🛰️ Remote Sensing¶

🧠 NeurIPS2025 · 12 paper notes

📌 Same area in other venues: 📷 CVPR2026 (63) · 🔬 ICLR2026 (11) · 🧪 ICML2026 (3) · 🤖 AAAI2026 (7) · 📹 ICCV2025 (11) · 🧪 ICML2025 (7)

🔥 Top topics: Remote Sensing ×2

C3PO: Cross-View Cross-Modality Correspondence by Pointmap Prediction: This paper introduces the C3 dataset comprising 90K ground photo–floor plan pairs (597 scenes, 153M pixel-level correspondences, and 85K camera poses), exposes the limitations of existing correspondence models under cross-view cross-modality settings (e.g., ground photos vs. floor plans), and demonstrates that training on this dataset reduces the RMSE of the best-performing baseline by 34%.
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning: This paper proposes ChA-MAEViT, which enhances cross-channel feature learning for multi-channel images (MCI) through four key components: dynamic channel-patch joint masking, memory tokens, hybrid token fusion, and a channel-aware decoder. The method outperforms the state of the art by an average of 3.0–21.5% across three satellite and microscopy datasets.
Cloud4D: Estimating Cloud Properties at a High Spatial and Temporal Resolution: The first learning framework based on ground-level multi-view cameras that reconstructs four-dimensional (3D spatial + temporal) cloud liquid water content distributions via a homography-guided 2D-to-3D Transformer. The method achieves less than 10% error relative to radar at 25 m spatial and 5 s temporal resolution, improving spatiotemporal resolution by an order of magnitude over satellite observations.
Connecting the Dots: A Machine Learning Dataset for Ionospheric Prediction: This paper constructs an open, ML-ready ionospheric prediction dataset that integrates 8 heterogeneous data sources (solar observations, geomagnetic indices, TEC maps, etc.) spanning approximately 14 years (2010–2024). Three spatiotemporal baseline models—LSTM, SFNO, and GraphCast—are trained on this dataset, achieving TEC forecasts with lead times up to 12 hours.
EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting: This paper proposes EcoCast, a Transformer-based spatio-temporal sequence model that integrates satellite remote sensing (Sentinel-2), climate reanalysis (ERA5), and citizen science observations (GBIF). The model predicts next-month species occurrence probabilities from 12-month environmental feature sequences. On a five-species African bird distribution prediction task, the macro-average F1 score improves from 0.31 (Random Forest) to 0.65. An EWC-based continual learning framework is also designed to accommodate data updates.
GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data: GeoLink directly integrates OpenStreetMap vector data into remote sensing foundation model pretraining by encoding OSM data with a heterogeneous GNN and designing multi-granularity cross-modal learning objectives (region–image-level contrastive + object–patch-level fusion). Pretrained efficiently on 1.27 million sample pairs, GeoLink surpasses existing RS FMs across 7 classification and 4 segmentation/change detection benchmarks.
GreenHyperSpectra: A Multi-Source Hyperspectral Dataset for Global Vegetation Trait Prediction: GreenHyperSpectra constructs a pretraining dataset of 140,000+ multi-source hyperspectral vegetation samples spanning proximal, airborne, and satellite platforms. Label-efficient regression models trained via semi-supervised and self-supervised methods (MAE, GAN, RTM-AE) comprehensively outperform fully supervised baselines on 7 plant trait prediction tasks, with particularly pronounced advantages under label-scarce and out-of-distribution scenarios.
Mass Conservation on Rails – Rethinking Physics-Informed Learning of Ice Flow Vector Fields: This paper proposes a divergence-free neural network (dfNN) that architecturally enforces exact mass conservation (divergence identically zero) via the symplectic gradient of a stream function, and combines it with a directional guidance learning strategy. The approach significantly outperforms soft-constraint PINNs and unconstrained NNs on ice flux interpolation over Antarctica's Byrd Glacier.
OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning: This paper presents OrbitZoo, a multi-agent RL environment built on the industrial-grade astrodynamics library Orekit. It integrates high-fidelity orbital dynamics (including atmospheric drag, solar radiation pressure, and third-body effects), a PettingZoo multi-agent interface, and real-time 3D visualization. Validation against real Starlink ephemerides yields a mean MAPE of only 0.16%.
OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata: OrthoLoC establishes the first large-scale UAV 6-DoF localization benchmark dataset based on orthographic geodata (DOP+DSM), comprising 16,425 real UAV images across 47 regions in Germany and the United States. It further introduces AdHoP (Adaptive Homography Preprocessing), a matching enhancement technique that improves matching performance by 95% and reduces translation error by 63% without modifying the underlying feature matcher.
RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events: This work introduces RSCC — the first large-scale disaster-aware remote sensing change captioning dataset comprising 62,351 pre/post-disaster image pairs with detailed change descriptions, covering 31 global disaster events including earthquakes, floods, and wildfires. High-quality annotations are generated using the QvQ-Max visual reasoning model, and a comprehensive benchmark evaluation framework is established.
Scaling Image Geo-Localization to Continent Level: A hybrid approach combining classification-learned prototypes with aerial image embeddings achieves 68%+ recall@1 within 200 m and 59.2% within 100 m across 433,000 km² of Western Europe — the first system to attain such precision at continental scale.