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Vergleich LULC-Datensätze in der Schweiz

Quellenverzeichnis

Folgend sind in alle Quellen für diese Arbeit in mehrere Kategorien unterteilt und aufgelistet.

In dieser Arbeit wurde künstliche Intelligenz (KI), namentlich ChatGPT, zur Generierung einzelner Programmier-Codes sowie zur Überarbeitung von einzelnen Textabschnitten verwendet. Keine Sätze oder Abschnitte in der GitHub-Page wurden jedoch eins zu eins von der KI geschrieben und kopiert.

Internetquellen und Literatur

Quellen Zitiert nach APA 7 Style:

Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C., Batista E Silva, F., Brown, C., Butler, A., Calvin, K., Dendoncker, N., Doelman, J. C., Dunford, R., Engström, K., Eitelberg, D., Fujimori, S., Harrison, P. A., Hasegawa, T., Havlik, P., Holzhauer, S., … Rounsevell, M. D. A. (2017). Assessing uncertainties in land cover projections. Global Change Biology, 23(2), 767–781. https://doi.org/10.1111/gcb.13447

Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data (Professional Paper) [Professional Paper]. U.S. Geological Survey (USGS).

Beyeler, A., Gillard, M., Willi-Tobler, L., Hennig, E., Milani, G., & Douard, R. (2023, September). Einsatz von künstlicher Intelligenz in der Arealstatistik des BFS (No. 27205444; p. 8). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/27205444/master

BFS, B. für S. (2006). Arealstatistik – Standardnomenklatur NOAS04 (No. 205795; p. 1). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/205795/master

BFS, B. für S. (2013). Arealstatistik nach Nomenklatur 2004—Bodenbedeckung (No. 20185285; p. 4). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/20185285/master

BFS, B. für S. (2015, January 6). Arealstatistik nach Nomenklatur 2004—Bodennutzung (No. 256981; p. 4). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/256981/master

BFS, B. für S. (2018). Nomenklatur Arealstatistik. Bundesamt für Statistik (BFS). https://www.bfs.admin.ch/bfs/de/home/statistiken/raum-umwelt/nomenklaturen/arealstatistik/noas2004.assetdetail.6948898.html

BFS, B. für S. (2022, September). Arealstatistik—Berechnung von Veränderungen (No. 23328077; p. 6). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/23328077/master

BFS, B. für S. (2024a). Metadaten Arealstatistik. Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/32376220/appendix

BFS, B. für S. (2024b, August). Arealstatistik: Beschreibung der Geodaten (No. 32376295; p. 8). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/32376295/master

Black, B. (2022). Combining filter and embedded approaches to improve variable selection in land use change Cellular Automata models using Random Forests [Application/pdf]. 25 slides. https://doi.org/10.3929/ETHZ-B-000593547

Black, B., Adde, A., Farinotti, D., Guisan, A., Külling, N., Kurmann, M., Martin, C., Mayer, P., Rabe, S.-E., Streit, J., Zekollari, H., & Grêt-Regamey, A. (2024). Broadening the horizon in land use change modelling: Normative scenarios for nature positive futures in Switzerland. Regional Environmental Change, 24(3), 115. https://doi.org/10.1007/s10113-024-02261-0

Black, B., Van Strien, M. J., Adde, A., & Grêt-Regamey, A. (2023). Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions. Environmental Modelling & Software, 159, 105574. https://doi.org/10.1016/j.envsoft.2022.105574

Bossard, M., Feranec, J., & Otahel, J. (2000). CORINE land cover technical guide – addendum 2000 (EEA Technical Report No. 40). European Environment Agency (EEA). https://www.eea.europa.eu/publications/tech40add/at_download/file

Brown, C. F., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., Lahera, E., Wiles, O., Ilyushchenko, S., Gorelick, N., Zhang, L. L., Alj, S., Schechter, E., Askay, S., Guinan, O., Moore, R., Boukouvalas, A., & Kohli, P. (2025). Land Cover Classification System (LCCS): Classification Concepts and User Manual (No. arXiv:2507.22291). arXiv. https://doi.org/10.48550/arXiv.2507.22291

