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.

Special opportunity areas of the Fifth Planning District in Roanoke Valley/Alleghany Virginia, 1975. This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); http://rvarc.org/ , http://lva1.hosted.exlibrisgroup.com/F?func=find-b&find_code=WRD&request=fifth+planning+district+commission. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000059.zip.The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

This is a dataset of georeferenced 1917 Sanborn Fire Insurance maps of Knoxville TN, including individual sheets, a sheet index, a seamless mosaic, and a map key. Digital images of the data sheets were downloaded from the University of Tennessee Library https://digital.lib.utk.edu/collections/sanbornmapcollection. Multi-part sheets were clipped into pieces for georeferencing. Chris DeRolph georeferenced each sheet and piece, where possible. There were a few outlying images that were unable to be georeferenced due to lack of recognizable common features between the sheets and reference maps/imagery in the sheet vicinity. The sheet index shapefile includes a field with a hyperlink to the UTK library download page for the sheet. The seamless mosaic was created using the Mosaic to New Raster tool in ArcGIS Pro with all georeferenced sheets/pieces as inputs and the Minimum Mosaic Operator. No attempt was made prior to the mosaicking process to remove sheet numbers, scale bars, north arrows, overlapping labels/annotation, etc. Viewing individual sheets will provide the cleanest look at an area, while the seamless mosaic provides the most comprehensive view of the city at the time the maps were created.

Major thoroughfare plan of the Fifth Planning District in Roanoke Valley/Alleghany Virginia, 1975. This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); http://rvarc.org/ , http://lva1.hosted.exlibrisgroup.com/F?func=find-b&find_code=WRD&request=fifth+planning+district+commission. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000065.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

This two-part video tutorial provides a comprehensive, step-by-step guide to creating and georeferencing a 3D textured model of a historic building using Reality Capture. It covers the entire process, from photo alignment and importing GPS data from a text file to identifying and using ground control points (GCPs) to improve the alignment of model components and accurately georeference the point cloud. The tutorial also demonstrates model creation, cleaning, simplification, and texturing. In the final steps, the model is exported and imported into ArcGIS Pro for geographic analysis and visualization.

Critical environmental areas of thel Fifth Planning District in Roanoke Valley/Alleghany Virginia, 1975. This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); http://rvarc.org/ , http://lva1.hosted.exlibrisgroup.com/F?func=find-b&find_code=WRD&request=fifth+planning+district+commission. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000062.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

Purpose:This feature layer describes water quality sampling data performed at several operating coal mines in the South Fork of Cherry watershed, West Virginia.Source & Data:Data was downloaded from WV Department of Environmental Protection's ApplicationXtender online database and EPA's ECHO online database between January and April, 2023.There are five data sets here: Surface Water Monitoring Sites, which contains basic information about monitoring sites (name, lat/long, etc.) and NPDES Outlet Monitoring Sites, which contains similar information about outfall discharges surrounding the active mines. Biological Assessment Stations (BAS) contain similar information for pre-project biological sampling. NOV Summary contains locations of Notices of Violation received by South Fork Coal Company from WV Department of Environmental Protection. The Quarterly Monitoring Reports table contains the sampling data for the Surface Water Monitoring Sites, which actually goes as far back as 2018 for some mines. Parameters of concern include iron, aluminum and selenium, among others.A relationship class between Surface Water Monitoring Sites and the Quarterly Monitoring Reports allows access to individual sample results.Processing:Notices of Violation were obtained from the WV DEP AppXtender database for Mining and Reclamation Article 3 (SMCRA) Permitting, and Mining and Reclamation NPDES Permitting. Violation data were entered into Excel and loaded into ArcGIS Pro as a CSV text file with Lat/Long coordinates for each Violation. The CSV file was converted to a point feature class.Water quality data were downloaded in PDF format from the WVDEP AppXtender website. Non-searchable PDFs were converted via Optical Character Recognition, so that data could be copied. Sample results were copied and pasted manually to Notepad++, and several columns were re-ordered. Data was grouped by sample station and sorted chronologically. Sample data, contained in the associated table (SW_QM_Reports) were linked back to the monitoring station locations using the Station_ID text field in a geodatabase relationship class.Water monitoring station locations were taken from published Drainage Maps and from water quality reports. A CSV table was created with station Lat/Long locations and loaded into ArcGIS Pro. It was then converted to a point feature class.Stream Crossings and Road Construction Areas were digitized as polygon feature classes from project Drainage and Progress maps that were converted to TIFF image format from PDF and georeferenced.The ArcGIS Pro map - South Fork Cherry River Water Quality, was published as a service definition to ArcGIS Online.Symbology:NOV Summary - dark blue, solid pointLost Flats Surface Water Monitoring Sites: Data Available - medium blue point, black outlineLost Flats Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineLost Flats NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Surface Water Monitoring Sites: Data Available - medium blue point, black outlineBlue Knob Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineBlue Knob NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Biological Assessment Stations: Data Available - medium green point, black outlineBlue Knob Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Surface Water Monitoring Sites: Data Available - medium blue point, black outlineRocky Run Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineRocky Run NPDES Outlet Monitoring Sites - orange point, black outlineRocky Run Biological Assessment Stations: Data Available - medium green point, black outlineRocky Run Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Stream Crossings: turquoise blue polygon with red outlineRocky Run Haul Road Construction Areas: dark red (40% transparent) polygon with black outlineHaul Road No 2 Surface Water Monitoring Sites: Data Available - medium blue point, black outlineHaul Road No 2 Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineHaul Road No 2 NPDES Outlet Monitoring Sites - orange point, black outline

