EuroMineNet: A Multitemporal Sentinel-2 Benchmark for Spatiotemporal Mining Footprint Analysis in the European Union (2015-2024)

2025-10-16

AI Summary

Mining activities drive economic growth but cause severe environmental degradation—including deforestation and soil/water contamination—necessitating long-term, large-scale surface change monitoring. To address critical gaps in temporal depth and geographic coverage, this work introduces the first multi-temporal remote sensing benchmark dataset spanning 133 mining sites across the European Union from 2015 to 2024. We propose the Change-Aware Intersection-over-Union (CA-TIoU) metric, enabling unified, interpretable quantification of both gradual and abrupt surface changes. Leveraging Sentinel-2 imagery and expert-validated annotations, we systematically evaluate 20 GeoAI models, revealing superior performance of long-sequence change modeling while identifying limitations in short-term dynamic detection. All code and data are released openly under FAIR principles, advancing intelligent, sustainable resource management through transparent, reproducible Earth observation analytics.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Mining activities are essential for industrial and economic development, but remain a leading source of environmental degradation, contributing to deforestation, soil erosion, and water contamination. Sustainable resource management and environmental governance require consistent, long-term monitoring of mining-induced land surface changes, yet existing datasets are often limited in temporal depth or geographic scope. To address this gap, we present EuroMineNet, the first comprehensive multitemporal benchmark for mining footprint mapping and monitoring based on Sentinel-2 multispectral imagery. Spanning 133 mining sites across the European Union, EuroMineNet provides annual observations and expert-verified annotations from 2015 to 2024, enabling GeoAI-based models to analyze environmental dynamics at a continental scale. It supports two sustainability-driven tasks: (1) multitemporal mining footprint mapping for consistent annual land-use delineation, evaluated with a novel Change-Aware Temporal IoU (CA-TIoU) metric, and (2) cross-temporal change detection to capture both gradual and abrupt surface transformations. Benchmarking 20 state-of-the-art deep learning models reveals that while GeoAI methods effectively identify long-term environmental changes, challenges remain in detecting short-term dynamics critical for timely mitigation. By advancing temporally consistent and explainable mining monitoring, EuroMineNet contributes to sustainable land-use management, environmental resilience, and the broader goal of applying GeoAI for social and environmental good. We release the codes and datasets by aligning with FAIR and the open science paradigm at https://github.com/EricYu97/EuroMineNet.

Problem

• Mining activities cause environmental degradation like deforestation and pollution
• Existing datasets lack temporal depth for monitoring mining land changes
• EuroMineNet enables continental-scale mining footprint mapping and change detection

Innovation

• Sentinel-2 multispectral imagery for mining footprint mapping
• Annual expert-verified annotations from 2015 to 2024
• Novel Change-Aware Temporal IoU metric for evaluation