ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Tracking an animal's location from video has many applications, from providing information on health and welfare to validating sensor-based technologies. Typically, accurate location estimation from video is achieved using cameras with overhead (top-down) views, but structural and financial limitations may require mounting cameras at other angles. We describe a user-friendly solution to manually extract an animal's location from non-overhead video. Our method uses QGIS, an open-source geographic information system, to: (1) assign facility-based coordinates to pixel coordinates in non-overhead frames; 2) use the referenced coordinates to transform the non-overhead frames to an overhead view; and 3) determine facility-based x, y coordinates of animals from the transformed frames. Using this method, we could determine an object's facility-based x, y coordinates with an accuracy of 0.13 ± 0.09 m (mean ± SD; range: 0.01–0.47 m) when compared to the ground truth (coordinates manually recorded..., Please see the description in the associated research publication., Please see the included README file.

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Georeferencing the "Atlas du plan général de la ville de Paris par Edme Verniquet" Géoréférencement de l'Atlas du plan général de la ville de Paris par Edme Verniquet This dataset contains the necessary data control points to georeference the "Atlas du plan général de la ville de Paris par Edme Verniquet" based on 2 different versions of the atlas: one digitized by the Bibliothèque nationale de France (BnF) and the other by The David Rumsey Historical Map Collection. The dataset contains the control points in QGIS format (.points files) and as Allmaps georeference annotations. It also contains the georeferenced map sheets as geotiff.

Summary of desired future workshop topics that were listed by participants on the last day of the workshop.

This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

Three months after the workshop, participants were surveyed to assess what workshop-related knowledge and materials were being used and disseminated to others. This document summarized data collected in this particular survey.

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation. European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788. The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel. Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe. Dataset Record The forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB. The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given. In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below. Code Availability For the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629. Acknowledgements The computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

The data sets include 10 scanned historical maps (Plan 1-9 and 11) from 1920 of the region of Grosses Moos in Switzerland. The maps have been drawn by different surveyors with the plane table principle in 1920 represented with a map including topographical information such as contour lines, water bodies and houses and other urban infrastructures. In addition, the original measurement points were marked with a grid of 20-30m spacing and additional points if needed. These individual points were digitized by first georeferencing the individual maps with QGIS and then digitising each single measurement point along with their respective recorded heights. 44319 points were digites in LV03 (Swiss coordinate system) with the corresponding height (stored in the file "HoehendatenPunktwolke_Ins_1920.geojson").

Overview

This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a collaborative drone campaign organized by the Urban Transport Systems Laboratory (LUTS), EPFL, within the framework of NCCR Automation, in cooperation with the cantonal traffic planning department of Aargau. The collection took place on Monday, 23rd October 2023, during peak morning and afternoon hours, resulting in nearly 11 hours of 4K video data.

Dataset Composition

This dataset contains CSV files structured with consistent data fields representing georeferenced trajectories, vehicle types (car, bus, truck), and timestamps, capturing detailed vehicle movements within roundabout environments.

File Organization

File names follow the convention:

D{X}_{TP}{N}_{S}.csv

D{X} — the drone identifier, where {X} is a number (e.g., 1, 2) indicating which drone captured the data.→ Example: D1 = data collected by Drone 1.

{TP}{N} — the time period and session number, where {TP} is either AM (morning) or PM (afternoon), and {N} is an integer indicating the session number.→ Example: AM2 = second morning session.

{S} — the site identifier, corresponding to one of the monitored sites:→ F1 = Roundabout F1 (Frick)→ F2 = Roundabout F2 (Frick)→ L1 = Roundabout L1 (Laufenburg)

CSV File Structure

Each CSV file includes:

Column Name Description Format / Units

track_id Unique vehicle identifier (per file) Integer

type Vehicle type (Car, Bus, Truck) Categorical

lon WGS84 geographic longitude Decimal degrees (15 d.p.)

lat WGS84 geographic latitude Decimal degrees (15 d.p.)

time Local timestamp in ISO 8601 format String (hh:mm:ss.ss)

Data Collection and Processing

Collection Method: Two drones flying at an altitude of 120 meters above ground level, capturing videos at 4K resolution (3840×2160 pixels) at 29.97 FPS.

Locations:

Roundabout F1 (Frick): Intersection of Bahnhofstrasse and Hauptstrasse 3 (Urban)

Roundabout F2 (Frick): Intersection of Hauptstrasse 3 with Gänsacker and Stöcklimattstrasse (Urban)

Roundabout L1 (Laufenburg): Intersection at Hauptstrasse 7 near the German border (Rural)

Data Processing: The detection, tracking, and trajectory stabilization were performed using the early version of the Geo-trax framework (v0.1.0), an advanced computer vision pipeline tailored for drone-captured traffic footage. The resulting trajectories are precisely represented in stabilized pixel coordinates, which are subsequently transformed into geographic coordinates (WGS84). This georeferencing process follows a procedure similar to that described in Espadaler-Clapés et al., 2023, and includes:

Identification and extraction of Ground Control Points (GCPs) in the first stabilized video frame using QGIS Georeferencer, linking pixel coordinates to UTM coordinates.

Linear regression modeling between stabilized pixel coordinates and corresponding UTM coordinates derived from GCPs to estimate transformation parameters.

Projection to WGS84, converting UTM coordinates into global geographic coordinates using a standard GIS transformation (EPSG:4326).

