Zoom to desired area, click in the map and click the link to download 2016 Aerial Imagery at 3" resolution of the selected Index Grid. Image downloads are a .zip MrSid file with the .sid and the .sdw. The .sdw contains the georeferencing information for the .sid image.

Download the entire imagery for Dunwoody here: https://dungis.dunwoodyga.gov/SIDZIP/

Download / Reference / get a spreadsheet of the Image Index Grid Polygon here: https://get-dunwoody.opendata.arcgis.com/datasets/aerial-image-index-grid-layer

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.

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.

Contains links to download 2016 3" Aerial Images.Downloads are a .zip MrSid file with the .sid and the .sdw. The .sdw contains the georeferencing information for the .sid image.Use Aerial Image Index web-map: https://arcg.is/2j8DJlZ to look-up Images by Index Gridor download the entire imagery for Dunwoody here: https://dungisapp.dunwoodyga.gov/SIDZIP/

In Puerto Rico, tens of thousands of landslides, slumps, debris flows, rock falls, and other slope failures were triggered by Hurricane María, which made landfall on 20 September 2017. “Landslide” is used here and below to represent all types of slope failures. This dataset is a point shapefile of landslide headscarps identified across Puerto Rico using georeferenced aerial and satellite imagery recorded following the hurricane. The imagery used includes publicly available aerial imagery obtained by the Federal Emergency Management Agency (FEMA; Quantum Spatial, Inc., 2017), aerial imagery obtained by the National Oceanic and Atmospheric Administration (NOAA; NOAA, 2017), and several WorldView satellite imagery datasets available from DigitalGlobe, Inc. The FEMA imagery was recorded by Sanborn and Quantum Spatial, Inc. between 25 September and 27 October 2017, has a pixel resolution of approximately 15 cm, and includes over 6,000 image tiles that cover approximately 97% of the large island and 100% of Vieques. The NOAA imagery was recorded 22-26 September 2017, also has a resolution of approximately 15 cm, and covers about 10% of the large island, 60% of Vieques, and 100% of Culebra. The DigitalGlobe imagery used in this project was recorded during September-November 2017, has a pixel resolution of approximately 50 cm, and covers approximately 99% of the large island and 35% of Vieques. DigitalGlobe images were acquired via the DigitalGlobe Open Data Program, the DigitalGlobe Foundation imagery grant, and via partnership with the U.S. Geological Survey. No imagery was examined for Desecheo, Mona, Monito, Caja de Muertos, or other smaller islands. The FEMA imagery was usually used first for landslide mapping due to its high resolution and more accurate georeferencing. For almost every location, there were multiple images available due to overlap in each dataset and overlap between different datasets. This overlap was helpful when clouds or shadows obscured the view of the ground surface in one or more images for a given location. Additional oblique and un-georeferenced aerial imagery recorded by the Civil Air Patrol (ArcGIS, 2017) was consulted, if needed. Comparing the post-event imagery with pre-event imagery available through the ESRI ArcGIS basemap layer and/or Google Earth was useful to accurately identify sites that failed during September 2017; such comparisons were made for landslides that appeared potentially older. Some landslides in our inventory may have occurred prior to Hurricane María—potentially triggered by Hurricane Irma which passed northeast of Puerto Rico two weeks earlier—or between the time of the hurricane and when photographs were taken. UTM Zone 19N projection with WGS 84 datum was used throughout the mapping process. The inventory process began with creation of a first draft by a team of 15 people. This draft was subsequently checked for quality and revised by the three leaders of the mapping effort. Each identified landslide is represented by a point located at the center of its headscarp. The horizontal position of headscarp points was carefully selected using multiple overlapping images (usually available) and other geospatial datasets including lidar acquired during 2015 and available from the U.S. Geological Survey 3DEP program, the U.S. Census Bureau TIGER road shapefile, and the National Hydrology Dataset flowline shapefile. Mapping was generally performed at 1:1000 scale. Given errors in georeferencing and landslides poorly resolved in imagery, we conclude that headscarp point locations are generally accurate within 3 m. Municipality (municipio) and barrio names in which each landslide occurred are included in the attribute table of the shapefile, as are the geographic coordinates of each point in decimal degrees (WGS 84 datum). Landslides were identified in 72 of the 78 municipalities of Puerto Rico. No landslides were documented on the island municipalities of Culebra or Vieques. On the main island of Puerto Rico, 64% of land experienced 0-3 landslides per square kilometer, 26% experienced 3-25 landslides per square kilometer, and 10% experienced more than 25 landslides per square kilometer. Concentrated zones of more than 100 landslides per square kilometer are in the municipalities of Maricao, Utuado, Jayuya, and Corozal. Of the ten barrios where more than 100 landslides per square kilometer were catalogued, eight are in Utuado. The drainage basins with the highest density of landslides are the Rio Grande de Arecibo and Rio Grande de Añasco watersheds, each with over 30 landslides per square kilometer. Six out of the seven sub-basins with more than 50 landslides per square kilometer are in the Rio Grande de Arecibo basin. We identified and mapped 71,431 landslides in total. The College of Arts and Sciences at the University of Puerto Rico in Mayagüez is thanked for providing release time to K.S. Hughes to permit partial development of this dataset. References ArcGIS, 2017, CAP Imagery – Hurricane Maria: https://www.arcgis.com/home/webmap/viewer.html?webmap=3218d1cb022d4534be0c7d6833c0adf1. Last accessed 18 June 2019. NOAA, 2017, Hurricane MARIA Imagery: https://storms.ngs.noaa.gov/storms/maria/index.html. Last accessed 18 June 2019. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria. Last accessed 18 June 2019.