Bruin, S. de, Bregt, A., & Ven, M. van de. (2001). Assessing fitness for use: The expected value of spatial data sets. International Journal of Geographical Information Science, 15(5), 457–471. https://doi.org/10.1080/13658810110053116

Burley, T. M. (1961). Land use or land utilization? The Professional Geographer, 13(6), 18–20. https://doi.org/10.1111/j.0033-0124.1961.136_18.x

Büttner, G., Kosztra, B., Maucha, G., Pataki, R., Kleeschulte, S., Hazeu, G., Vittek, M., Schröder, C., & Littkopf, A. (2021, April 20). Product user manual CORINE. European Environment Agency (EEA). https://land.copernicus.eu/en/technical-library/clc-product-user-manual/@@download/file

Büttner, G., Kosztra, B., Soukup, T., Sousa, A., & Langanke, T. (2017, October 25). CLC2018 Technical Guidelines. European Environment Agency (EEA). https://land.copernicus.eu/en/technical-library/clc-2018-technical-guidelines/@@download/file

Cheng, C., Messerschmidt, L., Bravo, I., Waldbauer, M., Bhavikatti, R., Schenk, C., Grujic, V., Model, T., Kubinec, R., & Barceló, J. (2024). A General Primer for Data Harmonization. Scientific Data, 11(1), 152. https://doi.org/10.1038/s41597-024-02956-3

Comber, A., Fisher, P., & Wadsworth, R. (2005, April). What is land cover? Department of Geography, University of Leicester. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Th64cmYAAAAJ&citation_for_view=Th64cmYAAAAJ:ZeXyd9-uunAC

Costelloe, D., Mooney, P., & Winstanley, A. (2001). Multi-Objective Optimisation on Transportation Networks. National University of Ireland Maynooth (NUIM). http://www.caesarsystems.com/Technica/Value_of_Info/valueof.htm

Duckham, M., Lingham, J., Mason, K., & Worboys, M. (2006). Qualitative reasoning about consistency in geographic information. Information Sciences, 176(6), 601–627. https://doi.org/10.1016/j.ins.2005.01.021

Dykes, J., Wood, J., & Slingsby, A. (2010). Rethinking Map Legends with Visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6), 890–899. https://doi.org/10.1109/TVCG.2010.191

Eastman, J. R., & He, J. (2020). A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling. Land, 9(11), 407. https://doi.org/10.3390/land9110407

EEA, E. E. A. (2020a). Metadaten CORINE Raster. European Environment Agency (EEA). https://sdi.eea.europa.eu/catalogue/srv/api/records/960998c1-1870-4e82-8051-6485205ebbac

EEA, E. E. A. (2020b). Metadaten CORINE Vektor. European Environment Agency (EEA). https://sdi.eea.europa.eu/catalogue/srv/api/records/71c95a07-e296-44fc-b22b-415f42acfdf0

EEA, E. E. A. (2025, October 16). CORINE Nomenklatur Tabelle. European Environment Agency (EEA). https://www.parcs.at/nphtt/pdf_public/2014/30611_20141209_154737_CORINE_Nomenklatur.pdf

Escobar, F., & Mas, J.-F. (Eds.). (2018). Geomatic Approaches for Modeling Land Change Scenarios. Springer-Verlag. https://doi.org/10.1007/978-3-319-60801-3

Feranec, J., Jaffrain, G., Soukop, T., & Hazeu, G. (2006). The thematic accuracy of Corine land cover 2000: Assessment using LUCAS. European Environment Agency (EEA). https://doi.org/10.2800/87469

Feranec, J., Jaffrain, G., Soukup, T., & Hazeu, G. (2010). Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Applied Geography, 30(1), 19–35. https://doi.org/10.1016/j.apgeog.2009.07.003

Ferster, M., & Assoulin, D. (2019). Arealstatistik: Referenzdokument zu den Formeln (No. 23264167; p. 5). Bundesamt für Statistik (BFS). https://dam-api.bfs.admin.ch/hub/api/dam/assets/23264167/master

FOEN, F. O. for the E. (2022). Switzerland’s greenhouse gas inventory 1990–2020: National inventory report and reporting tables (CRF). Federal Office for the Environment (FOEN). http://www.climatereporting.ch

Gomes, E., Inácio, M., Bogdzevič, K., Kalinauskas, M., Karnauskaitė, D., & Pereira, P. (2021). Future land-use changes and its impacts on terrestrial ecosystem services: A review. Science of The Total Environment, 781, 146716. https://doi.org/10.1016/j.scitotenv.2021.146716

Google Earth Engine. (2022). ESA WorldCover 10m v100 Earth Engine Data Catalog. Google for Developers. https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100

Grüter, C. (2024, July 1). Minimales Geodatenmodell amtliche Vermessung Bodenbedeckung. Bundesamt für Landestopografie swisstopo. https://www.cadastre-manual.admin.ch/dam/de/sd-web/ajONrLGSL74-/DMAV_Bodenbedeckung_V1_0-DE.pdf

Grütter, C. (2025, February 1). Dokumentation Modellierungsgrundsätze Geodatenmodell der amtlichen Vermessung DMAV. Bundesamt für Landestopografie swisstopo. https://www.cadastre-manual.admin.ch/dam/de/sd-web/CCicuR3tpw70/DMAV_Modellierungsgrunds%C3%A4tze_V1_0-DE.pdf

IPCC. (2003). Good practice guidance for land use, land-use change and forestry. Institute for Global Environmental Strategies.

Jansen, L. J. M., Groom, G., & Carrai, G. (2008). Land-cover harmonisation and semantic similarity: Some methodological issues. Journal of Land Use Science, 3(2–3), 131–160. https://doi.org/10.1080/17474230802332076

Jeannet, A., & Willi-Tobler, L. (2024). Arealstatistik: Erhebung der Bodennutzung und der Bodenbedeckung. Bundesamt für Statistik (BFS). https://www.bfs.admin.ch/bfs/de/home/statistiken/raum-umwelt/bodennutzung-bedeckung.assetdetail.30245479.html

Karimi, Z. (2021). Confusion Matrix. ResearchGate. https://www.researchgate.net/publication/355096788_Confusion_Matrix

Klir, G. J., & Wierman, M. J. (1999). Uncertainty-Based Information (Vol. 15). Springer-Verlag. https://doi.org/10.1007/978-3-7908-1869-7

Kosztra, B., Hazeu, G., Arnold, S., & Büttner, G. (2019). CORINE Land Cover Nomenclature. European Environment Agency (EEA). https://land.copernicus.eu/en/technical-library/clc-illustrated-nomenclature-guidelines/@@download/file

Kubacka, M., & Piniarski, W. (2024). Searching for optimal solutions in a landscape fragmentation assessment. Ecological Indicators, 163, 112118. https://doi.org/10.1016/j.ecolind.2024.112118

Leyk, S., Boesch, R., & Weibel, R. (2005). A Conceptual Framework for Uncertainty Investigation in Map-based Land Cover Change Modelling. Transactions in GIS, 9(3), 291–322. https://doi.org/10.1111/j.1467-9671.2005.00220.x

Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., & Pesaresi, M. (2019). The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385–1409. https://doi.org/10.5194/essd-11-1385-2019

Liang, X., Guan, Q., Clarke, K. C., Chen, G., Guo, S., & Yao, Y. (2021). Mixed-cell cellular automata: A new approach for simulating the spatio-temporal dynamics of mixed land use structures. Landscape and Urban Planning, 205, 103960. https://doi.org/10.1016/j.landurbplan.2020.103960

Livers, F., & Felder, S. (2015). Analytische Legenden für Bodenbedeckungsveränderungen [Bachelor]. Fachhochschule Nordwestschweiz (FHNW).

