Abstract The recovery of degraded areas is imperative for the sustainability of mining activities. The main action implemented to improve the chemical, physical and biological conditions of soils, tailings and sterile deposits is the incorporation of organic material. Biosolids (hygienized sewage sludge) are among the organic materials that can be applied. However, considering the health risk they represent, not all areas are suitable for receiving this waste. The present research sought to map the environmental suitability of the Quadrilátero Ferrífero (QF) region to assess the applicability of biosolids. For this purpose, maps were elaborated using restrictive criteria established for the safe application of this residue to the soil by means of the Geographic Information System (GIS), using the ArcGIS software, version 10.2. The established criteria were pedology, topography, hydromorphism, presence of protected areas, soil texture, susceptibility to erosion, proximity to urban areas and their overlaps to obtain the final suitability areas. For the exclusion of areas that presented legal restrictions, the criteria of protected area, areas close to water bodies, urban areas, shallow soils and a slope greater than 45% were used, as established in literature, in CONAMA 498/2020 and in the Forest Law - Federal Law 12,652 of 2012. Of the areas analyzed, 58.5% were suitable for biosolid application, equivalent to 10,858.3 ha of the 18,587 ha studied, indicating the feasibility of biosolids application in part of the QF area to be recovered.

Easements are usually written in deeds or viewable on survey plans. They distinguish use rights, maintenance obligations, and where structures may be built.Data Development: Polygons are created by georeferencing survey plans into Arcmap. Or created manually using the metes and bounds description written in the deed. Key Attribute Fields: Easement TypeCoordinate System:Lambert Conformal Conic, NAD83, PA South Stateplane coordinates, US Foot.

Map source providing the foundation for Phase 2 GLRI Data Processing for the Georeferencing Dashboards.

aGeographic Information System address georeferencing tool.bGlobal Positioning System.cIn a batch using Google Earth address georeferencing tool.dAddress by address using Google Earth address georeferencing tool.Summary statistics of positional errors (in meters) according to address georeferencing method.

This set of GeoTIFF and EPPL7 files represents the Minnesota Department of Transportation's County Highway Map Series in georeferenced image formats. These images of the standard Mn/DOT County Highway Map product can be used in GIS systems and overlayed with other GIS information. The origin of this data is Mn/DOT's Microstation CAD system, where all linework, feature type coding, and symbolization is stored and updated. To produce these data sets, Mn/DOT exported the data from Microstation into postscript files. LMIC then imported the data into GIS systems for georeferencing and further processing. The GeoTIFF data are distributed in both County Highway Map map sheet and full county extents; EPPL7 data sets are distributed only as full county files. Map collars have been removed. This data set represents the Mn/DOT County Highway Map as of January 1, 2002.

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.

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 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

Dashboard show availability of resources in support of the Great Lakes Restoration Initiative (GLRI) projects, assigned priority and sediment budget phase by USACE District, and a map of datasets for use in the Data Processing phase in the sediment budget development process.

Results from Google Earth address georeferencing.

Datasets and codes, which are used in the paper "Using large language models to address the bottleneck of georeferencing natural history collections"1. System requirements: Windows 10; R language: v 4.2.2; Python: v 3.8.122. Instructions for use: The "data" folder contain the key sampling and intermediate data in the analysis process of this study. The initial specimen dataset included a total of 13,064,051 records from the Global Biodiversity Information Facility (GBIF) can be downloaded from GBIF DOI: https://doi.org/10.15468/dl.fj3sqk.Data file name and its meaning or purpose：occurrence_filter_clean.csv: The data before sampling 5,000 records based on continents, after cleaning the initial specimen datamain data frame 5000_only country state county locality.csv: The 5,000 sample data used for georeferencing, containing only basic information such as country, state/province, county, locality, and true latitude and longitude from GBIFmain data frame 100_only country state county locality.csv: The 100 sub-sample data used for humnan and reasoning-LLM georeferencing, containing only basic information such as country, state/province, county, locality, and true latitude and longitude from GBIFmain data frame 5000.csv: records all output data and required records from the analysis of 5,000 sample points, including coordinates and error distances from various georeferencing methods, locality text features, and readability metricsmain data frame 100.csv: records all output data and required records from the analysis of 100 sub-sample points, including coordinates and error distances from various georeferencing methods, locality text features, and readability metricsgeoref_errorDis.csv: used for Figure 1bsummary_error_time_cost.csv: time taken and cost records for various georeferencing methods, used for Figure 4for_human_completed.csv: results of manual georeferencing by the participantshf_v2geo.tif: Global Human Footprint Dataset (Geographic) (Version 2.00), from https://gis.earthdata.nasa.gov/portal/home/item.html?id=048c92f5ce50462a86b0837254924151, used for Figure 5acountry file folder: global country and county polygon vector data, used to extract centroid coordinates of counties in ArcGIS v10.8

