A revised arXiv paper presents a deep evidential regression approach for estimating forest height using multimodal satellite imagery. The method targets applications including carbon accounting, biodiversity monitoring, and ecosystem management. It aims to give accurate predictions while quantifying uncertainty in sparse-data settings.
A revised arXiv paper proposes a federated learning defense framework for synthetic aperture radar target recognition that adapts dynamically to noise. The approach aims to reduce reliance on centralized training, which raises privacy and bandwidth concerns. It is positioned as a cross-domain replacement for an earlier submission.
Researchers analyzed 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips to study how Robert Smithson's 1970 land artwork Spiral Jetty has repeatedly been submerged and exposed as the Great Salt Lake shrank. The study treats the artwork as a large-scale visual indicator of changing water levels in the lake's north arm. It suggests long-running satellite archives can document climate-driven hydrological shifts at a specific site.
Researchers introduce a multimodal, multiresolution foundation model trained from scratch for lunar remote sensing. It was pretrained on SomBench, a geographically partitioned dataset of roughly two million co-registered tile bundles covering 11 sensor modalities at two spatial resolutions of 1 m/pixel. The work targets general-purpose representation learning for planetary surface analysis.
A new arXiv preprint proposes an API that bridges Earth Observation data cubes, which store imagery as spatio-temporal arrays, and the tabular or tensor formats that machine learning models expect. Built on the openEO standard, the interface aims to replace platform-specific preprocessing steps so the same ML workflow can run across different EO infrastructures. The work targets researchers who want to train and apply models on satellite data without writing custom conversion code for each provider.
Researchers examined whether simulated snowpack conditions from the SNOWPACK model can serve as a data-driven predictor of avalanche activity detected by satellite imagery. The work targets regions where direct field observations are too sparse to support conventional forecasting. It points toward filling observational gaps in mountain avalanche monitoring with model-based estimates.
A new arXiv paper applies hyperbolic geometry to open-world object detection in satellite and aerial imagery. The work targets the fact that remote-sensing object categories carry hidden hierarchical structure, which standard Euclidean embedding spaces struggle to represent. The method is designed to flag unknown objects and incrementally absorb them into the model once annotations become available.
Researchers describe a machine learning approach that classifies sea ice by its stage of development, using labels drawn from several operational ice charts compiled by human analysts. The method also estimates uncertainty, which is relevant for navigation and ice monitoring where chart interpretations can vary.
A new arXiv paper introduces MethaneFuse, a method that learns from multiple public satellite sources to detect methane plumes. Because real-world plume events seldom have fully paired measurements across sensors, the approach is designed to work with complementary spatial, spectral, and atmospheric observations rather than complete multi-sensor coverage. The work targets gaps left by incomplete satellite data in methane monitoring.
A new arXiv paper presents an interpretable approach to fusing hyperspectral and LiDAR imagery for land-cover classification, drawing on heat conduction modeling. The method aims to combine spectral, spatial, and structural information while addressing limits of existing multimodal fusion techniques.
A new arXiv paper proposes SPECTRA, a fine-tuning method for geospatial foundation models that combines band-routed embeddings with stage-wise LoRA. The approach targets cross-sensor adaptation, aiming to let models pretrained on Earth observation, climate and weather data transfer to downstream tasks across different sensor types. The abstract excerpt provided does not detail the reported results.