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.

This data release includes digital geographic information system (GIS) data which was extracted from the U.S. Geological Survey Professional Paper 1300, volumes 1 and 2. Included are 14 shapefiles comprised of points, lines, and polygons representing the items shown in the page size figures from the reports, and one .lyrx symbology file for use in ArcGIS Pro. Also included are 364 georeferenced images of the report figures, 332 of which are figures for individual study areas, from which the shapefiles were digitized, and 32 of which are state index maps. The georeferenced images are named with the figure number or state name, and page number from the source report. Features represented in the shapefiles include figure map extents, study areas, assessment potential areas, deposits, occurrences, mines, drill holes, wells, springs, volcanic features, sample sites, geology, and geologic structures that were shown in the report figures. The .lyrx file is included to assist in the symbolizing of the geologic structures line shapefile. The data digitized from the georeferenced figures were assumed correct, and very little accuracy checking was completed.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Dataset description: This repository contains data pertaining to the manuscript "Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system." submitted to Journal of Maps. NOAH-H Mosaics: Mawrth_Vallis_NOAHH_Mosaic_DC_IG_25cm4bit_20230121_reclass.zip This folder contain mosaics of terrain classifications for Mawrth Vallis, Mars, made by the Novelty or Anomaly Hunter - HiRISE (NOAH-H) deep learning convolutional neural network developed for the European Space Agency (ESA) by SCISYS Ltd. In coordination with the Open University Planetary Environments Group. These folders contain the NOAH-H mosaics, as well as ancillary files needed to display the NOAH-H products in geographic information software (GIS). Included are two large raster datasets, containing the NOAH-H classification for the entire study area. One uses the 14 descriptive classes of the terrain, and the other with the five interpretative groups (Barrett et al., 2022). · Mawrth_Vallis_NOAHH_Mosaic_DC_25cm4bit_20230121_reclass.tif Contains the full 14 class “Descriptive Classes” (DC) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. · Mawrth_Vallis_NOAHH_Mosaic_IG_25cm4bit_20230121_reclass.tif Contains the 5 class “Interpretive Groups” (IG) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. Symbology layer files: NOAH-H_Symbology.zip This folder contains GIS layer file and colour map files for both the Descriptive Classes (DC) and interpretive Groups (IG) versions of the classification. These can be applied to the data using the symbology options in GIS. Georeferencing Control points: Mawrth_Vallis_Final_Control_Points.zip This file contains the control points used to georeferenced the 26 individual HiRISE images which make up the mosaic. These allow publicly available HiRISE images to be aligned to the terrain in Mawrth Vallis, and thus the NOAH-H Mosaic. Twenty-six 25 cm/pixel HiRISE images of Mawrth Vallis were used as input for NOAH-H. These are:

