Work

Remote sensing monitoring of open-pit mine's year-to-year change in the earthwork volume using AI data reconstruction

Year: 2026

Type: article

Abstract: Reconstructing the 3D landform directly from planar RGB imagery enables substantial cost savings in related downstream applications, such as estimate of the yearly earthwork volume. We proposed a novel deep learning algorithm, termed P_DEMNet and an effective post-processing workflow in this study. Our innovations focus on: (1) a just-right integration of deep supervision, fractal characteristics and estimate of the uncertainties; and (2), development of mining-GIS-remote sensing joint rulesets. Our estimation over Yichun is 27,764,507 ± 1,932.3011 m3 (from 1998 to 2014) and 530,676,971 ± 6,319.411 m3 (from 2014 to 2024), and the yearly yield of metal (Li) in the Yifeng mine is at least 2410.2 tons (±51.5), and the Yichun Mine (open-pit parts)'s contribution of Li is 172.30 tons (±0.51), of Nb is 4.59 tons (±0.013) and of Ta is 7.72 tons (±0.023). A series of comparative/ablation experiments have demonstrated its advantages over other state-of-the-art modules with nontrivial improvement.

Source: Geocarto International

DOI: 10.1080/10106049.2026.2614787

Authors: Bo Zhao, Qishen Chen, Dehui Zhang, Kun Wang, Yanfei Zhang, Anbo Zheng

Institutions: Chinese Academy of Geological Sciences, China University of Geosciences, China University of Geosciences (Beijing), Southwest Jiaotong University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,390

Funders: National Natural Science Foundation of China, China Geological Survey, Chinese Academy of Geological Sciences