Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-681-2023
https://doi.org/10.5194/essd-15-681-2023
Data description paper
 | 
08 Feb 2023
Data description paper |  | 08 Feb 2023

TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit

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Cited articles

Ahlswede, S., Thekke-Madam, N., Schulz, C., Kleinschmit, B., and Demir, B.: Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods, in: IEEE International Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia, https://doi.org/10.48550/arXiv.2201.07495, 2022. a
Ansari, M., Homayouni, S., Safari, A., and Niazmardi, S.: A New Convolutional Kernel Classifier for Hyperspectral Image Classification, IEEE J. Sel. Top. Appl., 14, 11240–11256, 2021. a
Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., and Nemani, R.: Deepsat: a learning framework for satellite imagery, in: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, 3–6 November 2015, Seattle, Washington, USA, 1–10, https://doi.org/10.48550/arXiv.1509.03602, 2015. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 1–12, 2018. a
Böckmann, T.: Warum sind Betriebsinventuren für die forstliche Praxis wichtig (Why is two-phase sampling for stratification so important for forestry enterprises?), Forstarchiv, 87, 31–37, 2016. a
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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.