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

Esri ArcGIS Online (AGOL) Map Image Layer which provides access to the MDOT SHA Recorded Plats data product.MDOT SHA Recorded Plats consists of polygon geometric features which represent the boundary of georeferenced MDOT SHA right of way plats throughout the State of Maryland. All plats included are recorded, that is to say they are recorded in land records as legal instruments. Each plat has a link to a scanned image on the Maryland State Archives (MSA) website (https://plats.net). This boundary data is owned by the MDOT SHA OHD Plats & Surveys Division (PSD). This information is for general informational purposes only and should be confirmed by MDOT SHA PSD to be deemed official.This data is updated nightly through an automated process which pulls data from several sources to create the plat bounding boxes. The process of georeferencing MDOT SHA right of way plats is ongoing. The plats in this data only represent plats that have been georeferenced. The sources include:PSD data for the location (corners) of platsORE data for information about platsThere are three criteria for a plat to be included in this data:The recorded date must be populatedThe plat corner coordinates must be entered by PSDThe plat URL must be populated by PSDFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

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

description: This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The original photo (Frame 135) was georeferenced against the 2009 NAIP image within ArcMap 10.1. Six to eight points were used in the georeferencing process. This data set provides a look back in time following the closure of the Flaming Gorge Dam. It allows a look at landscape features including riparian vegetation extent, existing farmland, shrublands, etc. It also provides an important look at the location of the Green River channel to interpret river dynamics and movement. Pixel resolution of this image is 0.6 meters. This image should be used for resource level interpretation only.; abstract: This data set is a georeferenced version of an original black and white aerial photograph downloaded from Earth Explorer (USGS; www.earthexplorer.usgs.gov). The original photo (Frame 135) was georeferenced against the 2009 NAIP image within ArcMap 10.1. Six to eight points were used in the georeferencing process. This data set provides a look back in time following the closure of the Flaming Gorge Dam. It allows a look at landscape features including riparian vegetation extent, existing farmland, shrublands, etc. It also provides an important look at the location of the Green River channel to interpret river dynamics and movement. Pixel resolution of this image is 0.6 meters. This image should be used for resource level interpretation only.

