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satellite-imagery

topic4 events
papersTODAY 04:00 UTC

Machine Learning API for Earth Observation Data Cubes Built on openEO

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.

earth-observation/openeo/api/remote-sensing
papersTODAY 04:00 UTC

Deep Evidential Regression Model Estimates Forest Height from Satellite Imagery

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.

remote-sensing/uncertainty/climate/deep-learning
papersTODAY 04:00 UTC

Satellite Imagery of Spiral Jetty Tracks Great Salt Lake Decline

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.

remote-sensing/climate/earth-observation/arxiv
papersTODAY 04:00 UTC

Study Tests Whether SNOWPACK Simulations Can Predict Satellite-Mapped Avalanches

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.

research/remote-sensing/forecasting/simulation