Please note: for a correct view and use of this dataset it is advisable to consult it at original page on the Arezzo Portal. At the same address there are also, for the enabled datasets, additional access formats, the preview of the visualization via API call, the consultation of the fields in DCAT-AP IT format, the possibility to express an evaluation and comment on the dataset itself. All resource formats available for this dataset can be downloaded as ZIP packages: inside the package sarà available the resource in the chosen format, complete with all the information on the metadata and the license associated with it. The resource is the logical container of the projects of the cartographic works of the start of the procedure of the variant to the Structural Plan and the new Operational Plan, realized through the desktop application QGIS.

Plugin pro QGIS pro efektivní využití OpenData. Software používá CKAN API k nalezení záznamů.

DOP werden aus Orientierten Luftbildern hergestellt und jeweils im Anschluss an eine Befliegung für die neu erfassten Gebiete berechnet. OGC GeodatendiensteErste Bildflüge des Jahres 2025 im WMS eingebunden. Weitere Informationen im Newsfeed.WMShttps://opendata.lgln.niedersachsen.de/doorman/noauth/dop_wmsWMS historischhttps://opendata.lgln.niedersachsen.de/doorman/noauth/doph_wms STAC-APIKatalog URL:https://dop.stac.lgln.niedersachsen.deOpenAPI Service Beschreibung:https://dop.stac.lgln.niedersachsen.de/api.html MassendownloadGeoJSONKoordinatenreferenzsystemEPSG 25832 (ETRS89/UTM 32N) Metadatenhttps://ni-harvest-prod.geocat.live/ DatenformatCloud Optimized GeoTiff (RGBI),JPEG komprimiertes Cloud Optimized GeoTiff (RGB) AktualitätPDF Dateigröße539 MB (RGBI inkl. XML-Metadatendatei),ca. 15 MB (RGB inkl. XML-Metadatendatei) Auflösung20cm, in Teilen auch 10cm Kachelgröße2 km x 2 km FarbkanäleRGB, RGBI Produkt- und Formatbeschreibunghttps://www.lgln.niedersachsen.de BeschreibungDOP werden aus Orientierten Luftbildern hergestellt und jeweils im Anschluss an eine Befliegung für die neu erfassten Gebiete berechnet. In einem rechnergestützten Verfahren werden die Orientierten Luftbilder auf das Digitale Geländemodell DGM 5 projiziert und zu ATKIS-DOP aufbereitet. Das Ergebnis sind georeferenzierte, digitale fotorealistische Abbildungen der Erdoberfläche, in denen jedem Pixel eine eindeutige Koordinate zugeordnet werden kann. ATKIS-DOP sind maßstabstreu und können so direkt mit Karten gleichen Maßstabs verglichen oder mit Fachdaten, z. B. Straßenplanungen, digital zusammengeführt oder überlagert werden. Die Georeferenzierung der ATKIS-DOP erfolgt im Europäischen Terrestrischen Referenzsystem 1989 in Verbindung mit der Universalen Transversalen Mercator-Abbildung in Zone 32 (ETRS89/UTM32). Die ATKIS-DOP, die dem Produkt-standard der Arbeitsgemeinschaft der Vermessungsverwaltungen der Bundesrepublik Deutschland (ADV) entsprechen, stehen flächendeckend für Niedersachsen zur Verfügung. Abhängig von den Bildflügen, werden sie seit 2011 regelmäßig innerhalb von drei Jahren aktualisiert.Hinweis: Für einige Kacheln liegen bereits mehrere DOP vor - die neueren entsprechen der Qualitätsstufe "TrueDOP".Mit dem Bildflugjahr2021 hat das LGLN die Bereitstellung der (klassischen) Digitalen Orthophotos in die Qualitätsstufe TrueDOP überführtund folgt demProdukt- und Qualitätsstandardfür Digitale Orthophotos in der Version 4.1 der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (AdV). Durch Verwendung des sog. bildbasierten Digitalen Oberflächenmodells (bDOM) als Entzerrungsfläche wird eine genauere Bildgeometrie erzeugt und perspektivische Verzerrungen oder sichttote Bereiche werden vermieden.Ausführliche Produktbeschreibung Brauchen Sie Unterstützung?Für fachliche Fragestellungen zu dem Produkt, sehen Sie bitte in das FAQ.Hierfinden Sie eine Anleitung, wie Sie einen WebMapService (WMS) in eine Software (hier in QGIS) einbinden und nutzen können.In unseren Anleitungen finden Sie weitere Informationen, wie eine STAC-API verwendet werden kann. Für eine schnelle visuelle Darstellung des STAC kann der Radiant Earth STAC-Viewerverwendet werdenFür eine Nutzung der STAC-API in QGIS können Sie das QGIS-Plugin "QGIS STAC API-Browser" verwenden.In ArcGIS Pro können Sie ab der Version 3.2STAC API Verbindungenherstellen. Hierfinden Sie eine Anleitung für den Massendownload. Sind die Daten für Sie hilfreich?Feedback zum Produkt

