Articles | Volume 15, issue 2
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-312', Anonymous Referee #1, 03 Oct 2022
    • AC1: 'Reply on RC1', Christian Schulz, 23 Nov 2022
  • RC2: 'Comment on essd-2022-312', Anonymous Referee #2, 27 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Christian Schulz on behalf of the Authors (23 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Jan 2023) by Jia Yang
AR by Christian Schulz on behalf of the Authors (05 Jan 2023)  Manuscript 
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.
Final-revised paper