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Copy file name to clipboardExpand all lines: DOCS/Urban_Atlas/CLMS_UA2021_LULC_PUM_v1.2.qmd
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title: "Urban Atlas 2021 and 2024 – Product User Manual"
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subtitle: "Copernicus Land Monitoring Service – Priority Area Monitoring"
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title: "Urban Atlas 2021 – Product User Manual"
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subtitle: "Copernicus Land Monitoring Service – Priority Area Monitoring – Urban Atlas 2021 and 2024"
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date: "05/02/2026"
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version: 1.2
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Researchers used Urban Atlas to map green spaces and analyse their accessibility using pedestrian street networks. This data helped city managers ensure that residents, regardless of income, have access to high-quality green spaces. The study highlighted the importance of differentiating green urban areas according to their public accessibility, as easy and free access to these areas is crucial for urban ecology and quality of life.
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{#fig-figure1}
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## Use case 2: National mapping of Local Climate Zones (LCZ) to help communities diagnose urban overheating in France
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In this type of study, the advantage of the Copernicus Urban Atlas product is that it provides free and consistent data for local authorities.
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# Product description
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- Building Block Height 2021 (product described in a separate Product User Manual)
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## Product characteristics
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- To maintain continuity of linear structures, they can be mapped smaller than 10 m over a distance up to 50 m (see figure below).
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Priority mapping rules for areas smaller than the MMU are used:
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To characterize the appearance of a real change between two images (in our case, two VHR mosaic, 2018-2021), CLS (Collecte Localisation Satellite) propose a method based on the **Principal Component Analysis (PCA)** which is a statistical method for dimensionality reduction that preserves the inherent structure of data. PCA transforms correlated variables into uncorrelated principal components, which are linear combinations of the original variables. These components are ordered by their ability to explain data variance, enabling the separation of significant changes from other variations. PCA involves computing the eigenvalues and eigenvectors of the covariance matrix derived from the difference image. By retaining only the principal component with the largest eigenvalue, PCA reduces sensor-induced noise and extracts the most significant change information, aiding in the generation of change maps. The @fig-figure5 is showing an example of a change detected between 2018 and 2021.
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### Change detection based on Sentinel-2
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See the latest version of the ATBD, for full details on RMSD computation and normalization.
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### Production of a layer of change candidates
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After obtaining the heatmaps derived from S2 (Sentinel-2) data and those derived from VHR data, it is necessary to combine both into a single layer that will constitute the change candidates. This process includes a comparison of the two detection bases, followed by a rule-based cleaning procedure and alignment with the minimum surface specifications of the LC/LU product.
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@fig-figure8 below shows the result of the labelling process and the final classification of GUAs into public (blue), private (red) and unknown (purple) in the FUA of Iasi, Romania. Additionally, it also shows class 14200 - recreational and sport areas (green).
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The reader is referred to the product’s ATBD for full details on Green Urban Areas classification. A detailed list of OSM data tags used to indicate public and private accessibility is included in Annex 1 at the end of the ATBD.
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