This dataset includes polygons with a minimum of 40 acres of woodlands per square mile as depicted in William H. Brewer�s 1873 map of woodland density and covers the conterminous United States. Each polygon has been labeled with the density category (1-5) depicted on the original map. This dataset was created by georeferencing a scanned version of the source map and by heads-up digitizing each woodland density polygon.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

High Resolution Imaging Science Experiment (HiRISE) digital terrain model (DTM) and orthorectified image of a viscous flow feature incised by a gully in Nereidum Montes, Mars.These data products were produced by Joel Davis at the Natural History Museum, London, and re-projected by Frances Butcher at the University of Sheffield for the purposes of the study presented in:Butcher, F.E.G., Arnold, N.S., Conway, S.J., Berman, D.C., Davis, J.M., and Balme, M.R. 2023, The Internal Structure of a Debris-Covered Glacier on Mars Revealed by Gully Incision, Icarus, https://doi.org/10.1016/j.icarus.2023.115717Please read the following information carefully.Data SetsThe following files are present in .tif format. These are geotiffs and have geospatial metadata: 1 m/pixel DTM: ‘NWArgyre_1_AATE_1m_Sinusoidal.tif’ 1 m/pixel FOM (Figure of Merit - see Readme.txt): ‘FOM_NWArgyre_1_AATE_1m_Sinusoidal.tif’ 25 cm/pixel orthoimage: ‘ESP_051036_1370_25o_Sinusoidal.tif’ Readme.txt: Contains further information, including an explanation of the values in the Figure of MeritThe DTM was generated using BAE Systems SOCET SET software, with the following HiRISE images as input: ESP_051036_1370: https://www.uahirise.org/ESP_051036_1370 ESP_015947_1370: https://www.uahirise.org/ESP_015947_1370The orthoimage was generated by orthorectifying HiRISE image ESP_051036_1370 using the DTM.The DTM, FOM, and orthoimage were re-projected in ESRI ArcGIS 10.7 to minimise the effects of distortion upon the measurements and modelling results presented in Butcher et al. (2023). The sinusoidal projection used has a central meridian of 308.75°E, and is based on the IAU spherical datum for Mars (radius 3396190 m).The data are not georeferenced to any other dataset in this release. Therefore care should be taken in the first instance, with georeferencing as required. The overall quality of the DTM is good, but noise levels vary – check the FOM (see below) and create a shaded relief map to ensure the DTM is adequate for your required use.We note that since we generated the DTM and orthoimage, the HiRISE team also released a DTM generated from the same images, including additional colour orthorectified images (which are used in Butcher et al. 2023). These independently-generated data can be found at: https://www.uahirise.org/dtm/ESP_015947_1370DTM Vertical PrecisionThe vertical precision of the DTM was estimated by Butcher et al. (2023) to be 0.2 m (based on a stereo convergence angle between input images of 14.8°, and assuming an RMS pixel matching error of 0.2 pixels) following the approach of:- Kirk, R. L., et al. (2008), Ultrahigh resolution topographic mapping of Mars with MRO HiRISE stereo images: Meter-scale slopes of candidate Phoenix landing sites, J. Geophys. Res., 113, E00A24, doi:10.1029/2007JE003000.Figure of Merit (FOM) explanationPlease see 'Readme.txt' for an explanation of values in the Figure of Merit.CreditIf using the data products included herein, please cite: Butcher et al. (2023) HiRISE images should be credited "Image: NASA/JPL-Caltech/University of Arizona"

