Administrative boundary is a sub layer of Administrative units and is based on the data set swissBOUNDARIES3D by swisstopo. It contains all administrative units and national boundaries of Switzerland and the Principality of Liechtenstein in vector form. The product is based on an optimized data model for Switzerland and conforms to the data of the Swiss Federal Statistical Office. swissBOUNDARIES3D replaces the GG25 dataset from 2010 onwards.

The administrative boundaries (Municipalities, Districts and Cantons) provided by Swiss Federal Statistical Office (FSO) are optimized for the cartographic visualization of statistical data at smaller scales. The source data (swissBOUNDARIES3D) has been generalized to the following two levels of detail, 1:500,000 and 1:2,000,000.

Yearly updates, matching the update cycles of the census data (GEOSTAT) provided by the FSO, are also available.

A detailed description of the data is available in English, German and French.

The service is in the Swiss coordinate system CH1903+ LV95. The LV95 Swiss Topographic map is best suited as a basemap for this service.

Administrative units is based on the data set swissBOUNDARIES3D by swisstopo. It contains all administrative units and national boundaries of Switzerland and the Principality of Liechtenstein in vector form. The product is based on an optimized data model for Switzerland and conforms to the data of the Swiss Federal Statistical Office. swissBOUNDARIES3D replaces the GG25 dataset from 2010 onwards.

This feature layer is based on the swissBOUNDARIES3D data provided by the Federal Office of Topography (swisstopo) and contains following layers of information: Administrative base units (municipalities)DistrictsCantonsCountries

Two levels of detail are available: large scale, based on swissTLM3D data and mid scale, based on VECTOR200 data.The perimeter includes Switzerland, the Principality of Liechtenstein as well as the exclaves in neighboring countries. Besides political municipalities, areas such as the State Forest of Galm, lakes and other common areas larger than 5 km2 which are not assigned to a specific municipality are also designated as independent surfaces. The data set includes 2222 municipalities.swissBOUNDARIES3D is updated annually. The current version represents the situation as of 1 January of the current year. Updating is based on the information of the Cadastral Surveying (CS) and the Federal Statistical Office (FSO). A detailed description of the data is available in both German and French.The service is in the Swiss coordination system CH1903+ LV95. The LV95 Swiss Topographic map is best suited as a basemap for this service.

Boundaries. Map types: Lines, Choropleths. Spatial extents: World, Switzerland, Switzerland plus. Times: 2022, 2022

This polygon shapefile contains the boundary of Switzerland (adm0). This layer is part of the Global Administrative Areas 2015 (v2.8) dataset.

The boundaries form part of the inventory map, regardless of the classification (national, regional, local). On the map the boundaries mark the beginning and end of a section (of a route). The boundaries are point objects.

We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

The winter national map is a derivation of the topographical national maps of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation at a scale of 1:10'000 to 1:1 million. The winter national map is only available in a digital format (Swiss Map Raster, API, WMTS) and is updated once a year..

