This proposal is a continuation of the project 'GeoKernels': Kernel-Based Methods for Geo- and Environmental Sciences (200021-113944/1). The projects deal with the fundamental developments in the field of intelligent geospatial data analysis and modelling using Machine Learning algorithms. The first phase of the GeoKernels project provided a general methodology for using the state-of-the-art models in machine learning (where kernel methods establish one of the main successful areas) for spatio-temporal data analysis and modelling. Real life data lie on some lower-dimensional manifolds in the original high-dimensional geo-feature space. For environmental data these natural low-dimensional geo-manifolds are induced by rivers, relief features, urban structures, hydro-geological formations, etc. During PhaseI in the GeoKernels methodology, semi-supervised learning was applied to the stated problems in an efficient and elegant manner. The continuation of the project (Phase2) is aimed at advancing the data-driven GeoKernels modelling methodology, bringing it closer to the need of real-life operational use, from one side, and developing new methods concerned with geomanifold modelling by feature extraction and interpretable predictions with multiple kernel learning. The new developments will provide more transparency to the data-driven methods and will bring more flexibility for modelling complex environmental processes. The methods are particularly targeted at applications in natural hazards assessments and forecasting, topo-climatic modelling and renewable resources evaluation. Due to the established collaboration, the results of this multidisciplinary project will improve spatial data collection and management process in different scientific fields, will develop new procedures of environmental pattern recognition and modelling approaches using recent achievements in machine learning. The main results will be presented at the international conferences and workshops and published in scientific journals and books. The results, including the software modules (Machine Learning Office) and online interactive case studies will be available at the website of the project for the research and educational purposes (www.geokernels.org).
Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gra-dient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.
Version HIstory: This version of the Berlin-Urban-Gradient-Dataset was updated to account for errors in the spatial referencing. This included six updated header files (.hdr) and two updated shapte files. See details in the new version and the associated data report.
27 Feb 2025: change to CC BY 4.0 License.
Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gradient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.Version HIstory:This version of the Berlin-Urban-Gradient-Dataset was updated to account for errors in the spatial referencing. The following files were updated:Folder “BerlinUrbGrad2009_01_image_products\01_image_products”Replacement of header files of the four image products: (1) EnMAP01_Berlin_Urban_Gradient_2009.hdr, (2) EnMAP02_Berlin_Urban_Gradient_2009.hdr, (3) HyMap01_Berlin_Urban_Gradient_2009.hdr, (4) HyMap02_Berlin_Urban_Gradient_2009.hdr.Folder “BerlinUrbGrad2009_02_additional_data\02_additional_data\land_cover”:Replacement of header files of the two reference land cover images (Land-Cov_Layer_Level1_Berlin_Urban_Gradient_2009.hdr, Lan d-Cov_Layer_Level2_Berlin_Urban_Gradient_2009.hdr).Replacement of the shapefile (incl. extensions) representing the references polygons (LandCov_Vec_polygons_Berlin_Urban_Gradient_2009.shp, *.dbf, *.prj, *.sbn, *.sbx, *.shp.xml, *.shx).