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

The data are derived from interpretation of seismic reflection profiles within the offshore Corinth Rift, Greece (the Gulf of Corinth) integrated with IODP scientific ocean drilling borehole data from IODP Expedition 381 (McNeill et al., 2019a, 2019b). The data include rift fault coordinate (location, geometry) information and slip rate and extension rate information for the major faults. Seismic reflection data were published in Taylor et al. (2011) and in Nixon et al. (2016). Preliminary fault interpretations and rate data, prior to IODP drilling, were published in Nixon et al. (2016). Details of datasets: The data can be viewed in GIS software (ArcGIS, QGIS) or the Excel and .dbf files can be used for viewing of rate data and import of fault coordinates into other software. The 4 folders are for different time periods with shape files for the N-Dipping and S-Dipping Faults in the offshore Corinth Rift and respective slip and extension (horizontal) rates. The shapefiles are digitised fault traces for the basement offsetting faults, picked from the Multichannel Seismic Data collected by the R/V Maurice Ewing. Fault traces are segmented and each segment has an average throw (vertical) rate (Tavg) in mm/yr. The rates for the segments are averages based on measurements at the ends of each segment. The major fault trace segments also have slip-rates (slip_rate) and extension-rates (ext_rate or extension_) in mm/yr. All rates as well as the names for major faults can be located in the attribute table of the shape files along with X- and Y-coordinates. The coordinate system is WGS84 UTM Zone 34N. The shape files can be loaded into a GIS (ArcGIS, QGIS etc.) allowing mapping and visualization of the fault traces and their activity rates. In addition, the attribute tables are .dbf files found within each folder. These have also been provided as .xlsx (Excel) files which include the fault coordinate information, and slip rates and extension rates along the major faults. References McNeill, L.C., Shillington, D.J., Carter, G.D.O., and the Expedition 381 Participants, 2019a. Corinth Active Rift Development. Proceedings of the International Ocean Discovery Program, 381: College Station, TX (International Ocean Discovery Program). McNeill, L.C., Shillington, D.J., et al., 2019b, High-resolution record reveals climate-driven environmental and sedimentary changes in an active rift, Scientific Reports, 9, 3116. Nixon, C.W., McNeill, L.C., Bull, J.M., Bell, R.E., Gawthorpe, R.L., Henstock, T.J., Christodoulou, D., Ford, M., Taylor, B., Sakellariou, S. et al., 2016. Rapid spatiotemporal variations in rift structure during development of the Corinth Rift, central Greece. Tectonics, 35, 1225–1248. Taylor, B., J. R. Weiss, A. M. Goodliffe, M. Sachpazi, M. Laigle, and A. Hirn (2011), The structures, stratigraphy and evolution of the Gulf of Corinth Rift, Greece, Geophys. J. Int., 185(3), 1189–1219.

