detect anomalies, and automate regional classifications based on temperature patterns. Potential predictive models could analyze time series data for forecasting, while convolutional neural networks (CNNs) may aid in identifying UHI expansion areas through spatial pattern recognition. This toolkit, combining a user-friendly GUI, robust data processing, and machine learning capabilities, provides a valuable resource for researchers and urban planners dedicated to addressing the pressing issue of urban heat.

"Python-Based Mapping of Urban Heat Island Expansion: A Machine Learning Approach for Time Series and Spatial Gradient Analysis in Aurangabad (MH)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.e695-e698, November-2024, Available :http://www.jetir.org/papers/JETIR2411476.pdf

"Python-Based Mapping of Urban Heat Island Expansion: A Machine Learning Approach for Time Series and Spatial Gradient Analysis in Aurangabad (MH)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppe695-e698, November-2024, Available at : http://www.jetir.org/papers/JETIR2411476.pdf