Evidence-based Inference and Quantification of Urban Expansion Using YOLOv8 and High-Resolution Satellite Imagery

Authors

  • Deepika Pahuja Amity Institute of Information Technology, Amity University, India
  • Sarika Jain Amity Institute of Information Technology, Amity University, India
  • Shishir Kumar School of Information Science and Technology, Babasaheb Bhimrao Ambedkar University, India
Volume: 15 | Issue: 5 | Pages: 26626-26631 | October 2025 | https://doi.org/10.48084/etasr.12235

Received: 19 May 2025 | Revised: 26 June 2025 and 9 July 2025 | Accepted: 11 July 2025 | Online: 2 August 2025


Corresponding author: Deepika Pahuja

Abstract

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.

Keywords:

urban expansion, LULC, Yolov8, Google Earth Pro, satellite imagery, deep learning

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How to Cite

[1]
D. Pahuja, S. Jain, and S. Kumar, “Evidence-based Inference and Quantification of Urban Expansion Using YOLOv8 and High-Resolution Satellite Imagery”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 26626–26631, Oct. 2025.

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