MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-16
• Main Authors: Yu, Weikang, Zhang, Xiaokang, Gloaguen, Richard, Xiang Zhu, Xiao, Ghamisi, Pedram
• Format: Article
• Language: English
• Subjects: Annotations; Benchmark; Benchmark testing; Benchmarks; Change detection; Data mining; Datasets; Distribution functions; Earth; Environmental management; Environmental monitoring; Fast Fourier transformations; Feature extraction; Fourier transforms; frequency-domain learning; Graphical models; Image resolution; Imagery; mining change detection; Regulatory compliance; Remote monitoring; Remote sensing; Representations; Sustainable development; Transformers; unified framework
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3491715

Monitoring land changes triggered by mining activities is crucial for industrial control, environmental management, and regulatory compliance, yet it poses significant challenges due to the vast and often remote locations of mining sites. Remote sensing technologies have increasingly become indispensable to detect and analyze these changes over time. We thus introduce MineNetCD, a comprehensive benchmark designed for global mining change detection using remote sensing imagery. The benchmark comprises three key contributions. First, we establish a global mining change detection dataset featuring more than 70k paired patches of bitemporal high-resolution remote sensing images and pixel-level annotations from 100 mining sites worldwide. Second, we develop a novel baseline model based on a change-aware fast Fourier transform (ChangeFFT) module, which enhances various backbones by leveraging essential spectrum components within features in the frequency domain and capturing the channelwise correlation of bitemporal feature differences to learn change-aware representations. Third, we construct a unified change detection (UCD) framework that currently integrates 20 change detection methods. This framework is designed for streamlined and efficient processing, using the cloud platform hosted by HuggingFace. Extensive experiments have been conducted to demonstrate the superiority of the proposed baseline model compared with 19 state-of-the-art change detection approaches. Empirical studies on modularized backbones comprehensively confirm the efficacy of different representation learners on change detection. This benchmark represents significant advancements in the field of remote sensing and change detection, providing a robust resource for future research and applications in global mining monitoring. Dataset and Codes are available via the link.