PSP_002140_2025_RED

PSP_002074_2025_RED

ESP_057351_2020_RED

ESP_053909_2025_RED

ESP_053698_2025_RED

ESP_052274_2025_RED

ESP_051931_2025_RED

ESP_051351_2025_RED

ESP_051219_2030_RED

ESP_050217_2025_RED

ESP_046960_2025_RED

ESP_046670_2025_RED

ESP_046525_2025_RED

ESP_046459_2025_RED

ESP_046314_2025_RED

ESP_045536_2025_RED

ESP_045114_2025_RED

ESP_044903_2025_RED

ESP_043782_2025_RED

ESP_043637_2025_RED

ESP_038758_2025_RED

ESP_037795_2025_RED

ESP_037294_2025_RED

ESP_036872_2025_RED

ESP_036582_2025_RED

ESP_035804_2025_RED NOAH-H produced corresponding 25 cm/pixel rasters where each pixel is assigned a terrain class based on the corresponding pixels in the input HiRISE image. To mosaic the NOAH-H rasters together, first the input HiRISE images were georeferenced to the HRSC basemap (HMC_11E10_co5) tile, using CTX images as an intermediate step. High order (spline, in ArcGIS Pro 3.0) transformations were used to make the HiRISE images georeference closely onto the target layers. Once the HiRISE images were georeferenced, the same control points and transformations were applied to the corresponding NOAH-H rasters. To mosaic the georeferenced NOAH-H rasters the pixel values for the classes needed to be changed so that more confidently identified, and more dangerous, classes made it into the mosaic (see dataset manuscript for details. To produce a HiRISE layer which fits the NOAH-H classification, download one of the listed HiRISE images from https://www.uahirise.org/, Select the corresponding control point file from this archive and apply a spline transformation through the GIS georeferencing toolbar. Manually Mapped Aeolian Bedforms: Mawrth_Manual_TARs.zip The manually mapped data was produced by Fawdon, independently of the NOAH-H project, as an assessment of “Aeolian Hazard” at Mawrth Vallis. This was done to inform the ExoMars landing site selection process. This file contains two GIS shape files, containing the manually mapped bedforms for both the entire mapping area, and the HiRISE image ESP_046459_2025_RED where the two datasets were compared on a pixel scale. The full manual map is offset slightly from the NOAH-H, since it was digitised from bespoke HiRISE orthomosaics, rather than from the publicly available HiRISE Red band images. It is suitable for comparison to the NOAH-H data with 100m-1km aggregation as in figure 8 of the associated paper. It is not suitable for pixel scale comparison. The map of ESP_046459_2025_RED was manually georeferenced to the NOAH-H mosaic, allowing for direct pixel to pixel comparisons, as presented in figure 6 of the associated paper. Two GIS shape files are included: · Mawrth_Manual_TARs_ESP_046459_2025.shp · Mawrth_Manual_TARs_all.shp Containing the high fidelity data for ESP_046459_2025, and the medium fidelity data for the entire area respectively. The are accompanied by ancillary files needed to view them in GIS. Gridded Density Statistics This dataset contains gridded density maps of Transverse Aeolian Ridges and Boulders, as classified by the Novelty or Anomaly Hunter – HiRISE (NOAH-H). The area covered is the runner up candidate ExoMars landing site in Mawrth Vallis, Mars. These are the data shown in figures; 7, 8, and S1. Files are presented for every classified ripple and boulder class, as well as for thematic groups. These are presented as .shp GIS shapefiles, along with all auxiliary files required to view them in GIS. Gridded Density stats are available in two zip folders, one for NOAH-H predicted density, and one for manually mapped density. NOAH-H Predicted Density: Mawrth_NOAHH_1km_Grid_TAR_Boulder_Density.zip Individual classes are found in the files: · Mawrth_NOAHH_1km_Grid_8TARs.shp · Mawrth_NOAHH_1km_Grid_9TARs.shp · Mawrth_NOAHH_1km_Grid_11TARs.shp · Mawrth_NOAHH_1km_Grid_12TARs.shp · Mawrth_NOAHH_1km_Grid_13TARs.shp · Mawrth_NOAHH_1km_Grid_Boulders.shp Where the text following Grid denotes the NOAH-H classes represented, and the landform classified. E.g. 8TARs = NOAH-H TAR class 8. The following thematic groups are also included: · Mawrth_NOAHH_1km_Grid_8_11continuousTARs.shp · Mawrth_NOAHH_1km_Grid_12_13discontinuousTARs · Mawrth_NOAHH_1km_Grid_8_10largeTARs.shp · Mawrth_NOAHH_1km_Grid_11_13smallTARs.shp · Mawrth_NOAHH_1km_Grid_8_13AllTARs.shp When the numbers denote the range of NOAH-H classes which were aggregated to produce the map, followed by a description of the thematic group: “continuous”, “discontinuous”, “large”, “small”, “all”. Manually Mapped Density Plots: Mawrth_Manual_1km_Grid.zip These GIS shapefiles have the same format as the NOAH-H classified ones. Three datasets are presented for all TARs (“_allTARs”), Continuous TARs (“_con”) and Discontinuous TARs (“_dis”) · Mawrth_Manual_1km_Grid_AllTARs.shp · Mawrth_Manual_1km_Grid_Con.shp · Mawrth_Manual_1km_Grid_Dis.shp Related public datasets: The HiRISE images discussed in this work are publicly available from https://www.uahirise.org/. and are credited to NASA/JPL/University of Arizona. HRSC images are credited to the European Space Agency; Mars Express mission team, German Aerospace Center (DLR), and the Freie Universität Berlin (FUB). They are available at the ESA Planetary Science Archive (PSA) https://www.cosmos.esa.int/web/psa/mars-express and are used under the Creative Commons CC BY-SA 3.0 IGO licence. SPATIAL DATA COORDINATE SYSTEM INFORMATION All NOAH-H files and derivative density plots have the same projected coordinate system: “Equirectangular Mars” - Projection: Plate Carree - Sphere radius: 3393833.2607584 m SOFTWARE INFORMATION All GIS workflows (georeferencing, mosaicking) were conducted in ArcGIS Pro 3.0. NOAH-H is a deep learning semantic segmentation software developed by SciSys Ltd for the European Space Agency to aid preparation for the ExoMars rover mission.