Love, K., Ye, K., Smith, E., & Prisley, S. (2010). Error Models in Geographic Information Systems Vector Data Using Bayesian Methods. https://www.researchgate.net/figure/Shi-et-als-G-band-concept_fig3_266419954

Markham, K., Frazier, A. E., Singh, K. K., & Madden, M. (2023). A review of methods for scaling remotely sensed data for spatial pattern analysis. Landscape Ecology, 38(3), 619–635. https://doi.org/10.1007/s10980-022-01449-1

Matthews et al. (2021). Annex VII: Glossary. In Climate Change 2021: The Physical Science Basis. Cambridge University Press. https://doi.org/10.1017/9781009157896.022

Meentemeyer, V., & Box, E. O. (1987). Scale Effects in Landscape Studies. In M. G. Turner (Ed.), Landscape Heterogeneity and Disturbance (Vol. 64, pp. 15–34). Springer-Verlag. https://doi.org/10.1007/978-1-4612-4742-5_2

Mitas, L., & Mitasova, H. (1999). Spatial interpolation. In Geographical Information Systems (pp. 481–492). Wiley. http://fatra.cnr.ncsu.edu/~hmitaso/gmslab/papers/mitas_mitasova_1999_2005.pdf

NCCS. (2018). CH2018 – Climate Scenarios for Switzerland (p. 271). National Center for Climate Services (NCCS). https://www.nccs.admin.ch/dam/nccs/de/dokumente/website/klima/CH2018_Technical_Report.pdf

numiqo Team. (2025). Cohens Kappa. Online Statistics Calculator. https://numiqo.de/tutorial/cohens-kappa

swisstopo. (2024, November 1). Erfassungsgrundsätze Bodenbedeckung und Einzelobjekte (BB AV). Bundesamt für Landestopografie swisstopo. https://www.cadastre-manual.admin.ch/dam/de/sd-web/ibUKBxKIPZKW/241101_Erfassungsgrunds%C3%A4tze_BB_EO_DE.pdf

swisstopo. (2025). DMAV_Bodenbedeckung_V1_0.ili (Version 1.0) [Ili-Model]. Bundesamt für Landestopografie swisstopo. https://models.geo.admin.ch/V_D/DMAV_Bodenbedeckung_V1_0.ili

Thomas, I. N., & Giuliani, G. (2023). Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey. Land, 12(7), 1386. https://doi.org/10.3390/land12071386

Tsendbazar, N., Xu, P., Herold, M., Lesiv, M., & Duerauer, M. (2022, October 25). Product Validation Report ESA. European Environment Agency (EEA). https://worldcover2021.esa.int/data/docs/WorldCover_PVR_V2.0.pdf

Van De Kerchove, R., Zanaga, D., Xu, P., Tsendbazar, N., & Lesiv, M. (2022, October 24). Product user manual WorldCover (ESA). European Environment Agency (EEA). https://worldcover2021.esa.int/data/docs/WorldCover_PUM_V2.0.pdf

Verordnung über die amtliche Vermessung (VAV), 211.432.2 SR. (1992). https://www.fedlex.admin.ch/eli/cc/1992/2446_2446_2446/de

Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., … Arino, O. (2021). ESA WorldCover 10 m 2020 v100 (Version v100) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5571936

Datenquellen

Name der Datenquelle Link
Arealstatistik https://opendata.swiss/de/dataset/arealstatistik-der-schweiz
Amtliche Vermessung https://geodienste.ch/services/av
CORINE LandCover https://land.copernicus.eu/en/products/corine-land-cover?tab=datasets
ESA WorldCover https://esa-worldcover.org/en/data-access
   