These maps are georeferenced versions of the maps produced by The University Museum, University of Pennsylvania, project at Tikal, Guatemala and published as Tikal Report 11. These georeferenced maps are intended for use with GIS (Geographic Information System) software. The maps should be useful for archaeologists, tourists and managers of Tikal National Park. This map set consists of eleven georeferenced maps. The set includes two versions of the overview map of the central sixteen square kilometers of Tikal—the "Ruins of Tikal" map. One version includes the map border. The other version is without the border. The nine remaining maps cover the inner nine square kilometers in detail, without borders. The maps were georeferenced as part of a University of Cincinnati project in Tikal, under permit of the Guatemalan government. The UC Project georeferenced the maps using land survey methods. We created transformation equations based on a point of beginning, a reference direction and a map scale. Directions and distances on the ground were transformed into UTM projected directions and distances. The point of beginning was the Petty Company benchmark shown on the "Camp Quad" map. In 2010 we determined the location with a GPS receiver. We accessed both the horizontal and vertical accuracy of the georeferenced maps. Based on 96 test points spread throughout the area of the maps, we found the median horizontal accuracy of the maps, compared to GPS, to be 5.6 meters. Based on 103 test points spread throughout the area of the maps, we found the median vertical accuracy of the maps, compared to a NASA radar altimetry mission, to be 2.1 meters. The borders of the maps were removed so the set of maps will “seamlessly” fit together in GIS. See Tikal Report No.11 for versions of the maps with borders (one version of the georeferenced "Ruins of Tikal" map includes the border). The georeferencing files are optimized for use in ArcGIS version 9.2 and beyond. The PDF file of TR11 from which these maps were extracted was made with the generous assistance of the University Museum Library and the Tikal Archives. Details of the georeferencing and accuracy check are in a report to the Dirección Patrimonio Cultural y Natural de Guatemala: Christopher Carr, Eric Weaver, Nicholas Dunning, and Vernon Scarborough (2011) EVALUACIÓN DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENNSYLVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). In Lentz, D., C. Ramos, N. Dunning, V. Scarborough and L. Grazioso. PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Additional details of the strategies the Penn Project used to produce these high quality maps, the georeferencing methodology, and the accuracy check process are forthcoming in a book chapter. The book is on the UC project at Tikal, to be published by Cambridge University Press. The chapter is Carr, Weaver, Dunning and Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. In Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. Permission to publish these maps must be secured from: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. .................................................................................................................. Estos mapas son versiones georeferenciados de los mapas producidos por el Museo Universitario de la Universidad de Pennsylvania, Proyecto Tikal, Guatemala y publicado como Informe de Tikal No. 11. La intensión de estos mapas georeferenciados es para ser utilizados con el Sistema de Información Geográfica (SIG). Los mapas deben ser útiles para los arqueólogos, los turistas y los administradores del Parque Nacional Tikal. Este conjunto de mapas consta de once mapas georreferenciados. El juego incluye dos versiones del mapa general de los 16 km2 centrales del mapa de las "Ruins of Tikal". Una versión del mapa incluye sus encuadrados. La otra versión esta sin los encuadrados. Los nueve mapas restantes cubren los mapas interiores de 9 km2 en detalle, sin encuadrados. Los mapas fueron georeferenciados como parte de un proyecto de la Universidad de Cincinnati en Tikal, con permiso del Ministerio de Cultura y Deportes del Gobierno de Guatemala. El Proyecto de la Universidad de Cincinnati georeferenció los mapas utilizando métodos de reconocimiento de campo. Creamos ecuaciones de transformación basado en un punto de inicio, una dirección de referencia y un mapa a escala. Direcciones y distancias en el campo se transformaron en direcciones proyectadas UTM y distancias. El punto de inicio fue el punto de refere... Visit https://dataone.org/datasets/doi%3A10.6067%3AXCV82B8ZW9_meta%24v%3D1377891225229 for complete metadata about this dataset.

Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands.

Update May 2015 - This dataset has been rename from "Brattstrand Bluff penguin GIS dataset" to "Islands NE of Brattstrand Bluff penguin GIS dataset" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.

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).

