USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Cooling degree (F) days Heating degree (F) days This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

This data set provides a grid of quads and projection information to be used for rover operations and the informal geographic naming convention for the regional geography of Oxia Planum. Both subject to update prior to the landed mission.Contents This data set contains 4 shapefiles and 1 zipped folder.OxiaPlanum_GeographicFeatures_2021_08_26. Point shapefile with the names of geographic features last updated at the date indicatedOxiaPlanum_GeographicRegions_2021_08_26. Polygon shapefile with the outlines of geographic regions fitted to the master quad grid and last updated at the date indicated.OxiaPlanum_QuadGrid_1km. Polygon shapefile of 1km quad that will be used for ExoMars rover missionOxiaPlanum_Origin_clong_335_45E_18_20N. The center point of the Oxia Planum as defined by the Rover Operations and Control center and origin point used for the Quad gridCRS_PRJ_Equirectangular_OxiaPlanum_Mars2000.zip. Zip folder containing the projection information use for all the data associated with this study. These are saved in the ESRI projection (.prj) and well know text formal (.wkt)Guide to individual filesFile name (example) Description OxiaPlanum_QuadGrid_1km.cpg Text display information OxiaPlanum_QuadGrid_1km.dbf Database file OxiaPlanum_QuadGrid_1km.prj Projection information OxiaPlanum_QuadGrid_1km.sbx Spatial index file OxiaPlanum_QuadGrid_1km.shp Shape file data

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This layer has projections of VAR in decadal increments from 1950 to 2100 and for three Shared Socioeconomic Pathways (SSPs). The variables included are:Cooling degree (F) days Heating degree (F) days This layer uses data from the LOCA2 and STAR-ESDM downscaled climate models for the Contiguous United States. Further processing by the NOAA Technical Support Unit at CICS-NC and Esri are explained below.For each time and SSP, there are minimum, maximum, and mean values for the defined respective geography: counties, tribal areas, HUC-8 watersheds. The process for deriving these summaries is available in Understanding CRIS Data. The combination of time and geography is available for a weighted ensemble of 16 climate projections. More details on the models included in the ensemble and the weighting methodologies can be found in CRIS Data Preparation. Other climate variables are available from the CRIS website’s Data Gallery page or can be accessed in the table below. Additional geographies, including Alaska, Hawai’i and Puerto Rico will be made available in the future.GeographiesThis layer provides projected values for three geographies: county, tribal area, and HUC-8 watersheds.County: based on the U.S. Census TIGER/Line 2022 distribution. Tribal areas: based on the U.S. Census American Indian/Alaska Native/Native Hawaiian Area dataset 2022 distribution. This dataset includes federal- and state-recognized statistical areas.HUC-8 watershed: based on the USGS Washed Boundary Dataset, part of the National Hydrography Database Plus High Resolution. Time RangesProjected climate threshold values (e.g. Days Over 90°F) were calculated for each year from 2005 to 2100. Additionally, values are available for the modeled history runs from 1951 - 2005. The modeled history and future projections have been merged into a single time series and averaged by decade.Climate ScenariosClimate models use future scenarios of greenhouse gas concentrations and human activities to project overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Three different SSPs are available here: 2-4.5, 3-7.0, and 5-8.5 (STAR does not have SSP3-7.0). The number before the dash represents a societal behavior scenario. The number after the dash indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP 2-4.5 currently aligns with the international targets of the COP-26 agreement. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, providing a worst-case scenario if reductions in greenhouse gasses are not undertaken. Data ExportExporting this data into shapefiles, geodatabases, GeoJSON, etc is enabled.

Global prevalence of non-perennial rivers and streamsJune 2021prepared by Mathis L. Messager (mathis.messager@mail.mcgill.ca)Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.These datasets have been generated with source code located at messamat.github.io/globalirmap/.Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. 2. Repository contentThe data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):— GIRES_v10_gdb.zip/ : file geodatabase in ESRI® geodatabase format containing two feature classes (zipped) |——— GIRES_v10_rivers : river network lines |——— GIRES_v10_stations : points with streamflow summary statistics and metadata— GIRES_v10_shp.zip/ : directory containing ten shapefiles (zipped) Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— GIRES_v10_rivers_af.shp : Africa |——— GIRES_v10_rivers_ar.shp : North American Arctic |——— GIRES_v10_rivers_as.shp : Asia |——— GIRES_v10_rivers_au.shp : