This is the dataset of the ICDAR 2021 conference paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction".

This repository contains data used for our article, "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910". The README.md file explains the contents. This data can also be found at https://github.com/opengulf/ottoman-map.

Biogeoclimatic Ecosystem Classification (BEC) has been applied extensively in characterizing forested ecosystems in British Columbia. With a lack of qualified vectorization method used for BEC data transformation, the main goal of this research is to polygonize discontinuous BEC raster classes into vector map with better overall effectiveness and efficiency especially regarding the linear areas. The original data input for analysis is a machine-learning BEC zone raster map of Deception Study Area located in middle BC near Telkwa, with a resolution of 5m*5m. A comprehensive comparison between vectorization algorithms in GIS applications was conducted, including different filtering, simplifying and smoothing algorithms. Since we have the original predicted BEC raster map as the performance measurement, accuracy was directly measured as the percentage of correctly classified pixels when rasterizing the polygons. The evaluation criteria include visual effect, number of polygons, linear patches accuracy processing time. We found an appropriate vectorization routine to polygonize the classification raster maps. The polygonal map using Scenario D has overall satisfactory effectiveness and efficiency with a 46% linear patch accuracy and 62,014 polygons. The method also provides good approximations of the areas with moderate processing time. This is partly because we allow vertices to be located anywhere and not just exactly on the boundary of the original raster zones. We can promote this polygonization method in future predicted ecosystem mapping (PEM) product with similar linear and discontinuous areas. Priority of several key BEC zone classification with importance level regarding to the ecosystem condition related to endangered species can be further explored and added to the algorithms to better polygonize those areas in future studies.

The set contains vector data of the 1780 timeline which is derived from the Schmettau map and missing parts in the Northeast of the Uckermark region are added from the Prussian First Land Survery (Preußische Uraufname). The original map sheets are stored in the Map Section of the National State Library in Berlin. A set of forest data from these maps was bought from the Eberswalde Forestry State Centre of Excellence (LFE). These data were checked and partly adjusted to our own field experiences and all other land-cover types were added by digitizing.

Borders of the old Uckermark from: Fidicin E (1864) Die Territorien der Mark Brandenburg oder Geschichte der einzelnen Kreise, Städte, Rittergüter und Dörfer in derselben als Fortsetzung des Landbuchs Kaiser Karl’s, Vol. IV. Bd. 4: Kreis Prenzlau. Kreis, Templin. Kreis Angermünde. Reprint in 1974, de Gruyter, Berlin

The NOAA Composite Shoreline is primarily intended for high-resolution cartographic representation of the shoreline. It is a high-resolution vector shoreline based on a multi-temporal collection of NOAA coastal survey maps (T-sheets). Where T-sheets were unavailable, NOAA's extracted vector shoreline (EVS) was used to compile seamless shoreline coverage.

Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on January 16-18, 2014 over Breton Island, Louisiana and released under USGS field activity number 14LGC01. Quantum Spatial was contracted by the USGS to collect and process these data. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

Geological map GEOCR50 is a unique geographic information system, which has as its main parts database of the digitized and harmonized geological maps at a scale of 1 : 50,000 and database of the unified geological legend for the whole Czech Republic. This legend contains 4 basic types of information: chronostratigraphical units (classification), regional units (classification), lithological description of rocks and lithostratigraphical units (classification). GeoCR50 contains more than 260 000 mapped geological units from the whole area of the Czech Republic.

NOS coastal survey maps (often called t-sheet or tp-sheet maps) are special use planimetric or topographic maps that precisely define the shoreline and alongshore natural and man-made features, such as rocks, bulkheads, jetties, piers, and ramps. These maps typically range in scale from 1:5,000 to 1:40,000. The first shoreline survey was completed in 1834. Since the early 1800's, over 12,000 co...

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

The Charm-50 DB is the georeferenced database of 1/50,000 vectorized and harmonized geological maps. It comprises six layers of vector digital data: geological formations, contours, linear structural elements, point elements (structural and various), overloads (mylonites...). Scientific, technical and pedagogical use.

description: A first-surface elevation map was produced cooperatively from remotely sensed, geographically referenced elevation measurements collected by the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) on September 07-09, 2001. Elevation measurements were collected over the area using the NASA Airborne Topographic Mapper (ATM), a scanning lidar system that measures high-resolution topography of the land surface. The ATM system is deployed on a Twin Otter or P-3 Orion aircraft and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0.; abstract: A first-surface elevation map was produced cooperatively from remotely sensed, geographically referenced elevation measurements collected by the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) on September 07-09, 2001. Elevation measurements were collected over the area using the NASA Airborne Topographic Mapper (ATM), a scanning lidar system that measures high-resolution topography of the land surface. The ATM system is deployed on a Twin Otter or P-3 Orion aircraft and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0.

Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana and published in USGS Data Series 838. Photo Science, Inc., was contracted by the USGS to collect and process these data. Lidar data were acquired around portions of both the Alabama and Louisiana barrier islands; however, this dataset only contains shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

We use the MWS as a post-processing without filtering on Deep Watershed outputs to thin the prediction edges. The following parameters are static, and their respective columns are hidden: we use our proposed training configuration, the loss function is the binary cross entropy, no augmentation is performed, and DEF selection is performed with Joint Optimization (JO).

The following parameters are static, and their respective columns are hidden: we use the Meyer Watershed (MWS) for CSE and Joint Optimization (JO) for DEF selection, we use our proposed training configuration, the loss function is the binary cross entropy, no augmentation is performed. For the architectures, * indicates pre-trained variants.

The Digital Geologic-GIS Map of the Center Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (cent_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (cent_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (cent_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cent_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey and Kentucky Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cent_geology_metadata.txt or cent_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Position System (GPS) data collected by the Grand Bay National Estuarine Research Reserve and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This d ...

The Digital Geologic-GIS Map of Mammoth Cave National Park and Vicinity, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (maca_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (maca_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (maca_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (maca_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey and Kentucky Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (maca_geology_metadata.txt or maca_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The following parameters are static, and their respective columns are hidden: model architecture is U-Net (trained from scratch), we use the improved training variant, the loss function is the binary cross entropy, the best DEF is selected using joint optimization, and Meyer Watershed (MWS) is used for CSE.

Shorelines were derived from a U.S. Geological Survey Earth Resources Observations and Science Center (EROS) high-resolution orthorectified image that was collected on October 20, 2012 over Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0.

Vectorized 10-by-10 minute Grid (Polygon). The CRU_PY shapefile data layer is comprised of 92718 derivative calculated climate features derived based on 0.167 degrees resolution data originally from CRU. The layer provides nominal analytical/mapping at 1:70 000 000. Acronyms and Abbreviations: CRU - Climatic Research Unit - School of Environmental Sciences, University of East Anglia - Norwich.