Dominant tree species map of Switzerland We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg). Access will be granted upon request. In total, the following 15 species were mapped: Abies alba, Acer pseudoplatanus, Alnus glutinosa, Alnus incana, Betula pendula, Castanea sativa, Fagus sylvatica, Fraxinus excelsior, Picea abies, Pinus cembra, Pinus mugo arborea, Pinus sylvestris, Quercus petraea, Quercus robur, Sorbus aucuparia. -br/--br/- Approach -br/--br/- Data - Swiss National Forest Inventory Data (stand species with - 60 % dominance in upper canopy; on at least more than 9 plots dominant) - Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI) - Digital elevation model (DEM) (swissalti3d, 5 m) - Biogeographical regions (Federal Office for the Environment FOEN) - Forest mask 2017 (Approach: Waser et al., 2015) -br/--br/- Modeling approach We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold. The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time. -br/--br/- Training data for modeling - 295 Sentinel-2, DEM & Biogeographical variables - 10525 tree species pixels -br/--br/- Selected variables for final model 1. EVI of 2020.05.16 2. NDMI of 2020.03.12 3. CIRE of 2020.04.16 4. NDMI of 2020.07.05 5. CCI of 2020.05.11 6. dem 7. CCI of 2020.08.14 8. NDMI of 2020.08.24 9. CCI of 2020.12.22 10. NDMI of 2020.04.21 11. NDMI of 2020.11.17 12. NDMI of 2020.08.09 13. CIRE of 2020.03.22 14. CIRE of 2020.08.09 14. CCI of 2020.11.02 15. CIRE of 2020.06.10 -br/--br/- Overall Accuracy of final model - 0.759 -br/--br/- Nationwide prediction - Predicted throughout forest mask 2017 (Approach: Waser et al., 2015) - Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts) - Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data -br/--br/- -br/--br/- Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing -br/--br/- -br/--br/- References - Kuhn, M. (2022). Classification and Regression Training. 6.0-93. - Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001 - Meyer, H., Milà, C., & Ludwig, M. (2022). CAST: 'caret' Applications for Spatial-Temporal Models. 0.7.0. - Ververidis, D., & Kotropoulos, C. (2005). Sequential forward feature selection with low computational cost. 2005 13th European Signal Processing Conference. - Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition. Forests, 6, 12, 4510–4528. - Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

Swiss Map Vector 1000 is the Swiss national 1:1,000,000 scale map in vector format. It provides a clear depiction of the main traffic axes, residential areas, bodies of water, summits and borders. Terrain is depicted using raster data. Relief and hypsometric depictions are available. Switzerland and neighbouring countries are shown from Lyon to Salzburg and from Strasbourg to Genoa. Vector data comes from the digital cartographic model, which is also used for the derivation of printed maps, and the Swiss Map Raster. Contents are structured according to subject matter and can be edited by class or by object. Depiction is very similar to that of the printed national map.

This vector layer provides a detailed vector basemap for Switzerland in the local projection system CH1903+ LV95 featuring a classic Esri topographic map style. Layer designed for use with a Hillhade relief for added context. This vector tile layer provides unique capabilities for customization and high-resolution display.This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, administrative boundaries, and shaded relief for added context. It is built on the datasets swissTLM3D, swissBOUNDARIES3D, and swissTLMRegio, and Swiss Map Vector 10 provided by swisstopo and is enhanced with owner parcels, roads and sidewalks provided by following cantons: AargauLV95 Swiss Topographic Map - Overview, Appenzell I.Rh., Basel-Landschaft, Basel-Stadt, Bern, Fribourg, Genève, Glarus, Graubünden, Schaffhausen, Schwyz, Solothurn, St. Gallen, Thurgau, Ticino, Uri, Valais, Zug and Zürich.This is a multisource map style. This layer also includes vector contour lines. Even though there are two source paths in the layer's json, these are referenced from a single vector tile layer in this web map. The root.json style file calls two vector Hosted Tile Layers to display all the data in the map. One source (esri) contains all the basemap tiles for this layer. The other source (contours) contains all the contour lines. Use the Map Viewer (not Classic) to view all the features in this layer as intended.Use this MapThis map is designed to be used as a basemap layer or reference layer in a web map. You can add this layer to a web map and save as your own map. If you would like to use this map as a basemap layer in a web map, you may use the vector basemap LV95 Swiss Topographic (with Contours and Hillshade) web map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers. For details on how to customize this map, please refer to this article.DataThe source data can be downloaded from swisstopo's website and geodienste.ch.Data vintage: March 2025. The service is updated annually.Data vintage Contours: 2017

Swiss Map Vector 500 is the Swiss national 1:500,000 scale map in vector format. It includes the railway network, all highways, semi-highways, transit and link roads, a highly simplified depiction of residential areas and terrains and the names of principal objects. Raster data for relief and rock features complete the vector data. Switzerland and neighbouring countries are shown. Vector data comes from the digital cartographic model, which is also used for the derivation of printed maps, and the Swiss Map Raster. Contents are structured according to subject matter and can be edited by class or by object. Depiction is very similar to that of the printed national map.

