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Title: Open-pit coal mine detection in large-scale remote sensing images

Authors: Qiang Liu; Xiaoliang Yang; Chongyang Wei; Kenan Cheng; Junzheng Wu

Addresses: Northwest Institute of Nuclear Technology, Xi'an, Shaanxi, 710024, China ' Northwest Institute of Nuclear Technology, Xi'an, Shaanxi, 710024, China ' Northwest Institute of Nuclear Technology, Xi'an, Shaanxi, 710024, China ' Northwest Institute of Nuclear Technology, Xi'an, Shaanxi, 710024, China ' Northwest Institute of Nuclear Technology, Xi'an, Shaanxi, 710024, China

Abstract: Satellite remote sensing is crucial for large-scale, regular monitoring of mineral mining. It helps grasp the mining status and fight illegal activities. However, challenges like scarce training datasets, low detection efficiency, fragmented targets, and inaccurate positioning exist. This paper focuses on open-pit coal mines. It presents a saliency-guided image-cutting algorithm and an improved non-maximum suppression-based object-relocation algorithm. These are integrated with a deep-learning object-detection model to form a deep-learning-based mine-detection framework. Tests on 10 large-scale images show the framework achieves 85.22% recall and 45.73% precision efficiently, outperforming pure deep-learning models.

Keywords: coal mine; open-pit mining area; deep learning; saliency detection; detection framework.

DOI: 10.1504/IJICT.2025.148130

International Journal of Information and Communication Technology, 2025 Vol.26 No.31, pp.25 - 39

Received: 14 Apr 2025
Accepted: 11 Jun 2025
Published online: 26 Aug 2025 *