activity
20242026
collaborators

7 papers

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.LG2026

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

Jan Pauls, Max Zimmer, Berkant Turan +4

With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, an…

cs.CV2025

FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring

Martin Schwartz, Fajwel Fogel, Nikola Besic +9

The recent decline of the European forest carbon sink highlights the need for spatially explicit and frequently updated forest monitoring tools. Yet, existing satellite-based distu…

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…