Historical imagery was obtained from University of Florida’s historical Imagery site, “Aerial Photography: Florida”, the Florida Department of Transportation (FDOT) Aerial Photo Lookup System, or from the FDEP district offices. Images downloaded from UF were saved locally and georeferenced by GIS team members, whereas the imagery received from the district offices were georeferenced by District staff. It is understood that these "pre-georeferenced" tiles were georeferenced within ArcMap by various staff from the District offices. The following applies to the imagery georeferenced in-office by the Division of Water Resource Management (DWRM):The georeferencing was completed in either ArcMap 10.3.1 or ArcGIS Pro. The following standards were held for the georeferencing process: the minimum number of control points was 10 points. The RMS value was kept at or below 5.0 for all tiles georeferenced in 1st Order Polynomial, and 2.0 for those georeferenced in 2nd Order Polynomial (where 1st Order was not possible). The maximum individual residual was at or under twice the RMS. Again, these were the standards, but the accuracy is not guaranteed. To QC for human error, once all counties for the given decade were georeferenced a comparison task was completed. This QC emphasized that this data is only a visual aid in that distances can be off 50 meters or more in some areas. These are mostly areas where there were limited reference features to georectify the original images. The smallest distance found was under 10 meters. To attain more information on this QC please contact FDEP WRM GIS. As stated in the use limitation, but emphasized here, information contained herein is provided for informational purposes only. The State of Florida, Department of Environmental Protection provides geographic information systems (GIS) data and metadata with no claim as to the completeness, usefulness, or accuracy of its content, positional or otherwise. The State and its officials and employees make no warranty, express or implied, and assume no legal liability or responsibility for the ability of users to fulfill their intended purposes in accessing or using GIS data or metadata or for omissions in content regarding such data. The data could include technical inaccuracies and typographical errors. The data is presented "as is," without warranty of any kind, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. Your use of the information provided is at your own risk. In providing this data or access to it, the State assumes no obligation to assist the user in the use of such data or in the development, use, or maintenance of any applications applied to or associated with the data or metadata.Please contact GIS.Librarian@FloridaDEP.gov for more information.

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.

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.

This scanned historical map from the University of Idaho Experimental Forest archives shows areas recommended for purchase in and around the proposed U of I Experimental Forest. 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.

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.

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).

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.

This scanned historical map from the University of Idaho Experimental Forest archives shows watersheds 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.

A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry “burned” in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow. Methods The following is a summary of data utilized for developing a bathymetric terrain for 2D hydraulic modeling using HEC-RAS. Data used for model calibration and validation is also discussed.

Available Data Cross-section elevation data were collected by the United States Army Corps of Engineers (USACE) Kansas City District at approximately 200-foot to 1000-foot increments at the confluence of the Big Blue River and the Kansas River near Manhattan, Kansas. The following equipment was used by two complete surveying teams: • Ohmex SonarMite single beam echo sounder SFX @ 200khz, • Ohmex SonarMite single beam echo sounder DFX @ 28kHz & 200kHZ, • Trimble R12i 0096 & 0098, • Trimble R8 1984 & 6282

The cross-section elevation data were collected by boat and supplemented by hand-carried, pole-mounted Trimbles on April 10 to 14, 2023. The USGS gage on the Big Blue River near Manhattan, KS (06887000) had an average discharge of 425 cfs during the field collection time period (Figure 1). A USGS gage downstream of the confluence, Kansas River at Wamego, KS (06887500) shows an average discharge of 780 cfs at the same time period (Figure 2).

Figure 1 (Refer to supplemental information file). USGS gage Big Blue R NR Manhattan, KS – 06887000 discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 425 cfs.

Figure 2 (Refer to supplemental information file). USGS gage Kansas River at Wamego, KS (06887500) discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 780 cfs. Wamego, KS is downstream of the Big Blue River and Kansas River confluence and represents combined flow for both tributaries.

