arXiv Computer Vision
SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping
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arXiv Machine Learning
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Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
arXiv AI
Jul 1
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.
arXiv Machine Learning
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Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery
arXiv:2608. 06406v1 Announce Type: cross Abstract: Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management.
Hugging Face Trending Papers
Jun 28
Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring
Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datasets are predominantly drawn from North America, Europe, Asia, and Australia, leaving a critical gap in African forestry data.
arXiv AI
Jul 14
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.
arXiv Machine Learning
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Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors
The study presents a method for estimating building heights in a large Brazilian city using freely available satellite data, including TerraSAR-X StripMap, PlanetScope, and Sentinel-1. A geographically weighted random forest model achieved an RMSE of 5.34 m and an R² of 0.756 against LiDAR reference data, with local feature importance varying by building type and context. The results highlight that no single sensor dominates across all scenarios, offering guidance for selecting satellite-derived products in different urban settings.