This dataset represents the base (ground-level) outline, or footprint, of buildings and other man-made structures in Fulton County, Georgia. The original data were produced by digitizing structures from 1988 aerial ortho-photography. Updates to the data are made from various aerial ortho-photography. In 2010, the data table structures was modified to include a number of attributes derived from tax assessment data through a spatial join of structures with tax parcels. The attributes include feature type (residential or commercial), structure form (conventional, ranch, colonial, etc.), number of stories, and the year built. In 2012, updates to features began using building sketch data collected by the Fulton County Tax Assessors. The building sketch data consist of turtle graphics type descriptors defining (in ungeoreferenced space) the ground-level outline of each structure in the County. These descriptors were converted to an ESRI SDE feature class using Python, georeferencing each structure by placing it in the center of its associated tax parcel. Each structure shape was is then manually translated and rotated into position using aerial imagery as a reference. As of May 2014, this update process was still in progress.This dataset is used in large-scale mapping to show the location of individual buildings and other man-made structures and in smaller-scale mapping to show general patterns of development. May also be used to estimate human population for very small areas. Other applications include the computation of impervious surfaces in stormwater studies and the development of 3-D urban models.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is a polygon shapefile digitisation of Map 2 - PEL 96 Phase One Area 45,500 acres from the report: Strike Energy Limited (2015) Southern Cooper Basin Gas Project Independent Contingent Resource Certification. Viewed 21 May 2015, http://www.strikeenergy.com.au/wp-content/uploads/2015/04/20150427_Sthern-Cooper-Basin-Contingent-Resources.pdf (GUID: 7a040992-f3da-4391-8f24-c2a489cb3f22).

Dataset History

Map 2 - PEL 96 Phase One Area 45,500 acres from the report: Strike Energy Limited (2015) Southern Cooper Basin Gas Project Independent Contingent Resource Certification. Viewed 21 May 2015, http://www.strikeenergy.com.au/wp-content/uploads/2015/04/20150427_Sthern-Cooper-Basin-Contingent-Resources.pdf (GUID: 7a040992-f3da-4391-8f24-c2a489cb3f22) was georeferenced in ArcMap.

Using the georeferencing toolbar in ArcMap, this map was georeferenced to the Cooper CRDP points (Coal Resource Development Pathway (CRDP) Locations for the Cooper Subregion, GUID: d82a737c-89b8-4e50-8973-47311ddd070f) and 250k watercourse lines (GEODATA TOPO 250K Series 3, GUID: a0650f18-518a-4b99-a553-44f82f28bb5f).

Following the georeferencing of this map, the PEL 96 phase one area feature was traced in ArcMap to produce a polygon shapefile.

Dataset Citation

Bioregional Assessment Programme (2015) Strike Energy PEL 96 Phase One Area. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/317dad8e-f6fb-4eea-bb33-f4da73a5023a.

Dataset Ancestors

• Derived From Southern Cooper Basin Gas Project Independent Contingent Resource Certification

• Derived From Petroleum Wells

• Derived From Coal Resource Development Pathway (CRDP) Locations for the Cooper Subregion

• Derived From GEODATA TOPO 250K Series 3

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition.

This dataset was created from work done in a PhD thesis.

A shapefile representing the Tomago sand beds digitised from an existing map which is included in the data set. The Tomago Sandbeds represent a groundwater resource, managed by Hunter Water, contributing to the lower Hunterâ s drinking water supply. The sandbeds are parallel to the coast between Newcastle and Port Stephens, starting at Tomago and extending north-east for 25 kilometres to Lemon Tree Passage.

Dataset History

A shapefile representing the Tomago sand beds digitised from an existing map which is included in the data set: tomago_sandbeds1.tif. The image was sourced from a PhD Thesis - citation below. A polygon shapefile was created in ArcGIS by georeferencing the image digitising the boundary of the sand beds shown in the map.

Image source:

Crosbie, R. S. (2003). Regional scaling of groundwater recharge (Doctoral dissertation, University of Newcastle).

