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cs.CV2026

SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping

Thomas Boudras, Martin Schwartz, Rasmus Fensholt +6

High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods usi…

cs.CV2026

ECHOSAT: Estimating Canopy Height Over Space And Time

Jan Pauls, Karsten Schrödter, Sven Ligensa +7

Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, wh…

cs.CV2025

FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series

Martin Schwartz, Fajwel Fogel, Nikola Besic +10

Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale of individual trees and resulting…

cs.CV2025

DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications

Ibrahim Fayad, Max Zimmer, Martin Schwartz +6

Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-s…

cs.CV2025

High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

Fabien H Wagner, Ricardo Dalagnol, Griffin Carter +19

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and…

cs.CV2024

Open-Canopy: Towards Very High Resolution Forest Monitoring

Fajwel Fogel, Yohann Perron, Nikola Besic +8

Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However,…