Das Digitale Geländemodell (DGM) ist ein Folgeprodukt aus den 3D-Messdaten. Es beschreibt die Geländeoberfläche, das Relief der Erde, durch die räumlichen Koordinaten einer repräsentativen Menge von Geländepunkten zum Erfassungszeitraum. OGC GeodatendiensteWMShttps://opendata.lgln.niedersachsen.de/doorman/noauth/dgm_wms STAC-APIKatalog URL:https://dgm.stac.lgln.niedersachsen.deOpenAPI Service Beschreibung:https://dgm.stac.lgln.niedersachsen.de/api.html MassendownloadGeoJSONKoordinatenreferenzsystemLage: EPSG 25832 (ETRS89/UTM 32N)Höhe: EPSG 7837 (DHHN2016 mit Normalhöhen-Null (NHN))Metadatenhttps://ni-harvest-prod.geocat.live/ DatenformatCloud-Optimized GeoTiff (COG) AktualitätPDF Dateigröße<5 MB je Kachel Auflösung1 m Kachelgröße1 x 1 km Produkt- und FormatbeschreibungGemäß Produkt- und Qualitätsstandard der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (https://www.adv-online.de/AdV-Produkte/Standards-und-Produktblaetter/Standards-der-Geotopographie/) Softwareempfehlungkostenfrei:QGISkommerziell:FMEesri ArcGIS BeschreibungDas Digitale Geländemodell (DGM) ist ein Folgeprodukt aus den 3D-Messdaten. Es beschreibt die Geländeoberfläche, das Relief der Erde, durch die räumlichen Koordinaten einer repräsentativen Menge von Geländepunkten zum Erfassungszeitraum. Höheninformationen werden maßstabsunabhängig und datenverarbeitungsgerecht vorgehalten.Auf Grundlage der seit 2019 niedersachsenweit verfügbaren Laserscan-Punktwolken aus Airborne Laserscaning (ALS), die eine geometrische Auflösung von mindestens 4 Punkten/m² aufweisen, wird ein hochgenaues DGM in 1 x 1 km Kacheln bereitgestellt. Die Rasterweite beträgt 1m (DGM1) und die Rasterelementposition liegt im Zentrum auf 0,5 m Positionen (= Pixelmitte). Die Höhengenauigkeit des DGM1 beträgt für flaches bis wenig geneigtes, offenes Gelände ≤15 cm und bei stark geneigtem Gelände mit dichter Vegetation ≤ 30 cm. Diese wurden über eine Delaunay-Triangulation aus der klassifizierten ALS-Punktwolke bestimmt. Das so entstandene COG ist in 32 Bit mit Float-Werten codiert und wurde über das Verfahren LZW komprimiert. Leere Pixel (NoData) enthalten den Wert -9999.EinsatzmöglichkeitenDas Digitale Geländemodell (DGM1) ist u. a. verwendbar fürFachinformationssystemenSimulation von Hochwasser und WindeinflüssenBodenkundlichen ReliefanalysenSchummerungs- und HöhenliniendarstellungenTrassenplanungen, Profildarstellungen und VolumenberechnungenEmissions- und Immissionsberechnungen, FunknetzplanungenForschung und Lehre Ausführliche Produktbeschreibung Brauchen Sie Unterstützung?Für fachliche Fragestellungen zu dem Produkt, sehen Sie bitte in das FAQ.Hierfinden Sie eine Anleitung, wie Sie einen WebMapService (WMS) in eine Software (hier in QGIS) einbinden und nutzen können.In unseren Anleitungen finden Sie weitere Informationen, wie eine STAC-API verwendet werden kann. Für eine schnelle visuelle Darstellung des STAC kann derRadiant Earth STAC-Viewerverwendet werdenFür eine Nutzung der STAC-API in QGIS können Sie das QGIS-Plugin "QGIS STAC API-Browser" verwenden.In ArcGIS Pro können Sie ab der Version 3.2STAC API Verbindungenherstellen. Hierfinden Sie eine Anleitung für den Massendownload. Sind die Daten für Sie hilfreich?Feedback zum Produkt

