# Open-Canopy: Towards Very High Resolution Forest Monitoring

arXiv: https://arxiv.org/abs/2407.09392 · HTML: https://mlsanity.com/paper/2407.09392/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 75 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar](https://mlsanity.com/paper/2304.07213/) (2023)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model](https://mlsanity.com/paper/2306.01936/) (2023)
- [Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans](https://mlsanity.com/paper/2204.11620/) (2022)

## Built on by (most cited first, 16 of 16)

- [Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation](https://mlsanity.com/paper/2408.04523/) (2024)
- [From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion](https://mlsanity.com/paper/2507.09081/) (2025)
- [Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas](https://mlsanity.com/paper/2411.14354/) (2024)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [CHMv2: Improvements in Global Canopy Height Mapping using DINOv3](https://mlsanity.com/paper/2603.06382/) (2026)
- [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://mlsanity.com/paper/2508.10894/) (2025)
- [Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using Airborne LiDAR HD Reference Data across Metropolitan France](https://mlsanity.com/paper/2512.11524/) (2025)
- [SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping](https://mlsanity.com/paper/2512.18128/) (2025)
- [EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?](https://mlsanity.com/paper/2511.21523/) (2025)
- [FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series](https://mlsanity.com/paper/2512.17021/) (2025)
- [AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture](https://mlsanity.com/paper/2605.22366/) (2026)
- [Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin](https://mlsanity.com/paper/2606.05368/) (2026)
- [Counting Trees from Satellite Imagery with Noisy Supervision](https://mlsanity.com/paper/2606.24786/) (2026)
- [Uncertainty-aware tree height change regression](https://mlsanity.com/paper/2607.00638/) (2026)
- [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026)
- [Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Study](https://mlsanity.com/paper/2609.37809/) (2026)

## Abstract (arXiv)

Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km2 across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-Delta, a benchmark for canopy height change detection between images from different years at tree level-a challenging task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.