Evidence-based Inference and Quantification of Urban Expansion Using YOLOv8 and High-Resolution Satellite Imagery - Pahuja, Deepika; Jain, Sarika; Kumar, Shishir (Engineering, Technology & Applied Science Research, 2025)
Urban expansion significantly impacts land use and ecological balance, necessitating efficient monitoring tools. This study introduces a deep learning-based approach for detecting urban growth in Greater Faridabad, Haryana, India, using high-resolution satellite imagery and the YOLOv8 object detection model. A custom dataset was created from Google Earth Pro imagery (2014-2024) and manually annotated to include key urban features such as buildings, roads, and construction areas. Images from 2015, 2020, and 2025 supported a temporal assessment of urbanisation trends. The YOLOv8m model exhibited strong performance, achieving a mean Average Precision (mAP) of 0.983 at IoU 0.5, 0.808 for mAP50–95, with 94.6% precision and 97.9% recall, indicating high detection accuracy with minimal false positives and negatives. Urban area grew from 26,837 px² in 2014 to 106,330 px² in 2020 (a 296% increase), and to 147,450 px² by 2025 - an additional 39%, resulting in a cumulative increase of 450%. These results reflect rapid urbanisation in the study area. The proposed method offers a reliable, scalable solution for automated urban change detection, supporting data-driven urban planning and sustainable development.