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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Data for research manuscript Predicting Local Climate Zones using Urban Morphometrics and Satellite Imagery

Authors: Majer, Hugo; Fleischmann, Martin;

Data for research manuscript Predicting Local Climate Zones using Urban Morphometrics and Satellite Imagery

Abstract

This data accompanies a research manuscript “Predicting Local Climate Zones using Urban Morphometrics and Satellite Imagery”. It contains the calculated urban morphometric attributes (momepy.zip), the used preprocessed Sentinel-2 data (imagery.zip), the best-performing rasterized subsets of morphometric attributes used in the fusion-based predictions (rasterized_morphometrics.zip), trained CNN models (s2_cnn_models.zip), and results - model's performance metrics saved in .csv files (results.zip). Original LCZ reference data are available from 2017 IEEE GRSS Data Fusion Contest

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average