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Visualization of Cologne Tree Data. As of now, this is work in progress. [Page code on github]
This visualization uses heavily modified and enriched data derived from the official "Baumkataster" tree inventory datasets for 2017 and 2020, published by Stadt Köln under Creative Commons Namensnennung 3.0 DE in https://offenedaten-koeln.de/dataset/baumkataster-koeln
Please refer to https://zushicat.github.io/cologne-trees-static-API and https://github.com/zushicat/cologne-trees-data for detailled information about the data enrichment process and the accumulation of the underlaying data.
Both Cologne "Baumkataster" tree inventory datasets are obviously far from being a complete depiction of the tree distribution throughout the city.
For a quick illustration, please change to "satellite" display in the map options, zoom in at any point and compare the rendered tree datapoints with trees that you can identify on the satelite image. (The satellite images are not necessarily up to date. Please refer to this mapbox documentation.)
Overcoming the dataset limitations
A separate machine learning project for object detection on orthographic images, or more specificially: tree crown detection on urban landscapes, (based on this model https://deepforest.readthedocs.io) is in developement right now and should be publicy available soon.
So far, this method of tree detection showed great initial results and will be a tremendous step forward to depict the real situation of tree distribution throughout the city.
You can find the implementation of the tree crown detection in this repository: https://github.com/zushicat/tree-crown-detection
Any results (resp. the repository link) regarding a Cologne specific analysis will be published as soon as possible.
Please keep in mind that this visualization as of now only reflects the tree distribution as derived from the official inventories, hence it shows what is counted and not necessarily what is real.