Swiss Map Vector 10 is the Swiss national 1:10,000 scale map in vector format. It includes all complete and differentiated rail, road and path networks, a detailed representation of residential areas, bodies of water, vegetation and terrains complete with annotations. Raster data for relief and rock features complete the vector data. The perimeter of the map covers the national territory of both Switzerland and the Principality of Liechtenstein. Updates are made every year based on the latest version of swisstopo's topographic landscape model (TLM). Contents are structured according to subject matter and can be edited by class or by object. Depiction is very similar to that of the printed national map.

The land use and cover category contains the "geostat25" and "wslhabmap" datasets.

The geostat25 dataset describes the land use and cover of Switzerland. After resampling the “Downscaled Land Use/Land Cover of Switzerland” source data (Giuliani et al., 2022) to the SWECO25 grid, we generated individual layers for the 65 land use and cover classes and the 3 time periods (1992-1997, 2004-2009, and 2013-2018) that were available. For each class and period, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 2,730 layers. Final values were rounded and multiplied by 100.

The wslhabmap dataset (land use and cover category) describes the natural habitats of Switzerland. After rasterizing and resampling the “Habitat Map of Switzerland v1” source data (Price et al., 2021) to the SWECO25 grid, we generated individual layers for 41 categories (32 classes and 9 groups). The groups correspond to the first level of the TypoCH classification and the classes to the second level. For details on the TypoCH classification see Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Guide des milieux naturels de Suisse : Écologie, menaces, espèces caractéristiques. Rossolis. For each of the 41 categories, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given category in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 574 layers. Final values were rounded and multiplied by 100.

The detailed list of layers available is provided in SWECO25_datalayers_details_lulc.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path.

References:

G. Giuliani, D. Rodila, N. Külling, R. Maggini, A. Lehmann, Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022).

B. Price, Huber, N., Ginzler, C., Pazúr, R., Rüetschi, M., "The Habitat Map of Switzerland v1," (Birmensdorf, Switzerland, 2021)

Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P.-L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: A cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02899-1

V2: metadata update

Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse The FOEN funded project ‘Developing a Habitat Map of Switzerland’ conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Habitats are mapped through a variety of approaches that can be grouped as either: 1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.

In a similar way to water storage capacity, the aim is to specify how many equivalents of cations can be stored in the soil. The stored milliequivalents of cations were converted on the basis of a column of soil with a surface area of 1cm2 and a height corresponding to the physiological root penetration depth. This gave the milliequivalents (mEq) of cations per cm2. (Details: Soil suitability map of Switzerland, March 1980).

On the soil suitability map, each mapping unit has a code consisting of an uppercase letter and a number. The letters stand for 25 different physiographical units. The numbers represent different elements of the landscape, categorised by bedrock, slope and gradient. Each mapping unit also corresponds to one or more soil types. There are 144 mapping units in total. They are grouped together into 18 different coloured categories on the map from the perspective of soil suitability. Agricultural criteria have primarily been used for this classification.

Degree of urbanisation of the communes. Map types: Lines, Choropleths. Spatial extent: Switzerland. Time: 2011. Spatial unit: Communes

Lebensraumkarte der Schweiz v1.1 2024/La carte des milieux naturels de Suisse v1.1 2024 The FOEN funded project ‘The Habitat Map of Switzerland:continual improvement’ conducted at the WSL, has improved on version 1.0 of a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015). The Habitat Map of Switzerland maps the TypoCH habitat types wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). Habitats are mapped through a variety of approaches that can be grouped as either: 1. Derived from the existing Swiss-wide datasets a) the high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), and b) the harmonized mapping of agricultural fields area from the ‘Landwirtschaftliche Nutzfläche’ (LWN) mapping. 2. Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3. Combining existing species distribution models to determine habitat types, or 4. Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models (based on digital aerial photogrammetry and/or SwissSurface3D aerial laser scanning (ALS) data), the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 or Planet satellite imagery. Further detail on the methodology can be found within the README document.