The image used was Figure 2.1 in the above publication.

Dataset Citation

University of Newcastle (2014) Tomago sand beds. Bioregional Assessment Source Dataset. Viewed 02 May 2016, http://data.bioregionalassessments.gov.au/dataset/04ff0b0d-3f63-4400-a986-d656da24c165.

Abstract This dataset was derived by the Bioregional Assessment Programme. The parent dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this …Show full descriptionAbstract This dataset was derived by the Bioregional Assessment Programme. The parent dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains a georeferenced version of Figure 15: Schematic structural map of the Gunnedah Basin (adapted and amended from Tadros, 1993 and Scheibner 1996), from the report - "Gurba, L, Golab, A, Jaworska, J, Douglass, J and Hyland, K, 2009. CO2 Geological Storage Opportunities in the Gunnedah Basin, and the Southern Bowen Basin, NSW. Cooperative Research Centre for Greenhouse Gas Technologies, Canberra, Australia, CO2CRC Publication Number RPT09-1456. 125pp." (GUID: 1be2af42-6a73-457f-ac29-cb52d06b0105). This georeferenced image is for visualisation in ArcGIS. Dataset History Figure 15: Schematic structural map of the Gunnedah Basin (adapted and amended from Tadros, 1993 and Scheibner 1996), from the report - "Gurba, L, Golab, A, Jaworska, J, Douglass, J and Hyland, K, 2009. CO2 Geological Storage Opportunities in the Gunnedah Basin, and the Southern Bowen Basin, NSW. Cooperative Research Centre for Greenhouse Gas Technologies, Canberra, Australia, CO2CRC Publication Number RPT09-1456. 125pp." (GUID: 1be2af42-6a73-457f-ac29-cb52d06b0105) was georeferenced in ArcMap using the Georeferencing tool bar. Graticule marks in the original figure were used as reference points in this process. The figure was exported in geoTIFF format. Dataset Citation Bioregional Assessment Programme (2016) Schematic structural map of the Gunnedah Basin. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/ceb50481-c35f-4e77-82bf-d25e4dec67ac. Dataset Ancestors Derived From CO2 Geological Storage Opportunities in the Gunnedah Basin, and the Southern Bowen Basin, NSW Status Report

This dataset is a contribution to the development of a kelp distribution vector dataset. Bull kelp (Nereocystis leutkeana) and giant kelp (Macrocystis pyrifera) are important canopy-forming kelp species found in marine nearshore habitats on the West coast of Canada. Often referred to as a foundation species, beds of kelp form structural underwater forests that offer habitat for fishes and invertebrates. Despite its far-ranging importance, kelp has experienced a decline in the west coast of North America. The losses have been in response to direct harvest, increase in herbivores through the removal of predators by fisheries or diseases, increase in water turbidity from shoreline development as well as sea temperature change, ocean acidification, and increased storm activates. Understanding these impacts and the level of resilience of different kelp populations requires spatiotemporal baselines of kelp distribution. The area covered by this dataset includes the BC coast and extends to portions of the Washington and Alaska coasts. This dataset was created using 137 British Admiralty (BA) charts, including insets, with scales ranging from 1:6,080 to 1:500,000, created between 1858 and 1956. All surveys were based on triangulation, in which a sextant or theodolite was used to determine latitude and angles, while a chronometer was used to help determine longitude. First, each BA chart was scanned by the Canadian Hydrographic Service (CHS) using the CHS Colortrac large format scanner, and saved as a Tagged Image Format at 200 DPI, which was deemed sufficient resolution to properly visualize all the features of interest. Subsequently, the scanned charts were imported into ESRI ArcMap and georeferenced directly to WGS84 using CHS georeferencing standards and principles (charts.gc.ca). In order to minimize error, a hierarchy of control points was used, ranging from high survey order control points to comparing conspicuous stable rock features apparent in satellite imagery. The georeferencing result was further validated against satellite imagery, CHS charts and fieldsheets, the CHS-Pacific High Water Line (charts.gc.ca), and adjacent and overlapping BA charts. Finally, the kelp features were digitized, and corresponding chart information (scale, chart number, title, survey start year, survey end year, and comments) was added as attributes to each feature. Given the observed differences in kelp feature representation at different scales, when digitizing kelp features, polygons were used to represent the discrete observations, and as such, they represent presence of kelp and not kelp area. Polygons were created by tracing around the kelp feature, aiming to keep the outline close to the stipe and blades. The accuracy of the location of the digitized kelp features was defined using a reliability criterion, which considers the location of the digitized kelp feature (polygon) in relation to the local depth in which the feature occurs. For this, we defined a depth threshold of 40 m to represent a low likelihood of kelp habitat in areas deeper than the threshold. An accuracy assessment of the digitized kelp features concluded that 99% of the kelp features occurred in expected areas within a depth of less than 40 m, and only about 1% of the features occurred completely outside of this depth.

