OpenStreetMap (OSM) is a collaborative project collecting geographical data of the entire world. The level of detail of OSM data and its data quality vary much across different regions and domains. In order to analyse such variations it is often necessary to research the history and evolution of the OSM data. The OpenStreetMap History Database (OSHDB) is a new data analysis tool for spatio-temporal geographical vector data. It is specifically optimized for working with OSM history data on a global scale and allows one to investigate the data evolution and user contributions in a flexible way. Benefits of the OSHDB are for example: to facilitate accessing OSM history data as a research subject and to assess the quality of OSM data by using intrinsic measures. This article describes the requirements of such a system and the resulting technical implementation of the OSHDB: the OSHDB data model and its application programming interface.

3D WebGIS systems have been mentioned in the literature almost since the beginning of the graphical web era in the late 1990s. The potential use of 3D WebGIS is linked to a wide range of scientific and application domains, such as planning, controlling, tracking or simulation in crisis management, military mission planning, urban information systems, energy facilities or cultural heritage management, just to name a few. Nevertheless, many applications or research prototypes entitled as 3D WebGIS or similar are mainly about 3D visualization of GIS data or the visualization of analysis results, rather than about performing the 3D analysis itself online. This research paper aims to step forward into the direction of web-based 3D geospatial analysis. It describes how to overcome speed and memory restrictions in web-based data management by adapting optimization strategies, developed earlier for web-based 3D visualization. These are applied in a holistic way in the context of a fully 3D line-of-sight computation over several layers with split (tiled) and unsplit (static) data sources. Different optimization approaches are combined and evaluated to enable an efficient client side analysis and a real 3D WebGIS functionality using new web technologies such as HTML5 and WebGL.

Our map represents the first successful large-area fusion of OpenStreetMap and Copernicus data at a spatial resolution of 10 m or finer and can be applied globally. We addressed varying label noise and feature space quality, utilizing artificial intelligence and advanced computing. Our method relies solely on openly available data streams and methods, eliminating training data acquisition or the need for additional expert knowledge for such purpose. We extracted land use labels from OpenStreetMap and remote sensing data to create a contiguous land use map of the European Union as of March 2020. OpenStreetMap tags were translated into land use labels, directly mapping 61.8% of the Union’s area. These labels served as training data for a classification model, predicting land use in remaining areas. Country-specific deep learning convolutional neural networks and Sentinel-2 feature space composites of 2020 at 10 m resolution were employed. The overall map accuracy is 89%, with class-specific accuracies ranging from 77% to 99%. The data set is available for download from https://doi.org/10.11588/data/IUTCDN and visualization at https://osmlanduse.org.

This paper describes the work-in-progress carried out at the Carinthia University of Applied Sciences to create a LabVIEW based toolkit that aims at providing laboratory developers with a tool to easily develop new batched lab servers for the iLab Shared Architecture.