  Monitoring changes triggered by mining activities is crucial for industrial
controlling, 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 bi-temporal 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
channel-wise correlation of bi-temporal feature differences to learn
change-aware representations. Third, we construct a unified change detection
(UCD) framework that integrates over 13 advanced change detection models. This
framework is designed for streamlined and efficient processing, utilizing the
cloud platform hosted by HuggingFace. Extensive experiments have been conducted
to demonstrate the superiority of the proposed baseline model compared with 12
state-of-the-art change detection approaches. Empirical studies on modularized
backbones comprehensively confirm the efficacy of different representation
learners on change detection. This contribution 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.