These maps are georeferenced versions of the maps produced by The University Museum, University of Pennsylvania, project at Tikal, Guatemala and published as Tikal Report 11. These georeferenced maps are intended for use with GIS (Geographic Information System) software. The maps should be useful for archaeologists, tourists and managers of Tikal National Park. This map set consists of eleven georeferenced maps. The set includes two versions of the overview map of the central sixteen square kilometers of Tikal—the "Ruins of Tikal" map. One version includes the map border. The other version is without the border. The nine remaining maps cover the inner nine square kilometers in detail, without borders. The maps were georeferenced as part of a University of Cincinnati project in Tikal, under permit of the Guatemalan government. The UC Project georeferenced the maps using land survey methods. We created transformation equations based on a point of beginning, a reference direction and a map scale. Directions and distances on the ground were transformed into UTM projected directions and distances. The point of beginning was the Petty Company benchmark shown on the "Camp Quad" map. In 2010 we determined the location with a GPS receiver. We accessed both the horizontal and vertical accuracy of the georeferenced maps. Based on 96 test points spread throughout the area of the maps, we found the median horizontal accuracy of the maps, compared to GPS, to be 5.6 meters. Based on 103 test points spread throughout the area of the maps, we found the median vertical accuracy of the maps, compared to a NASA radar altimetry mission, to be 2.1 meters. The borders of the maps were removed so the set of maps will “seamlessly” fit together in GIS. See Tikal Report No.11 for versions of the maps with borders (one version of the georeferenced "Ruins of Tikal" map includes the border). The georeferencing files are optimized for use in ArcGIS version 9.2 and beyond. The PDF file of TR11 from which these maps were extracted was made with the generous assistance of the University Museum Library and the Tikal Archives. Details of the georeferencing and accuracy check are in a report to the Dirección Patrimonio Cultural y Natural de Guatemala: Christopher Carr, Eric Weaver, Nicholas Dunning, and Vernon Scarborough (2011) EVALUACIÓN DE LA EXACTITUD DE LOS MAPAS DE TIKAL DE LA UNIVERSIDAD DE PENNSYLVANIA, POR GPS Y ESTACIÓN TOTAL (Accuracy assessment of the Penn Project maps of Tikal, by GPS and Total Station). In Lentz, D., C. Ramos, N. Dunning, V. Scarborough and L. Grazioso. PROYECTO DE SILVICULTURA Y MANEJO DE AGUAS DE LOS ANTIGUOS MAYAS DE TIKAL. Additional details of the strategies the Penn Project used to produce these high quality maps, the georeferencing methodology, and the accuracy check process are forthcoming in a book chapter. The book is on the UC project at Tikal, to be published by Cambridge University Press. The chapter is Carr, Weaver, Dunning and Scarborough. Bringing the University of Pennsylvania maps of Tikal into the era of electronic GIS. In Lentz, Dunning, Scarborough (eds). Tikal and Maya Ecology: Water, Landscapes and Resilience. Permission to publish these maps must be secured from: The University of Pennsylvania Museum of Archaeology and Anthropology, 3260 South Street, Philadelphia, PA 19104, Tel: (215) 898-4050, Fax: (215) 573-9369, Email: publications@pennmuseum.org. .................................................................................................................. Estos mapas son versiones georeferenciados de los mapas producidos por el Museo Universitario de la Universidad de Pennsylvania, Proyecto Tikal, Guatemala y publicado como Informe de Tikal No. 11. La intensión de estos mapas georeferenciados es para ser utilizados con el Sistema de Información Geográfica (SIG). Los mapas deben ser útiles para los arqueólogos, los turistas y los administradores del Parque Nacional Tikal. Este conjunto de mapas consta de once mapas georreferenciados. El juego incluye dos versiones del mapa general de los 16 km2 centrales del mapa de las "Ruins of Tikal". Una versión del mapa incluye sus encuadrados. La otra versión esta sin los encuadrados. Los nueve mapas restantes cubren los mapas interiores de 9 km2 en detalle, sin encuadrados. Los mapas fueron georeferenciados como parte de un proyecto de la Universidad de Cincinnati en Tikal, con permiso del Ministerio de Cultura y Deportes del Gobierno de Guatemala. El Proyecto de la Universidad de Cincinnati georeferenció los mapas utilizando métodos de reconocimiento de campo. Creamos ecuaciones de transformación basado en un punto de inicio, una dirección de referencia y un mapa a escala. Direcciones y distancias en el campo se transformaron en direcciones proyectadas UTM y distancias. El punto de inicio fue el punto de refere... Visit https://dataone.org/datasets/doi%3A10.6067%3AXCV8XK8GDP_meta%24v%3D1377891145355 for complete metadata about this dataset.

The McIntyre 1966 geologic map of Reynolds Creek is the largest scale map of the region and the basis for the current geologic map units hosted by the ARS. However, the current set has artifacts from referencing it to a (suspected) 30m DEM, diminishing its usefulness. To improve on this map, the original paper maps were scanned and then imported and georeferenced to the current watershed boundaries and to topographic features of the 2014 LiDAR derived DEM in ArcMap. Georeferencing was done on a best-effort basis to both preserve the original line-drawing shapes from the McIntyre 1966 map, but to also conform to topographic features in the DEM. A final version of these geologic units is available as geology_2019rework_of_McIntyre1966_final.shp in the 2019_geology_ReynoldsCreekCZO.gdb geodatabase as well as being exported as a layer package for sharing as geology_2019rework_of_McIntyre1966_final.lpk Further work is needed in the field to ground-truth the data in addition to further hours spend assessing the fit of the current shapes to topographic features in the LiDAR-derived, 2014 1 m DEM. Significant improvements in accuracy of the map units could be achieved with minimal effort on those two units. In addition, an effort should be made to evaluate the McIntyre1966 map units with respect to the more recent map units by Bonnichsen and Godchaux in 2006 (https://www.idahogeology.org/product/DWM-80).

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.