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.

This Coastal Barrier Resources System (CBRS) data set, produced by the U.S. Fish and Wildlife Service (Service), contains areas designated as undeveloped coastal barriers in accordance with the Coastal Barrier Resources Act (CBRA), as amended (16 U.S.C. 3501 et seq.). These digital polygons are representations of the CBRS boundaries shown on the official CBRS maps referenced in 16 U.S.C. 3503(a). Copies of the official CBRS maps are available for viewing at Service’s Headquarters office and are also available to view or download at https://www.fws.gov/cbra/maps/index.html. The boundaries used to create the polygons herein were compiled between 12/6/2013 and 8/16/2023 from the official CBRS maps. The boundaries of the CBRS Units in Connecticut, Massachusetts, Rhode Island, and the Long Island portion of New York, were digitized from the official paper maps according to the guidelines in a notice published in the Federal Register on August 29, 2013 (see the “Georeferencing and Boundary Interpretation” and “Boundary Transcription” sections of 78 FR 53467; available at https://www.federalregister.gov/d/2013-21167). In all other cases where the official map was created through digital methods, the digital boundary was used. CBRS boundaries viewed using the CBRS Mapper or shapefiles are subject to misrepresentations beyond the Service’s control, including misalignments of the boundaries with third party base layers and misprojections of spatial data. The Service is not responsible for any misuse or misinterpretation of this digital data set, including use of the data to determine eligibility for Federal funding or financial assistance. Users should pair these data with the CBRS Buffer Zone shapefile and an orthoimage when inspecting areas that are within or in close proximity to the CBRS. Properties or structures that fall partially or entirely within the buffer area may be within the CBRS, and an official determination from the Service is recommended. For an official determination of whether or not an area or specific property is located within the CBRS, please follow the procedures found at https://www.fws.gov/service/coastal-barrier-resources-system-property-documentation. The official CBRS map is the controlling document and should be consulted for all official determinations in close proximity (within 20 feet) of a CBRS boundary. For any questions regarding the CBRS, please contact your local Service field office or email CBRA@fws.gov. Contact information for Service field offices can be found at https://www.fws.gov/node/267216.

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.

Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquad (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75-minutes of latitude by 3.75-minutes of longitude). The geographic extent of the DOQ is equivalent to a quarter-quad plus the overedge ranges from a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The standard, archived digital orthophoto is formatted as four ASCII header records, followed by a series of 8-bit binary image data records. The radiometric image brightness values are stored as 256 gray levels ranging from 0 to 255. The metadata provided in the digital orthophoto contain a wide range of descriptive information including format source information, production instrumentation and dates, and data to assist with displaying and georeferencing the image.

This feature class was digitized from the map, A.B. 1717, by Jeff Galef on August 22, 2012. The features were labeled as being in the Primary or Secondary Zone. The digitizing was done at a 1:4,000 scale. The features were digitized by a map that was georeferenced by Jeff Galef on July 25, 2012. The number of control points used was 25. The RMS error was 13.74340. The georeferencing was performed against the 2009 NAIP imagery, which was projected to UTM Zone 10, NAD 83.Digitizing was difficult since the line borders and the associated colors often did not match up. That is, there was a fair amount of overlap. The decision was made that the digitizing would follow the thick red and black lines where available. Otherwise, the digitizing followed the coloring. This feature class was edited on November 26, 2013 by Terri Fong to reflect the San Francisco Bay Conservation and Development Commission's map amendments of 2011. The amendments are described in Resolution No. 11-05 which can be found here: http://www.bcdc.ca.gov/BPA/Final2011.07.01.ResolutionNo1.10.pdf. This resolution changes the size of the Water Related Industry Reserve Area near Collinsville. The current Boundaries of the Suisun Marsh map can be found here: http://www.bcdc.ca.gov/plans/SMboundaries.pdf.