A 2D background map created based on a continuous digital map, projecting the map to EPSG:3857. Provides graphic map/image map/midnight map/gray map/hybrid map map information. WMTS is an OGC international standard that can be used in libraries and tools that comply with the standard, such as openlayers, qgis, arcgis, cesium, and leaflet. For more details, see the attached file. If the sample does not run, receive an authentication key, change it, and then run it.

🛰️ Geolocated Equipment Losses Dataset (WarSpotting API)

This dataset contains structured and geolocated records of military equipment losses collected via the WarSpotting.net API. Each entry includes details such as equipment type, model, status, nearest location, coordinates (where available), visual documentation, and source attribution.

It is ideal for spatial analysis, geospatial clustering (e.g., with H3), temporal trends, and visualization projects focused on equipment loss patterns over time.

🧾 Source & Collection Method

The dataset was generated using a custom Python script that systematically queried the WarSpotting public API from February 24, 2022 to June 8, 2025, collecting and flattening JSON data into an Excel file. The script handled pagination, cleaned the source data, and formatted each record for tabular use.

# Example API endpoint called per day:
https://ukr.warspotting.net/api/losses/russia/YYYY-MM-DD

Records were collected with a short delay between days to avoid overloading the server.

📄 Columns Included

🗃 Format

The dataset is provided in Excel (.xlsx) format and is ready for direct use in data science workflows including:

• Jupyter Notebooks
• Pandas / Python scripts
• Excel pivoting and sorting
• Geospatial tools (QGIS, Kepler.gl, Looker Studio, etc.)

🔧 Reproducibility

The dataset was created using the following Python packages:

pip install requests openpyxl

The full script used to collect the data is available on GitHub.

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy. Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca Available Products: ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/ Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/ Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022 Central Ontario Orthophotography Project (COOP) 2021 South-Western Ontario Orthophotography Project (SWOOP) 2020 Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 South Central Ontario Orthophotography Project (SCOOP) 2018 North-Western Ontario Orthophotography Project (NWOOP) 2017 Central Ontario Orthophotography Project (COOP) 2016 South-Western Ontario Orthophotography Project (SWOOP) 2015 Algonquin Orthophotography Project (2015) Additional Documentation: Ontario Web Raster Services User Guide (Word) Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every year Contact:Geospatial Ontario (GEO), geospatial@ontario.ca

A 40-minute tutorial to use OGC webservices offered by the Mission Atlantic GeoNode in your data analysis. The workshop makes use of Python Notebooks and common GIS Software (ArcGIS and QGIS), basic knowledge of Python and/or GIS software is recommended. • Introduction to OGC services • Search through metadata using the OGC Catalogue Service (CSW) • Visualize data using OGC Web Mapping Service (WMS) • Subset and download data using OGC Web Feature and Coverage Services (WFS/WCS) • Use OGC services with QGIS and/or ArcGIS

Das 3D-Gebäudemodell ist ein Folgeprodukt aus den ALKIS®-Gebäudegrundrissen, dem Digitalen Geländemodell (DGM) und den 3D-Messdaten.STAC-API Katalog URL:https://lod.stac.lgln.niedersachsen.deOpenAPI Service Beschreibung:https://lod.stac.lgln.niedersachsen.de/api.html MassendownloadGeoJSONKoordinatenreferenzsystemLage: EPSG 25832 (ETRS89/UTM 32N)Höhe: EPSG 7837 (DHHN2016 mit Normalhöhen-Null (NHN))Metadatenhttps://ni-harvest-prod.geocat.live/ DatenformatAdV-CityGML 1.0 AktualitätPDF Dateigrößeunterschiedlich Kachelgröße1 x 1 km² LegendeGemäß Produktions- und Qualitätsstandard (Stand 09/2024):Absolute Höhe (measuredHeight): Höhe des Gebäudes als Differenz in Metern zwischen dem höchsten Punkt der LoD2-Geometrie und dem tiefsten Bezugspunkt des GebäudesObjektidentifikator: eindeutige Bezeichnung eines GebäudesGebäudefunktion (function): entsprechend der Codeliste in Anlage 4 des PQS der AdVDatenquelle Dachhöhe: entsprechend der Codeliste in Anlage 1 des PQS der AdVDatenquelle Lage: entsprechend der Codeliste in Anlage 1 des PQS der AdVDatenquelle Bodenhöhe: entsprechend der Codeliste in Anlage 1 des PQS der AdVBezugspunkt Dach: entsprechend der Codeliste in Anlage 1 des PQS der AdVProduktionsdatum (creationDate): Letztes Importdatum in die Datenbank (Format: JJJJ-MM-TT)Grundrissaktualität: Zeitpunkt der Entnahme/des Abgleiches der Grundrisse mit der ALKIS-Datenbank (Format: JJJJ-MM-TT)Geometrietyp2D-Referenz (Geometrietyp2DReferenz): entsprechend der Codeliste in Anlage 1 des PQS der AdVAmtlicher Gemeindeschlüssel: GemeindeschlüsselReferenz auf das 2D-Gebäude: eindeutige Bezeichnung eines Gebäudes gemäß ALKIS®Adresse: bestehend aus Straße, Hausnummer, Stadt und Land, sofern geführt Zusätzliche Attribute: Lizenz: Geltende Lizenz je GebäudeEigentümer: Dateneigentümer, welcher mit der Lizenz zu nennen istProdukt- und FormatbeschreibungGemäß Produkt- und Qualitätsstandard der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (https://www.adv-online.de/AdV-Produkte/Standards-und-Produktblaetter/Standards-der-Geotopographie/) Softwareempfehlunghttps://citygmlwiki.org BeschreibungDas 3D-Gebäudemodell ist ein Folgeprodukt aus den ALKIS®-Gebäudegrundrissen, dem Digitalen Geländemodell (DGM) und den 3D-Messdaten. Das 3D-Gebäudemodell steht in zwei Detaillierungsgraden zur Verfügung: Level of Detail 1 (LoD1) und Level of Detail 2 (LoD2). Für das 3D-Gebäudemodell LoD1 erfolgt die Modellierung der Gebäude als Blockmodelle (Flachdach). Die niedersachsenweite Herstellung der LoD1-Gebäudemodelle ist seit 2014 abgeschlossen und steht seitdem flächendeckend zur Verfügung. Seit 2019 wird dieses automatisch aus den LoD2-Gebäudemodellen abgeleitet, wofür der Mittelwert aus First- und Traufhöhe als Gebäudehöhe verwendet wird.Die Erzeugung der 3D-Gebäudemodelle erfolgt auf Grundlage von ALKIS® (Gebäudegrundrisse), dem DGM mit 5m Rasterauflösung (Geländehöhe des Gebäudes) und den 3D-Messdaten (Höhenpunkte des Gebäudedaches aus der Laserscann- bzw. Matching-Punktwolke). Die Lagegenauigkeit entspricht der Lagegenauigkeit des zugrundeliegenden Gebäudegrundrisses. Die Höhengenauigkeit beträgt größtenteils 5 m. Grobe Abweichungen sind bei komplexen Dachformen möglich. Neben der Lage- und Körperdarstellung der Gebäude umfassen die LoD1-Daten eine umfassende Anzahl an Attributen. Diese werden durch den Produkt- und Qualitätsstandard (PQS) der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (AdV) vorgegeben. Weiterhin bietet das LGLN zusätzliche Attribute an (s. Legende).Einsatzmöglichkeiten Präsentationen von Bauvorhaben und ihre Auswirkungen auf die UmgebungFunknetz- und VersorgungsleitungsplanungAbbildung realistischer Szenarien im Bereich der Fahrzeugnavigation und FlugsimulationAnalysen und Simulationen von Emissions-, Immisions-, Lärm- und HochwasserausbreitungenErstellung von Sichtbarkeits- und VerschattungsanalysenForschung und Lehre Ausführliche Produktbeschreibung Brauchen Sie Unterstützung?Für fachliche Fragestellungen zu dem Produkt, sehen Sie bitte in das FAQ.In unseren Anleitungen finden Sie weitere Informationen, wie eine STAC-API verwendet werden kann. Für eine schnelle visuelle Darstellung des STAC kann derRadiant Earth STAC-Viewerverwendet werdenFür eine Nutzung der STAC-API in QGIS können Sie das QGIS-Plugin "QGIS STAC API-Browser" verwenden.In ArcGIS Pro können Sie ab der Version 3.2STAC API Verbindungenherstellen. Hierfinden Sie eine Anleitung für den Massendownload. Sind die Daten für Sie hilfreich?Feedback zum Produkt

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

• `Helsinki_Travel_Time_Matrix_2023.csv.zst`: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.
• `Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: a set of 13231 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern `Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv` where `5787545` is replaced by the `to_id` by which the rows in the file are grouped. Use the `from_id` column to join with the geometries from one of the files below.

Geometry, only:

• `Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files.

Table structure

from_id

to_id

walk_avg

walk_slo

bike_avg

bike_fst

bike_slo

pt_r_avg

pt_r_slo

pt_m_avg

pt_m_slo

pt_n_avg

pt_n_slo

car_r

car_m

car_n

walk_d

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations’ results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. ‘Rush hour’ refers to an 1-hour window between 8 and 9 am, ‘midday’ to 12 noon to 1 pm, and ‘nighttime’ to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R⁵ engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for `walk_avg` (as well as the respective `pt_*_walk_avg`), and the slowest quintile of all measured walker across all conditions for `walk_slo` (and the respective `pt_*_walk_slo`).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

A dataset describing exposed bedrock and surficial geology of Antarctica constructed by the GeoMAP Action Group of SCAR (The Scientific Committee on Antarctic Research) and GNS Science, New Zealand. Legacy geological map data have been captured into a geographic information system (GIS), refining its spatial reliability, harmonising classification, then improving representation of glacial sequences and geomorphology. A total 99,080 polygons have been unified for depicting geology at 1:250,000 scale, but locally there are some areas with higher spatial precision. Geological definition in GeoMAP v.2022-08 is founded on a mixed chronostratigraphic- and lithostratigraphic-based classification. Description of rock and moraine polygons employs international GeoSciML data protocols to provide attribute-rich and queriable data; including bibliographic links to 589 source maps and scientific literature. Data are provided under CC-BY License as zipped ArcGIS geodatabase, QGIS geopackage or GoogleEarth kmz files. GeoMAP is the first detailed geological dataset covering all of Antarctica. GeoMAP depicts 'known geology' of rock exposures rather than 'interpreted' sub-ice features and is suitable for continent-wide perspectives and cross-discipline interrogation.

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.

Tjenesten viser en oversikt over hvilke norske sjøområder som har blitt dybdekartlagt/sjømålt. Dybdedata er samlet inn over en lang tidsperiode, av ulike aktører og med ulikt utstyr. I egenskapstabellen til datasettet finnes det opplysninger som måleårstall, kartleggingsmetode, dataeier/hvilken aktør som har samlet inn dataene, utstyr, ekkolodd-type, spesifikasjon og oppløsning. Innholdet i tjenesten er basert på tilgjengelig data hos Kartverket.

