This dataset features a map of building fractions (as opposed to built-up fractions including other impervious surfaces such as roads) for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. The data were created by using machine learning regression and spectral unmixing, using synthetically mixed training data. The dataset is completely based on freely accessible satellite imagery, and was validated with freely available building footprint reference data for three federal states. We recommend to use data at an aggregated resolution of 20m, 50m, or 100m, and to clip data at about 20% building fraction when using 10m resolution maps (or roughly the corresponding RMSE at any other resolution). Temporal extent
Used Sentinel-2 data were acquired in 2018, and Sentinel-1 data were acquired in 2017 (see publication). The map is, thus, representative for 2017/2018. Validation results can be affected by building footprint reference data from different years. Data format
The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Building fraction values are in percent, from 0 to 100. In the original dataset with 10m spatial resolution, fraction values are equivalent to area in m². In the aggregated dataset with 100m spatial resolution, the values must be multiplied with 100 in order to see area in m². Further information
For further information, please see the publication or contact Franz Schug ([email protected]). A web-visualization of this dataset is available here. Publication
Schug, F.; Frantz, D.; Okujeni, A.; Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters. DOI: 10.1080/2150704X.2022.2088253 Acknowledgements
The dataset was generated by FORCE v. 3.6.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. Sentinel-1 data were provided by EODC. We thank the providers of the building footprint reference data (see publication). Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset features a map of building fractions (as opposed to built-up fractions including other impervious surfaces such as roads) for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. The data were created by using machine learning regression and spectral unmixing, using synthetically mixed training data. The dataset is completely based on freely accessible satellite imagery, and was validated with freely available building footprint reference data for three federal states. We recommend to use data at an aggregated resolution of 20m, 50m, or 100m, and to clip data at about 20% building fraction when using 10m resolution maps (or roughly the corresponding RMSE at any other resolution). Temporal extent
Used Sentinel-2 data were acquired in 2018, and Sentinel-1 data were acquired in 2017 (see publication). The map is, thus, representative for 2017/2018. Validation results can be affected by building footprint reference data from different years. Data format
The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Building fraction values are in percent, from 0 to 100. In the original dataset with 10m spatial resolution, fraction values are equivalent to area in m². In the aggregated dataset with 100m spatial resolution, the values must be multiplied with 100 in order to see area in m². Further information
For further information, please see the publication or contact Franz Schug ([email protected]). A web-visualization of this dataset is available here. Publication
Schug, F.; Frantz, D.; Okujeni, A.; Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters. DOI: 10.1080/2150704X.2022.2088253 Acknowledgements
The dataset was generated by FORCE v. 3.6.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. Sentinel-1 data were provided by EODC. We thank the providers of the building footprint reference data (see publication). Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset is composed of three-season simulated EnMAP mosaics for the Lake Tahoe region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area and for the Santa Barbara region.

This dataset is composed of three-season simulated EnMAP mosaics for the Santa Barbara region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area and for the Lake Tahoe region.

This dataset is composed of simulated EnMAP mosaics for the San Francisco Bay Area, USA. Hyperspectral imagery used for the EnMAP simulation was collected across three time periods (Spring, Summer, and Fall) in 2013 with the AVIRIS-Classic sensor flown as part of the HyspIRI Preparatory Campaign. Flight lines were simulated to EnMAP-like data using the EnMAP end-to end Simulation tool to produce 30 x 30 m imagery with 195 bands (after band removal) ranging from 423 to 2439 nm. Secondary geometric correction was applied using automatically generated tie points, and a class-wise empirical across track brightness correction was implemented to mitigate brightness gradients.