Figure 3 (Refer to supplemental information file). Map of bathymetric cross-sections collected in April 2023 near Manhattan, KS. Arrows show flow direction. Inset is the data collection location relative to the state of Kansas.

Terrain The field data collection featured 56 cross-sections. HEC-RAS 6.3.1 was utilized to create a bathymetric surface by interpolating 1-D cross-sections, while a 1-m resolution USGS 3DEP terrain (2015) was used for the floodplain and surrounding areas. A more recent USGS 3DEP (2018) data was available but featured higher stream flow than the 2015 data collection and therefore, more of the channel was submerged. Overall, the difference between 2015 and 2018 had a mean deviation of ~0.04 feet, with a majority of the differences in the channel ranging between +/-0.5 feet. Islands in this reach are unvegetated and prone to movement, and therefore the exact channel form is uncertain. However, it is assumed that relative island areas are consistent throughout the reach, and 2015 LiDAR was used to delineate the most island area as possible.

To build the bathymetric terrain, a similar process as what was discussed in Harris et al. (2023), field collected data were imported into ArcGIS Pro 3.0 as a point shapefile. To preserve georeferencing, the point shapefile was segmented into groups of 3-4 cross-sections and these cross-sections were interpolated into mini-surfaces using the Inverse Distance Weighted (IDW) spatial analysis tool. These mini-surfaces were brought into HEC-RAS and cross-sections were drawn to intersect with these field surveyed locations. The 1-D cross-sections were then used to create a TIFF for the entire channel area. The 1D interpolation captures the channel centerline between measured cross-sections but meanders and channel widening may not be covered by the interpolated channel. The channel raster was broken into its component objects or “exploded”, in ArcGIS Pro using the Raster to Point tool. The points were then interpolated using the Inverse-Distance-Weighted interpolation tool (IDW). This creates a terrain that covers meanders and channel expansion while maintaining fidelity to the original channel raster.

Areas where the terrain was inundated at the time of LiDAR data collection are “flat” and referred to as a hydro-flattened surface. The Slope tool in ArcMap was used to delineate these hydro-flattened areas and a shapefile tracing unsubmerged islands was used. The IDW surface was clipped to the hydro-flattened extents and then mosaicked with the original 3DEP terrain to create a seamless bathymetric and topographic surface.

The field data collected in April 2023 (Figure 3) required supplemental information to cover a fish monitoring instance upstream of the bridge at Pillsbury Drive/177. In September 2021, the USACE Kansas City District collected sediment samples with XY-georeference and depth measurements. The LiDAR hydro-flattened surface was used to estimate the energy grade slope from the new cross-section to the recent field monitoring extents. The model scenario or “plan” on the April 2023 extents was run at a similar flow as was occurring in September 2021. The combination of water surface elevation at that flow (780 cfs), the energy grade slope in the 3DEP data and field measured depth in 2021 were used to estimate the elevation at the channel bed.

Land Cover Land cover was delineated using the Multi-Resolution Land Characteristic (MRLC) Consortium’s 2019 National Land Cover Data (NLCD) (MRLC 2016). Fifteen types of landcover were identified for this study area by the NLCD: Hay-Pasture, Shrub-Scrub, Developed Low Intensity, Developed Medium Intensity, Cultivated Crops, Deciduous Forest, Herbaceous, Develop Open Space, Developed High Intensity, Woody Wetlands, Emergent Herbaceous Wetland, Open Water, Mixed Forest, Barren Land, and Evergreen Forest. Manning’s n values were selected based on a range of n values along with a “Suggested Initial n” provided by Krest Engineers (2021) (Table 1). Table 1. A table representing a range of Manning’s n values, a suggested Manning’s n value, and percent imperviousness for each NLCD land cover type. (Krest Engineers, 2021)

Model Settings The 2D HEC-RAS mesh was set to 40-feet square, with breaklines to orient cell edges along areas of steep elevation change or to support model convergence. Boundary conditions were placed at three locations in the 2D flow area: the inflow of the Big Blue River (boundary condition type: flow hydrograph), the upstream end of the Kanas River (flow hydrograph), and the downstream end of the Kanas River (normal depth). An energy grade slope was given as 0.0005 ft/ft for the Big Blue River and 0.0003 ft/ft for the Kansas River. Advanced time step control adjustments were implemented using Courant’s Criterion, with a minimum Courant of 0.75 and a maximum of 3.