Libraries

Library Dokumentation Version
affine https://pypi.org/project/affine/ 2.4.0
anyio https://anyio.readthedocs.io/en/stable/ 4.12.0
appdirs https://pypi.org/project/appdirs/ 1.4.4
asgiref https://asgiref.readthedocs.io/en/latest/ 3.11.0
asttokens https://pypi.org/project/asttokens/ 3.0.0
attrs https://www.attrs.org/en/stable/ 25.4.0
branca https://python-visualization.github.io/branca/ 0.8.2
cffi https://cffi.readthedocs.io/en/latest/ 2.0.0
charset-normalizer https://charset-normalizer.readthedocs.io/en/latest/ 3.4.4
click https://click.palletsprojects.com/ 8.3.1
click-plugins https://pypi.org/project/click-plugins/ 1.1.1.2
cligj https://pypi.org/project/cligj/ 0.7.2
colorama https://pypi.org/project/colorama/ 0.4.6
comm https://pypi.org/project/comm/ 0.2.3
contourpy https://contourpy.readthedocs.io/en/stable/ 1.3.2
cycler https://matplotlib.org/cycler/ 0.12.1
debugpy https://github.com/microsoft/debugpy 1.8.17
decorator https://decorator.readthedocs.io/en/stable/ 5.2.1
et-xmlfile https://et-xmlfile.readthedocs.io/en/latest/ 2.0.0
exceptiongroup https://pypi.org/project/exceptiongroup/ 1.3.0
executing https://pypi.org/project/executing/ 2.2.1
fiona https://fiona.readthedocs.io/en/stable/ 1.10.1
folium https://python-visualization.github.io/folium/ 0.20.0
fonttools https://fonttools.readthedocs.io/en/latest/ 4.60.1
freexl https://www.gaia-gis.it/freexl/ 2.0.0
geopandas https://geopandas.org/en/stable/ 1.1.1
geotiff https://gdal.org/ 1.7.4
h11 https://python-hyper.org/projects/h11/en/stable/ 0.16.0
h2 https://python-hyper.org/projects/h2/en/stable/ 4.3.0
hpack https://python-hyper.org/projects/hpack/en/stable/ 4.1.0
htmltools https://rstudio.github.io/htmltools/ 0.6.0
hyperframe https://python-hyper.org/projects/hyperframe/en/stable/ 6.1.0
idna https://pypi.org/project/idna/ 3.11
importlib-metadata https://importlib-metadata.readthedocs.io/en/latest/ 8.7.0
ipykernel https://ipykernel.readthedocs.io/en/latest/ 7.1.0
ipyleaflet https://ipyleaflet.readthedocs.io/en/stable/ 0.20.0
ipython https://ipython.readthedocs.io/en/stable/ 8.37.0
ipywidgets https://ipywidgets.readthedocs.io/en/stable/ 8.1.8
jedi https://jedi.readthedocs.io/en/stable/ 0.19.2
jinja2 https://jinja.palletsprojects.com/ 3.1.6
joblib https://joblib.readthedocs.io/en/stable/ 1.5.2
jupyter-client https://jupyter-client.readthedocs.io/en/stable/ 8.6.3
jupyter-core https://jupyter-core.readthedocs.io/en/stable/ 5.9.1
jupyter-leaflet https://ipyleaflet.readthedocs.io/en/stable/ 0.20.0
jupyterlab-widgets https://ipywidgets.readthedocs.io/en/stable/ 3.0.16
kiwisolver https://pypi.org/project/kiwisolver/ 1.4.9
mapclassify https://pysal.org/mapclassify/ 2.8.1
markdown-it-py https://markdown-it-py.readthedocs.io/en/latest/ 4.0.0
markupsafe https://markupsafe.palletsprojects.com/ 3.0.3
matplotlib-base https://matplotlib.org/ 3.10.7
matplotlib-inline https://pypi.org/project/matplotlib-inline/ 0.2.1
mdit-py-plugins https://mdit-py-plugins.readthedocs.io/en/latest/ 0.5.0
