Tree canopy map of Lausanne at the 1m resolution obtained with DetecTree [1] from SWISSIMAGE 2016. Citation If you use this dataset, the source, i.e., SWISSIMAGE 2016 must be acknowledged. Additionally, a citation to DetecTree would certainly be appreciated. Note that DetecTree is based on the methods of Yang et al. [2], therefore it seems fair to reference their work too. An example citation in an academic paper might read as follows: The tree canopy dataset for the agglomeration of Lausanne has been obtained from the SWISSIMAGE 2016 aerial imagery dataset with the Python library DetecTree (Bosch, 2020), which is based on the approach of Yang et al. (2009). Technical specifications Source: SWISSIMAGE 2016 CRS: CH1903+/LV95 – Swiss CH1903+/LV95 (EPSG:2056) Resolution: 1m Extent: From file agglom-extent.shp. Obtained with the Urban footprinter. See the lausanne-agglom-extent repository for more details. Method: supervised learning (AdaBoost) with 4 classifiers on manually-generated ground truth masks for 7 training tiles (out of a total 499 tiles) of 512x512 pixels. See Yang et al. [2] for more details. Accuracy: 91.75%, estimated from a manually-generated ground truth mask for 1 tile of 512x512 pixels. Acknowledgements With the support of the École Polytechnique Fédérale de Lausanne (EPFL) References Bosch, M. (2020). Detectree: Tree detection from aerial imagery in Python. Journal of Open Source Software (under review). Yang, L., Wu, X., Praun, E., & Ma, X. (2009). Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 131-137). ACM.