https://rspaper.top/output/feed_uav.xml 2026-05-18T02:08:55Z Latest remote sensing papers (last 7 days) — 2 entries RS-Paper-Hub https://rspaper.top http://arxiv.org/abs/2605.15397v1 2026-05-14T00:00:00Z 2026-05-14T00:00:00Z Kangning Cui Surendra Bohara Suraj Prasai Zishan Shao Wei Tang Martin Pillaca Edwin Flores Zhen Yang Gregory Larsen Evan Dethier David Lutz Jean-Michel Morel Miles Silman Victor Pauca Fan Yang

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.

<p><strong>Publication:</strong> 70 pages, 35 figures</p> <p><strong>Category:</strong> Dataset</p> <p><strong>Tasks:</strong> CLS;SEG</p> http://arxiv.org/abs/2605.15326v1 2026-05-14T00:00:00Z 2026-05-14T00:00:00Z Nitik Jain Mangal Kothari

Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlusion. This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches: (i) experimental evaluation of LiDAR returns through vegetation to assess the feasibility of active sensing, (ii) visible--thermal image fusion using a multi-scale transform and sparse-representation framework to enhance human saliency, and (iii) synthetic-aperture image formation via Airborne Optical Sectioning (AOS) to suppress canopy clutter. A YOLOv5 detector is fine-tuned on the Teledyne FLIR thermal dataset and evaluated on thermal and fused imagery. Results show that the tested terrestrial LiDAR configuration provides limited penetration for object-level detection, while visible--thermal fusion improves target visibility in low-contrast scenes and AOS enhances ground-plane detection in synthetic forest imagery. The fine-tuned YOLOv5 achieves a mean average precision of $\sim$0.83 on the top three FLIR classes. These findings establish an initial baseline for UAV-deployable search-and-rescue and surveillance systems operating in forested environments, and motivate future work on dedicated forest datasets and real-time multimodal integration.

<p><strong>Category:</strong> Method</p> <p><strong>Tasks:</strong> OD</p>