Calibration The suggested value from Krest Engineers (2021) was the initial Manning’s n used for each land cover type (Table 1). The hydraulic model was then run, and the Manning’s n was changed to better conform to water surface elevations observed during field data collection. Flows corresponding to the field collection dates were 415 cfs for the Big Blue River and 360 cfs for the Kansas River. These streamflows were determined by cross-referencing the field collection dates (April 10 to 14, 2023) to continuous monitoring data available from USGS at gages Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100). The 2D model simulation results were compared to the field-measured water surface elevations at each channel cross-section with the ArcGIS Zonal Statistics as Table tool. Model improvement was determined by calculating the Root Mean Square Error (RMSE) of the simulated water surface elevation to the field observed water surface elevation, and the Manning’s n values resulting in the lowest error were selected. Following calibration, the model has overall RMSE of 0.29 ft for depth. The final Manning’s n values used for all the following simulations are included in Table 2.

Land Cover

Mannings n

Open Water

0.025

Emergent Herbaceous Wetlands

0.05

Woody Wetlands

0.045

Herbaceous

0.025

Mixed Forest

0.08

Evergreen Forest

0.08

Deciduous Forest

0.1

Scrub-Shrub

0.07

Hay-Pasture

0.025

Cultivated Crops

0.02

Baren Land

0.023

Developed, Open Space

0.03

Developed, Low Intensity

0.06

Developed, Medium Intensity

0.08

Developed, High Intensity

0.12

Table 2. The selected Manning’s n per Landcover classification after calibration

Simulations Apart from the calibration simulations, further simulations were conducted to match additional fish data collection from July 17 – 21, 2023 and October 2- 6, 2023. USGS gages, Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100), were used to find the discharge rates (in cfs) during those fish sampling periods. While discharge was consistent throughout the weeks for some gages (Figures 4 and 7), others showed differences greater than 10% or 100 cfs (Figures 5 and 6). The gages that showed significant differences were divided into two sub-simulations for the lower and higher flows during that week.

USGS Streamflow Data for July 17 - 21, 2023

HEC RAS Scenario Description River Simulation Flow (cfs)

July_KS_LF July lower flow Big

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.

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.

This scanned historical map from the University of Idaho Experimental Forest archives shows ownership of lands 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.

This scanned historical map from the University of Idaho Experimental Forest archives shows general purpose roads, present forest roads and truck trails, and proposed forest roads and truck trails 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.

This dataset contains a set of UAV orthophotos acquired from September 2020 to April 2023. For technical reasons the dataset was divided into 4 subsections, to be viewed together in GIS. The pictures were acquired to map soil marks. Every picture of the dataset cointains its metadata. The photos were acquired with a Mavic Air 2 and processed with the software Agisoft Metashape to generate the orthophotos. The georeferencing was made in ArcgisPro thanks to ground control points.

This scanned historical map from the University of Idaho Experimental Forest archives shows boundaries of areas of principal experimental and demonstration areas, areas having especially high recreational value, potential fishing streams, public camp grounds, and the boundary of a proposed game reserve 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.

This scanned historical map from the University of Idaho Experimental Forest archives shows school district boundaries, schoolhouses, occupied houses, and vacant house 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.

This scanned historical map from the University of Idaho Experimental Forest archives shows tax delinquent and tax exempt lands 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.

This scanned historical map from the University of Idaho Experimental Forest archives shows soil classification, timber fringe, and agricultural land on Moscow Mountain and vicinity. 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.

This scanned historical map from the University of Idaho Experimental Forest archives shows burned areas and locations of sawmills and brick facilities on the Palouse Range. 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.