Summary of topics to be covered in an ideal workshop as identified by workshop applicants in the workshop call for participation. We incorporated as many as possible that also fit our scope.

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 with a laser tape measurer). We demonstrate how this method can be used to answer research questions about space-use behaviors in captive animals, using 6 ewe-lamb pairs housed in a group pen. As predicted, we found that lambs maintained closer proximity to their dam compared to other ewes in the group and lamb-dam range sizes were strongly correlated. However, the distance traveled by lambs and their dams did not correlate, suggesting that activity levels differed within the pair. This method demonstrates how user-friendly, open-source GIS tools can be used to accurately estimate animal location and derive space-use behaviors from non-overhead video frames. This method will expand capacity to obtain spatial data from animals in facilities where it is not possible to mount cameras overhead.

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.

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).

The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

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

This collection contains georeferenced raster images (in GeoTIFF format) of the 'Gewestkaarten' (Regional maps) associated with Jacob van Deventer.

The georeferencing has been accomplished by linking hundreds of locality-pointers in the maps to the respective church towers. Using these pointers, the maps were distorted using Thin Plate Spline (Transformation method) / Nearest Neighbour (Resampling method).
Some regional maps, like the map of the County of Zeeland and surroundings, are so well executed that this method works really well without distorting the raster image too much. The map of the Duchy of Brabant (the oldest 'Gewestkaart' by Jacob van Deventer), is much less precise (with substantial intraregional differences). Only around half of the Brabant localities could be used as pointers in georeferencing in order to keep the distortion at an acceptable level.

The maps included in the dataset are:

• Jacob Bos, Flandriae recens exactaque descriptio (Rome: Michaelis Tramezini 1555). Based (partly?) on measurements by Jacob van Deventer and a map by Gerard Mercator, originally published around 1540. Amsterdam, Rijksmuseum, RP-P-BI-2756.
• Jacob Bos, Hollandiae Batavorum veteris insulae et locorum adiacentium exacta descriptio (Rome: Michaelis Tramezini 1556). Based on a map by Jacob van Deventer, originally published in 1542. Haarlem, Noord-Hollands Archief, NL-HlmNHA_560_000399_G.
• Jacob Bos, Brabantia Belgarum provinciae recens exactaque descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1536. Amsterdam, Rijksmuseum, RP-P-BI-2758. A different edition, similar to the lost Breslau edition of 1558 (link 1, 2, 3, 4), can be found in the Brussels Royal Library (circa 1556).
• Jacob Bos, Gelriae, Cliviae, Iuliae, nec non aliarum regionum adiacentium nova descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1543 (?). Amsterdam, Rijksmuseum, RP-P-BI-2757.
• Jacob Bos, Frisiae antiovissimae trans Rhenum provinc. et adiacentium regionum nova et exacta descriptio (Rome: Michaelis Tramezini 1558). Based on a map by Jacob van Deventer, originally published in 1545. Amsterdam, Rijksmuseum, RP-P-BI-2759.
• Jacob van Deventer, Zelandia (Antwerp: Gulielmus Sylvius 1560). Originally published in 1545 (or 1546?). Brielle, Streekarchief Voorne-Putten, 620 Collectie Topografische Kaarten, G058 ('TA_ALG_208 Kaart van Zeeland anno 1560. Komt voor een groot deel overeen met een kaart uit 1546 van Jacob van Deventer').

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").

Download file containing two raster images in png format, accompanied by world file (georeferencing file) and style file for QGis. The first png format image is a squared representation with pink coloring of areas with slopes greater than 30°. The second image in PNG format is a representation of squares themed on four classes of areas with slopes greater than 30°. The download file also includes the transformation of areas with slopes greater than 30° from a raster image to polygons contained in a file in shape format.

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

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 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.

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.

This dataset represents the first extensive global inventory of coastal springs, including both nearshore springs, discharging close to the shoreline, and offshore springs, also known as submarine springs, discharging directly onto the ocean floor. The data was gathered through a systematic bibliographic search following PRISMA guidelines. The final list of studies used to compile the data is presented in the Excel file "References". The spring’s locations were identified directly from documents, and in cases where maps were provided, they were extracted, georeferenced in QGIS, and their coordinates recorded. Additional data on coastal springs, including altitude, lithology, discharge patterns, discharge rates, and salinity, were collected when available. In total, 1,123 springs were identified, comprising 645 offshore springs and 478 nearshore springs, with a significant concentration in the Mediterranean region. The accuracy of each spring's location was verified using Google Earth. To provide context, various geological, hydrological, climatic, land use, and oceanic variables of the coastal watersheds where the springs are located (listed in the Excel file "CWD") were extracted from available global datasets. The inventoried springs and related coastal watersheds are also provided in ESRI shapefile format for quick visualization in GIS platforms. Most of these springs are located in Europe and North America, with fewer found in Africa, South America, and southern Asia. This dataset is valuable for hydrogeologists investigating the dynamics of coastal springs across diverse climatic, hydrological, and hydrogeological settings. By analysing the context of these coastal springs, researchers and water managers can identify potential zones for coastal springs and incorporate them into water resource assessments and vulnerability studies. Additionally, the dataset serves as a crucial resource for calibrating and validating geospatial methods used for identifying springs, such as remote sensing techniques. This dataset is intended to serve as a foundational resource for the development of a more detailed global inventory of coastal springs, and it welcomes contributions and updates from researchers worldwide.

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

Freshwater Fishes and Lampreys of Greece: An Annotated Checklist

The Red Book of Endangered Animals of Greece

The "Red List of Threatened Species"

The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"

The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.

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

Georeferenced (to WGS1984) and cropped set of about 820 historic maps of Burma at a scale of 1 inch per mile (63,360) covering about 75% of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1899 and 1946, have been scanned and shared with the public as part of the "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Many of these maps are reprints of earlier maps produced before the war. Most mapsheets are early editions (edition 1 or edition 2).

Each of the 820 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 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data.

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

• The mm_OI_JBv2024 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.
  
  • The mm_OI_JBv2024_kmlLinks contains kml files to easily load the mapsheets into Google Earth
  • The mm_historicOI_EPSG4326.gdb contains an ESRI mosaic dataset to easily load all mapsheets into ArcGIS
• The mm_OI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.
• The mm_topoOI_JBv7_masterlist.xlsx is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared as part of the "Old Survey Of India Maps" (e.g. to identify new mapsheets should new maps be released)
• The indexMaps folder contains small scale index maps to locate the map sheets using their map sheet Grid-Letter-nomenclature

All georeferenced map scans are based on maps shared by John Brown via Zenodo

• https://zenodo.org/records/8040798 (63k Maps of Burma, version 7, Published June 14, 2023)
• https://zenodo.org/records/10463372 (63k Maps of Burma--additional 1--20240105, version 1, Published January 5, 2024)

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 a number from 1 to 16 to indicate the number of the map in the 1 degree block.

This Number Letter Number designation is followed by the map series type either OI (contains a LCC grid) or OILatLon (only has a Lat-Lon grid), followed by the edition and year of the edition, followed by the date of publication/print. If the information is not available an "X" (for edition) or "0000" (for an unknown year) is used. A best-guess approach was used if the edition and print year and version information was ambiguous.

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

A topographical index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.

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.