ArticlePNAS nexus2023
Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.
Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- High-resolution UAV imagery-based instance segmentation for automatic counting of rubber trees across phenological seasons.Plant phenomics (Washington, D.C.) · 2026Article
- Toward Consistent Canopy Characterization From Low-Cost UAV Imagery.Ecology and evolution · 2026Article
- From Detection to Maps: A Review of Automated Urban Tree Mapping Using UAV and High-Resolution Satellite Data.Journal of imaging · 2026Review
- BCAR-Net: A Bidirectional Cross-Attention Network with Auxiliary Reconstruction for Tree Counting in Complex Forest Scenes Using Airborne RGB and LiDAR Data.Plants (Basel, Switzerland) · 2026Article
- An Open and Transferable Deep Learning Framework for Mapping Urban Tree Canopy Using NAIP Imagery.Remote sensing · 2026Article
- Contemporary high resolution European forest structure assessed using tree-level National Forest Inventory data.PloS one · 2026Article
- OliveTreeCrownsDb: A high-resolution UAV dataset for detection and segmentation in agricultural computer vision.Data in brief · 2025Article
- A Tree Crown Segmentation Approach for Unmanned Aerial Vehicle Remote Sensing Images on Field Programmable Gate Array (FPGA) Neural Network Accelerator.Sensors (Basel, Switzerland) · 2025Article
- Article
- Scattered tree death contributes to substantial forest loss in California.Nature communications · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.