mlsanityData to 7 Oct 2026

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

arXiv 2501.19328 ↗First posted 31 Jan 2025cs.LG cs.AI cs.CVcited by 10

Not on the map. Why?

Abstract (arXiv)

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, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel-1 composite and Sentinel~2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10m resolution temporal canopy height map of the European continent for the period 2019-2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

10 of 35 references.

References · 10

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  2. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  3. 2022A high-resolution canopy height model of the Earthcited by 91
  4. 2023Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidarcited by 60
  5. 2023ViTs for SITS: Vision Transformers for Satellite Image Time Seriescited by 55
  6. 2019Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraintscited by 36
  7. 2024Estimating Canopy Height at Scalecited by 15
  8. 2021How I Learned to Stop Worrying and Love Retrainingcited by 12
  9. 2022High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approachcited by 12
  10. 2023Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heightscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 5.

10 papers.

  1. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 35
  2. 2025DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applicationscited by 4
  3. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  4. 2025SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mappingcited by 1
  5. 2026From Machine Learning to Large-Scale EO Products: Best Practices for Making Mapscited by 1
  6. 2025FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Seriescited by 0
  7. 2026Estimating Individual Tree Height and Species from UAV Imagerycited by 0
  8. 2026Canopy Tree Height Estimation Using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensingcited by 0
  9. 2026Uncertainty-aware tree height change regressioncited by 0
  10. 2026Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Studycited by 0