# ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest

arXiv: https://arxiv.org/abs/2605.15397 · HTML: https://mlsanity.com/paper/2605.15397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 130 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 13 of 13)

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- [MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery](https://mlsanity.com/paper/2311.01676/) (2023)
- [MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing](https://mlsanity.com/paper/2507.08683/) (2025)

## Abstract (arXiv)

Illegal gold mining in the Amazon rainforest causes deforestation, water contamination, and long-term ecosystem disruption, yet remains difficult to monitor at fine spatial scales. Satellite imagery supports large-scale observation, but often misses small mining-related structures and subtle land-cover transitions, especially under frequent cloud cover. We introduce ELDOR, a large-scale UAV benchmark for monitoring environmental and landscape disturbance from illegal gold mining in the rainforest. ELDOR contains manually annotated orthomosaic imagery covering over 2,500 hectares, with pixel-level semantic labels for both mining-related activities and surrounding ecological structures. With this unified annotation source, we establish four benchmark tasks: semantic segmentation, segmentation-derived recognition, direct multi-label classification, and class-presence recognition with vision-language models. Across these tasks, we compare generic and remote-sensing-specific segmentation models, vision foundation model-related segmentation methods, direct multi-label classification methods, and vision-language models under a controlled closed-set protocol. Results show that current methods still struggle with rare small-scale mining structures and fine-grained recovery classes, suggesting the need for context-aware and multimodal modeling. To support domain analysis and practical use, we further build an interactive explorer for domain experts that provides a unified interface for data exploration and model inference.