activity
20222026
most citedVision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights

86 citations · 100 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Rapid Loss of the Sierra Nevada's Largest Trees Driven by Fire

Fabien H. Wagner, Dan J. Dixon, Christopher W. Woodall +15

Large trees disproportionately contribute to biomass storage, habitat structure, and ecosystem functioning. However, their distribution and health dynamics remain poorly quantified…

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

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.LG2025

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…

eess.IV2024

Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping

Adugna Mullissa, Sassan Saatchi

Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need t…

cs.CV2024★ 4 cited

Estimating Canopy Height at Scale

Jan Pauls, Max Zimmer, Una M. Kelly +6

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss functi…

cs.CV2024★ 1 cited

Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction

Fabien H Wagner, Samuel Favrichon, Ricardo Dalagnol +3

The Amazon, the world's largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reaches its lowest level in a century i…

cs.CV2023★ 4 cited

Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model

Fabien H Wagner, Sophia Roberts, Alison L Ritz +7

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

cs.CV2023★ 86 cited

Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights

Ibrahim Fayad, Philippe Ciais, Martin Schwartz +8

Accurate and timely monitoring of forest canopy heights is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestatio…

astro-ph.EP2022★ 5 cited

Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021

Fabien H Wagner, Ricardo Dalagnol, Celso HL Silva-Junior +5

Monitoring changes in tree cover for rapid assessment of deforestation is considered the critical component of any climate mitigation policy for reducing carbon. Here, we map tropi…

cs.CV2022

K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation

Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez +6

Deep learning self-supervised algorithms that can segment an image in a fixed number of hard labels such as the k-means algorithm and relying only on deep learning techniques are s…