Geotechnical reports are indexed within a database maintained by HPW-TEB Geotechnical Unit. Meta data associated to each geotechnical report are captured within this indexing table, including report reference number, title, author, highway and km start and end. The table has been modified to include columns that aid in georeferencing geotechnical reports. Added columns include route ID, Latitude, and Longitude. Transportation Engineering Branch is continually improving its geographical information systems with a major focus on creating linear referencing routes within ArcGIS. Georeferencing geotechnical reports will utilize the linear referencing routes in creating points and line shape files by referencing the highway number and km points or ranges as defined within the indexing table. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

This georeferenced pdf map shows the locations of sewer, electric, and gas lines at the Calvert Island facility. The map was produced by Watson and Barnard BC Land Surveyors and Blue Mountain Engineering in 2006 and Georeferenced by Will McInnes, of the Hakai Institute, in 2017.

Document original title: Utility map for District Lot 1414 Range 2, Victoria Coast District. BCGS102P 070 Original data sourced from Watson and Barnard BC Land Surveyors and Blue Mountain Engineering in 2006: 561 Bellany Close, Victoria, BC, V9B6C1 Georeferenced by Will McInnes, September 2017: Hakai Institute 1002 Wharf St, Victoria, BC, V8W1T4

Methods: Survey method unknown. Conducted by Blue Mountain Engineering in 2006 using survey grade GPS. Georeferencing conducted in September 2017 using ArcGIS and a spline re-projection method. All dimensions are metric. Bearings are grid bearings and are derived from post processed GPS observations from October 21st and 22nd, 2006. NAD 83 CSRS. Elevations are geodetic, shown in meters, derived from post processed GPS observation of Brass = 2.708 meters (CGVD28 HTv2.0)

Files used to create the map: File: 22341_TP_block.dw Description: CAD drawings of Hakai Institute field station on Calvert Island File: calvertislandtopogrophy2006.pdf - a survey of elevations for buildings at the Hakai Institute field station on Calvert Island as of 2006. File: hakai layout all.tif - Surveyed plan of water, electric, fuel, heating, and hydrant lines as well as roads at the Hakai Institute field station on Calvert Island. File: hakai layout.tif - Surveyed plan of water lines at the Hakai Institute field station on Calvert Island. File: 2010_Dec_14 HAKAI MASTER-HAKAI LAYOUT ALL.pdf - Surveyed plan of water, electric, fuel, heating, and hydrant lines as well as roads at the Hakai Institute field station on Calvert Island, updated master drawings.

This is a a one time dataset stored as a PDF and georeferenced in ArcGIS. Not updated since 2006.

TIFF raster image format with TFW files. It represents the entire municipal territory divided into 57 sheets. The information content is on a scale of 1:5000. The technical paper is made up of: 1) geometric elements. 2) constituent elements of the anthropic landscape such as: buildings, technical artifacts, roads, railways, canals, trees and rows, etc; 3) constitutive elements of the natural landscape such as: hydrography, vegetation, etc.; 4) administrative limits; 5) toponymy. WMS 1.3.0 service available at https://geoportale.comune.milano.it/arcgis/services/Cartografie_Raster/Milano_1930_TIF_CTC_GB/ImageServer/WMSServer and download service for portions of cartography at http://geodata.sitmilano .opendata.arcgis.com/ PLEASE NOTE: The map derives from the georeferencing of paper map sheets acquired via scanner. The paper support available at the time for the acquisition was found to be missing sheets 8,14,37 which are therefore not present in the digital format. - Raster image TIFF file format with TFW file. It represents the entire territory of the Municipality of Milan divided into 57 tiles. The scale is 1:5000. The Municipal Base Map is composed by: 1) geometric elements. 2) elements of the anthropic landscape such as buildings, technical structures, roads, railroads, canals, trees and rows of plants, etc.; 3) elements of the natural landscape such as hydrography, vegetation, etc.; 4) administrative boundaries; 5) toponyms. Available WMS service 1.3.0 at https://geoportale.comune.milano.it/arcgis/services/Cartografie_Raster/Milano_1930_TIF_CTC_GB/ImageServer/WMSServer and Clip and ship service of map areas at http://geodata.sitmilano.opendata.arcgis .com/ PLEASE NOTE: Digital map as result of scan process over a paper map. Original map had a lack of tiles number 8,14,37 during scan process, so that mentioned tiles are not available in digital copy.