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

TL;DR

This paper introduces MineNetCD, a comprehensive benchmark for global mining change detection using remote sensing imagery, including a large dataset, a novel change-aware model, and a unified framework for improved detection accuracy.

Contribution

The paper presents a new large-scale dataset, a novel ChangeFFT-based baseline model, and a unified change detection framework for remote sensing mining site analysis.

Findings

The ChangeFFT model outperforms 12 state-of-the-art methods.

The dataset contains over 70,000 annotated image pairs from 100 sites.

The unified framework streamlines change detection with high accuracy.

Abstract

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.