Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-681-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-681-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Feb 2023
Data description paper |
| 08 Feb 2023
| 08 Feb 2023
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
Steve Ahlswede
CORRESPONDING AUTHOR
Remote Sensing Image Analysis Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
Christian Schulz
CORRESPONDING AUTHOR
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Christiano Gava
Smart Data and Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Patrick Helber
Vision Impulse GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Benjamin Bischke
Vision Impulse GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Michael Förster
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Florencia Arias
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Jörn Hees
Smart Data and Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Fachbereich Informatik, Hochschule Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany
Begüm Demir
Remote Sensing Image Analysis Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
Birgit Kleinschmit
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
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Cited
21 citations as recorded by crossref.
- Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review A. Saim & M. Aly 10.3390/wild2010007
- Deep Active Learning for Multi-Label Classification of Remote Sensing Images L. Möllenbrok et al. 10.1109/LGRS.2023.3305647
- Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe C. Schulz et al. 10.1016/j.rse.2024.114162
- A Sentinel-2 machine learning dataset for tree species classification in Germany M. Freudenberg et al. 10.5194/essd-17-351-2025
- Status, advancements and prospects of deep learning methods applied in forest studies T. Yun et al. 10.1016/j.jag.2024.103938
- Map of forest tree species for Poland based on Sentinel-2 data E. Grabska-Szwagrzyk et al. 10.5194/essd-16-2877-2024
- GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022 Z. Yang et al. 10.5194/essd-16-2407-2024
- Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al. 10.1109/JSTARS.2024.3361556
- Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning S. Rust & B. Stoinski 10.3390/f15010188
- VHRTrees: a new benchmark dataset for tree detection in satellite imagery and performance evaluation with YOLO-based models Ş. Topgül et al. 10.3389/ffgc.2024.1495544
- Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning M. Beloiu et al. 10.3390/rs15051463
- Urban tree species classification using multisource satellite remote sensing data and street view imagery S. Wang et al. 10.1080/10095020.2024.2439380
- A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods L. Zhong et al. 10.3390/f15050852
- A Multi-Scale Convolution and Multi-Layer Fusion Network for Remote Sensing Forest Tree Species Recognition J. Hou et al. 10.3390/rs15194732
- An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series S. Hauser et al. 10.3390/rs16030488
- A Review of Individual Tree Crown Detection and Delineation From Optical Remote Sensing Images: Current progress and future J. Zheng et al. 10.1109/MGRS.2024.3479871
- Evaluating the effects of texture features on Pinus sylvestris classification using high-resolution aerial imagery F. Erdem & O. Bayrak 10.1016/j.ecoinf.2023.102389
- Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning S. Ecke et al. 10.1016/j.compag.2024.108785
- Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning W. Zhang et al. 10.3390/rs16142547
- National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data L. Blickensdörfer et al. 10.1016/j.rse.2024.114069
- Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty D. Joshi & C. Witharana 10.3390/rs17061066
21 citations as recorded by crossref.
- Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review A. Saim & M. Aly 10.3390/wild2010007
- Deep Active Learning for Multi-Label Classification of Remote Sensing Images L. Möllenbrok et al. 10.1109/LGRS.2023.3305647
- Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe C. Schulz et al. 10.1016/j.rse.2024.114162
- A Sentinel-2 machine learning dataset for tree species classification in Germany M. Freudenberg et al. 10.5194/essd-17-351-2025
- Status, advancements and prospects of deep learning methods applied in forest studies T. Yun et al. 10.1016/j.jag.2024.103938
- Map of forest tree species for Poland based on Sentinel-2 data E. Grabska-Szwagrzyk et al. 10.5194/essd-16-2877-2024
- GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022 Z. Yang et al. 10.5194/essd-16-2407-2024
- Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al. 10.1109/JSTARS.2024.3361556
- Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning S. Rust & B. Stoinski 10.3390/f15010188
- VHRTrees: a new benchmark dataset for tree detection in satellite imagery and performance evaluation with YOLO-based models Ş. Topgül et al. 10.3389/ffgc.2024.1495544
- Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning M. Beloiu et al. 10.3390/rs15051463
- Urban tree species classification using multisource satellite remote sensing data and street view imagery S. Wang et al. 10.1080/10095020.2024.2439380
- A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods L. Zhong et al. 10.3390/f15050852
- A Multi-Scale Convolution and Multi-Layer Fusion Network for Remote Sensing Forest Tree Species Recognition J. Hou et al. 10.3390/rs15194732
- An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series S. Hauser et al. 10.3390/rs16030488
- A Review of Individual Tree Crown Detection and Delineation From Optical Remote Sensing Images: Current progress and future J. Zheng et al. 10.1109/MGRS.2024.3479871
- Evaluating the effects of texture features on Pinus sylvestris classification using high-resolution aerial imagery F. Erdem & O. Bayrak 10.1016/j.ecoinf.2023.102389
- Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning S. Ecke et al. 10.1016/j.compag.2024.108785
- Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning W. Zhang et al. 10.3390/rs16142547
- National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data L. Blickensdörfer et al. 10.1016/j.rse.2024.114069
- Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty D. Joshi & C. Witharana 10.3390/rs17061066
Latest update: 08 May 2025
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Imagery from air and space is the primary source of large-scale forest mapping. Our study...
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