Denne tjenesten er basert på OGC-API Features. OGC-API er en gruppe nye standarder som blir utviklet av Open Geospatial Consortium (OGC) for lettere å kunne tilgjengeliggjøre geografiske data på web. Ikke alle GIS-programvarer har etablert støtte til alle de nye standarder, som gjør at f.eks. man må koble seg på med en vector tiles tilkobling til denne tjenesten i bl.a. QGIS.

Bruk oppgitt link sammen med tilkoblingstype vector tiles i QGIS. Merk at det ikke er noen stilsetting på vector tiles.

LRVA-2022: Generated standard map representation of the special topic maps for ecological corridors in Austria for the forest development plan. The maps are automatically updated as soon as the data basis (=modification of ecological corridors or modification forest mask from the Federal Forest Research Centre /BFW) changes. Details see also end report available: https://docs.umweltbundesamt.at/s/AzZrHid6q9wkDDM. QGIS-Modell for the maps generation available here: https://docs.umweltbundesamt.at/s/CjTNNLzPEKbNWyX

Please note: for a correct view and use of this dataset it is advisable to consult it at original page on the Arezzo Portal. At the same address there are also, for the enabled datasets, additional access formats, the preview of the visualization via API call, the consultation of the fields in DCAT-AP IT format, the possibility to express an evaluation and comment on the dataset itself. All resource formats available for this dataset can be downloaded as ZIP packages: inside the package sarà available the resource in the chosen format, complete with all the information on the metadata and the license associated with it. The conceptual model illustrated in the PDF file attached to the metadata sheet refers to the main classes adopted for the representation of the thematic layers in the QGIS project prepared for the realization of the cartographic elaboration referred to in the title of the sheet. The model was created as a diagram of the classes according to the UML language, adopting a reduced set of specifications. The classes represented in the diagram generally have a name coinciding with that of the corresponding dataset of the physical model. In the conceptual model, “classes” that are actually descriptive of layers representing particular thematic sub-sets of another class can also be illustrated by means of specific queries (Provided Feature Filter) and particular categorical representations. For the main classes are highlighted in special labels, with description enclosed by braces {}, the constraints (constraints) defined between the instances of the class and with the instances of the related classes. Additional natural language annotations have been added, including the name of the corresponding QGIS layer, a brief description of the class, and the data source. The colors assigned to the classes illustrated in the UML model are representative of the Spatialite geodatabases in which the corresponding datasets are stored: a descriptive legend of the various reference geodatabases has been reported in the UML model.

Limited availability of P in soils to crops may be due to deficiency and/or severe P retention. Earlier studies that drew on large soil profile databases have indicated that it is not (yet) feasible to present meaningful values for “plant-available” soil P, obtained according to comparable analytical methods, that may be linked to soil geographical databases derived from 1:5 million scale FAO Digital Soil Map of the World, such as the 5 x 5 arc-minute version of the ISRIC-WISE database. Therefore, an alternative solution for studying possible crop responses to fertilizer-P applied to soils, at a broad scale, was sought. The approach described in this report considers the inherent capacity of soils to retain phosphorus (P retention), in various forms. Main controlling factors of P retention processes, at the broad scale under consideration, are considered to be pH, soil mineralogy, and clay content. First, derived values for these properties were used to rate the inferred capacity for P retention of the component soil units of each map unit (or grid cell) using four classes (i.e., Low, Moderate, High, and Very High). Subsequently, the overall soil phosphorus retention potential was assessed for each mapping unit, taking into account the P-ratings and relative proportion of each component soil unit. Each P retention class has been assigned to a likely fertilizer P recovery fraction, derived from the literature, thereby permitting spatially more detailed, integrated model-based studies of environmental sustainability and agricultural production at the global and continental level (< 1:5 million). Nonetheless, uncertainties remain high; the present analysis provides an approximation of world soil phosphorus retention potential. The files are provided in ArcGIS and QGIS format.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.