Aerial map of the University of Virginia campus, 1975. This map was digitized and georeferenced by Virginia Tech University Library. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000023.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

: This is a georeferenced, stitched, aerial photo composite showing the Casey Upper Melt Lake in February 1979, prior to the construction of modern Casey station. It covers part of the Casey upper melt lake (where drinking water is sourced from), and the area where the Operations building and Casey tank house are currently sited. Frames 1,2,3,4 and 5 from CASC8438 Run 3 were cropped of their black borders and chromatic fringing in Irfanview, saved as *.jpg's and then stitched together in PTGui version 11.21 using default 35mm camera parameters. The resultant stitched image file was georeferenced in ArcGIS Pro 3.1.1. with reference to the Casey 2023 All Station Orthomosaic (WGS84 UTM 49S). 20 control points were used, fitted with a 2nd order polynomial. Total RMS Errors (Forward, Inverse, Forward-inverse) were 1.44, 31.87, 2.34.

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 training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This scanned historical map from the University of Idaho Experimental Forest archives shows the principal cover types on Moscow Mountain and vicinity. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.

The dataset presented here, a historical coregonine spawning database, or CORHIST for short, is the result of several years of coordinated research in archives, libraries, and field stations, to track down evidence of spawning locations for the Coregoninae sub-family of ciscoes and whitefishes in the Great Lakes of North America and their tributaries. Our objective was to accurately identify _location information to coordinates and add all associated data and metadata to a database built specifically for these types of records (a database capable of storing historical, geospatial, and biological data). Data for a total of 11 accepted species of coregonines are included in this dataset. Spawning or nursery habitat designations were assigned based on a wide-range of evidence from original sources, including descriptions of physiology, ontogeny, and behaviors, interviews, first-hand and Indigenous Ecological Knowledge, and by our own examination of museum specimens. Georeferencing was completed using evidence from original records, including navigational information such as dead reckonings, landmarks like islands, lighthouses, reefs, and river mouths, and by using depth and substrate descriptions. Occasionally, supplemental sources including various historical maps and/or published bathymetry and substrate layers were used to assist in georeferencing points. Data points were summarized and quality-checked using ArcMap 10.8 and ArcGIS Pro (datum: WGS84). Reference tables are also included with this dataset.

Vegetation map of Cobb Island and Little Cobb Island in Virginia photointerpreted from aeriel photographs (June 1974) and a field check (1975). This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); http://www.vacadsci.org/vjsArchives/V41/41-4A/TitlePage.html, http://www.vacadsci.org/vjsArchives/V41/41-4A/p282.pdf, http://vanhde.org/content/map, https://www.arcgis.com/home/item.html?id=f2add68fee9a416786306c8d881f4ebc. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000008.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

U.S. Dept. of Interior 1990 National Wetlands Inventory map of Indian Valley, Virginia. This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); https://www.fws.gov/wetlands/data/mapper.html. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000011.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.

Vegetation map of Hog Island and Rogue Island in Virginia photointerpreted from aeriel photographs (June 1974) and a field check (1975).This map was digitized and georeferenced by Virginia Tech University Library. Additional information can be found at the following url(s); http://www.vacadsci.org/vjsArchives/V41/41-4A/TitlePage.html, http://www.vacadsci.org/vjsArchives/V41/41-4A/p282.pdf, http://vanhde.org/content/map, https://www.arcgis.com/home/item.html?id=f2add68fee9a416786306c8d881f4ebc. GeoTIFF can be downloaded as a ZIP package at the following location; https://secure-archive.gis.vt.edu/gisdata/public/DigitizedMapCollections/VirginiaTechUniversityLibraries/VTU_DMC_000007.zip. The associated ZIP package contains an original scanned image and an image georeferenced using ArcGIS Pro.