mdurl https://github.com/executablebooks/mdurl 0.1.2
munkres https://pypi.org/project/munkres/ 1.1.4
narwhals https://pypi.org/project/narwhals/ 2.11.0
nest-asyncio https://github.com/erdewit/nest_asyncio 1.6.0
networkx https://networkx.org/documentation/stable/ 3.4.2
numpy https://numpy.org/doc/ 2.2.6
openpyxl https://openpyxl.readthedocs.io/en/stable/ 3.1.5
orjson https://github.com/ijl/orjson 3.11.4
packaging https://packaging.pypa.io/en/latest/ 25.0
pandas https://pandas.pydata.org/docs/ 2.3.3
parso https://parso.readthedocs.io/en/latest/ 0.8.5
patsy https://patsy.readthedocs.io/en/stable/ 1.0.2
pillow https://pillow.readthedocs.io/en/stable/ 11.3.0
plotly https://plotly.com/python/ 6.4.0
proj https://proj.org/ 9.6.2
prompt-toolkit https://python-prompt-toolkit.readthedocs.io/en/stable/ 3.0.52
psutil https://psutil.readthedocs.io/en/stable/ 7.1.2
pure-eval https://pypi.org/project/pure-eval/ 0.2.3
pycparser https://pypi.org/project/pycparser/ 2.22
pygments https://pygments.org/ 2.19.2
pyogrio https://pyogrio.readthedocs.io/en/stable/ 0.11.0
pyparsing https://pyparsing-docs.readthedocs.io/en/latest/ 3.2.5
pyproj https://pyproj4.github.io/pyproj/stable/ 3.7.1
pysocks https://pypi.org/project/PySocks/ 1.7.1
python-dateutil https://dateutil.readthedocs.io/en/stable/ 2.9.0
python-multipart https://pypi.org/project/python-multipart/ 0.0.20
pytz https://pythonhosted.org/pytz/ 2025.2
pyzmq https://pyzmq.readthedocs.io/ 27.1.0
questionary https://github.com/tmbo/questionary 2.1.1
rasterio https://rasterio.readthedocs.io/en/stable/ 1.4.3
requests https://requests.readthedocs.io/ 2.32.5
scikit-learn https://scikit-learn.org/stable/ 1.7.2
scipy https://scipy.org/ 1.15.2
seaborn https://seaborn.pydata.org/ 0.13.2
shapely https://shapely.readthedocs.io/en/stable/ 2.1.2
shiny https://shiny.posit.co/py/ 1.4.0
shinywidgets https://shiny.posit.co/py/api/shinywidgets/ 0.7.0
six https://six.readthedocs.io/ 1.17.0
snuggs https://pypi.org/project/snuggs/ 1.4.7
sqlite https://www.sqlite.org/docs.html 3.50.4
stack-data https://pypi.org/project/stack-data/ 0.6.3
starlette https://www.starlette.io/ 0.50.0
statsmodels https://www.statsmodels.org/stable/ 0.14.5
threadpoolctl https://threadpoolctl.readthedocs.io/en/latest/ 3.6.0
tornado https://www.tornadoweb.org/ 6.5.2
traitlets https://traitlets.readthedocs.io/en/stable/ 5.14.3
traittypes https://github.com/jupyter-widgets/traittypes 0.2.3
typing-extensions https://typing-extensions.readthedocs.io/ 4.15.0
uc-micro-py https://github.com/executablebooks/uc-micro-py 1.0.3
urllib3 https://urllib3.readthedocs.io/ 2.5.0
uvicorn https://www.uvicorn.org/ 0.38.0
watchfiles https://watchfiles.helpmanual.io/ 1.1.1
wcwidth https://pypi.org/project/wcwidth/ 0.2.14
websockets https://websockets.readthedocs.io/en/stable/ 15.0.1
widgetsnbextension https://ipywidgets.readthedocs.io/en/stable/ 4.0.15
xyzservices https://xyzservices.readthedocs.io/en/stable/ 2025.4.0
zipp https://zipp.readthedocs.io/en/stable/ 3.23.0

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