Dataset Statistics

Roundabout Videos Avg. Duration (min) Total Duration (min) Vehicles (total) Cars Buses Trucks

F1 8 18.63 149.04 4,283 3,967 72 244

F2 6 19.24 115.44 2,528 2,205 26 297

L1 4 20.39 81.56 2,130 1,980 24 126

Potential Applications

This dataset is well-suited for:

Gap acceptance behavior studies at roundabouts (e.g., Pascual Anglès et al., 2025)

Traffic flow analysis and modeling

Safety assessments using surrogate safety measures (SSMs)

Validation of traffic simulation models

OC is outcome-cluster uniqueness and CR is cluster-range uniqueness.Performance Metrics When Predicting Infiltration & Drainage.

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, Google Earth, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to detect species and track their population dynamics remains under-explored. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving two major Mediterranean invasive plant taxa with contrasting growth forms. This dataset contains the step-by-step protocol to perform retrospective image analysis using Google Earth Imagery, including writen protocols, videotutorials and the data. A ReadMe is found in the folder explaining all folder's contents, whereas a WatchMe has been recorded to perform an analogous function in the Youtube playlist including all videotutorials: https://www.youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDD Our pipeline opens new avenues for cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.The repository contains 4 folders (Data, Code, Protocols and Videos), acompaigned by a ReadMe.txt file with further details about the contents.

This layer shows soils in south Karamoja in 1959. The dataset is from the European Digital Archives of Soil Maps(EuDASM) 2005 at http://eusoils.jrc.ec.europa.eu/esdb_archive/eudasm/africa/images/maps/download/afr_ug3004_1so.jpg. The map was georeferenced using QGIS and reprojected to WGS84.

Georeferenced (to WGS1984) and cropped set of about 555 historic maps of Burma at a scale of 1 inch per two miles (1:126,720) covering most of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1878 and 1949, have been scanned and shared with the public as "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence.

Each of the map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG:4326) - standard GPS - projection to make them easier to use and combine with other GIS data.

Many grid cells in this dataset are covered by 2 versions of map sheets - those with hill shade and only lat-lon grid and those without hill shade and featuring a LCC map grid.

Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS.

• The mm_HI_JBv2024_epsg4326 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.
  
  • The mm_HI_JBv2024_epsg4326_kmlLinks contains a KML file for each map sheet facilitating their easy use in Google Earth byt linking them the georeferenced map sheet file located in the mm_HI_JBv2024_epsg4326 folder.
  • The mm_historicHI_EPSG4326.gdb contains three ESRI mosaic datasets to easily load all mapsheets, only mapheets with hillshading and lat-lon grid and only "regular" mapsheets without hillshading and LCC grid into ArcGIS
• The mm_HI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.
• The mm_historicTopoHI_JBv2024 is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared via the "Old Survey Of India Maps” Community (e.g. to identify new mapsheets should new maps be released)

All georeferenced map scans are based on maps shared as part of the "Old Survey Of India Maps” via Zenodo. Links to each file can be found in the above mentined excel file and most can be also accessed through the zenodo repository below.

• https://zenodo.org/records/8040798 (128k Maps of South Asia, version 4, Published May 3, 2023)

The file naming convention is to first give the number of the 4 degree x 4 degree block followed by the letter (A to P) of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by the cardinal direction letters (NE, NW, SE, SW) to indicate the 30x30 minutes sized map position in the 1 degree block.

This Number - Letter - Cardinal direction letter designation is followed by the year of the edition, followed by the map series type either HI-hs (hillshaded) or HI-reg (regular), followed by the map sheet title/name.

The original files as shared as part of the "Old Survey Of India Maps” have been renamed to further standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available.

Lineage: This version (1.01, Upload 2024-08-20) has some file attributes fixed.

This layer shows soils in 1967. The dataset source is the European Digital Archives of Soil Maps at http://eusoils.jrc.ec.europa.eu/esdb_archive/eudasm/africa/images/maps/download/afr_ug2001so.jpg The map was georeferenced using QGIS, the projection is WGS84.

Polygon dataset showing the 6 counties of Northern Ireland e.g. County Armagh, County Tyrone etc which were the primary local government geography of Northern Ireland before the introduction of unitary authorities in 1972. A PNG map showing the Northern Ireland county boundaries was downloaded from wikipedia: http://en.wikipedia.org/wiki/File:Northern_Ireland_-_Counties.png The PNG was georeferenced in QGIS using control points with reference to an OGL dataset downloaded from the UK Data Service showing the Northern Ireland coastline. Internal county boundaries were digitised from the georeferenced PNG as a set of polylines. These polylines were then snapped to the coastline features and polygons were generated. A county name was then assigned to each polygon in the attribute table. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-02-24 and migrated to Edinburgh DataShare on 2017-02-22.

This dataset focuses on the historical mapping of the Greater Donaumoos fen region using old maps spanning the last 235 years. The main observations include the georeferencing of these historical maps and the subsequent vectorisation of the anthropogenic ditches and the Danube's surface area. The data collection encompasses maps spanning multiple centuries, providing temporal coverage that highlights landscape changes over significant historical periods. The data was collected to enhance archaeological, historical, and ecological research, offering insights into past landscapes and their transformations over time. The method involved digitising old maps and applying geospatial techniques to align them accurately with current geographical coordinates (Schmidt et al., 2024). This process was essential to create vector data representing the historical state of the ditches and the Danube river in this region. The purpose of this data collection is to provide a valuable resource for researchers studying historical land use, environmental changes, and regional development. The georeferencing and vectorisation processes were conducted using QGIS, ensuring precise alignment and accurate representation of historical features. The data generated from this project is crucial for understanding how the Greater Donaumoos fen region has evolved, offering a foundational dataset for further interdisciplinary studies.

Example RGB and Multispectral data for processing with Agisoft Metashape and QGIS to create georeferenced orthomosaics and classifications.

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation.European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788.The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel.Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe.Dataset RecordThe forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below.Code AvailabilityFor the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).