Forest Remote Sensing

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Predicting Void Points in Self-Occluding Forest Volumes
Nov 3, 2026Benjamin Bachmayr, Mohamed Youssef, Oliver BimberForest Remote Sensing 3D Occupancy Prediction
Institute of Computer Graphics, Johannes Kepler University Linz, Austria
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Jun 30, 2026Shiyi Chen, Nicholas Saban, Collin Hargreaves +1Automated Data Annotation Interpretable ML
University of California, Berkeley, California, United States.
AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery
Jun 22, 2026Suraj Prasai, Kangning Cui, Rongkun Zhu +5Remote Sensing Image Understanding Remote Sensing
Wake Forest University · Hong Kong Baptist University
deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection
May 19, 2026Ayushi Sharma, Clemens Mosig, Lukas Drees +9Remote Sensing Image Segmentation Remote Sensing
Chair of Sensor-based Geoinformatics, University of Freiburg, Germany · EcoVision Lab, DM3L, University of Zurich, Switzerland · Institute for Earth System Science and Remote Sensing, Leipzig University, Germany +1
Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction
May 11, 2026Quanyun Wu, Kyle Gao, Wentao Sun +6Tree Species Classification Remote Sensing
University of Waterloo · Waterloo, Canada · University of Calgary +1
Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES
Apr 27, 2026Matti Hyyppä, Klaara Salolahti, Eric Hyyppä +9Point Cloud Classification Tree Species Classification
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland, Vuorimiehentie 5, Espoo, FI-02150, Finland
A multi-platform LiDAR dataset for standardized forest inventory measurement at long term ecological monitoring sites
Apr 16, 2026Michael R. Chang, Anna Candotti, Karl von Ellenrieder +2Remote Sensing Forest Remote Sensing
Faculty of Engineering, Free University of Bozen-Bolzano, via Bruno Buozzi 1, Bolzano, 39100, BZ, Italy · Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, piazza Università, 5, Bolzano, 39100, BZ, Italy · Competence Centre for Mountain Innovation Ecosystems, Free University of Bozen-Bolzano +1
SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping
Dec 19, 2025Thomas Boudras, Martin Schwartz, Rasmus Fensholt +6Remote Sensing Image Super-Resolution Remote Sensing
Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, CNRS, UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France · Department of Geography and Geology, University of Copenhagen, Øster Voldgade 10, Copenhagen, DK-1350, Denmark · Kayros SAS, Paris, 75009, France +2

Nov 3, 2026J/K move · Enter open · S save

Predicting Void Points in Self-Occluding Forest Volumes

Benjamin Bachmayr, Mohamed Youssef, Oliver Bimber

Institute of Computer Graphics, Johannes Kepler University Linz, Austria

Forest Remote Sensing 3D Occupancy Prediction 3D Reconstruction Remote Sensing

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Synthetic Aperture Imaging (SAI) enables drones to observe forest canopies by warping and averaging multiple displaced views at a common depth. However, occlusion is suppressed rather than eliminated, causing poorly observed regions to appear occupied and leading to systematic biomass overestimation. The Deep Forest project cannot distinguish reconstruction artifacts from real vegetation because its training data relies on measured reflectance points, excluding empty space and preventing the network from learning explicit occupancy boundaries. This thesis addresses the problem by modifying training labels rather than the imaging process. Each simulated scene is rendered twice: as a 9x9 grid of top-down captures for focal-stack generation and as 550 depth-clipped slabs derived from scene geometry to provide occupancy labels, explicitly representing void space as a negative class. A Gazebo pipeline generates procedural forest stands containing 80-200 trees across three species. A pretrained Pix2Pix model translates rendered RGB images into a synthetic near-infrared band, and Light Field Rendering produces aligned focal stacks of 550 planes across three channels. Four architectures were evaluated on a held-out scene. The plain 2D U-Net achieved an IoU of 0.6290, outperforming the volumetric 3D U-Net (0.5707). Established reconstruction methods performed substantially worse: COLMAP achieved 0.3446 and DepthAnything3 0.2959, both after their respective optimal dilation sweeps. An RGB-input variant of the volumetric architecture achieved the highest IoU of 0.6514 without dilation or per-scene calibration. These results highlight the limitations of conventional reconstruction methods, which recover visible surfaces but struggle to infer occupancy within closed canopies.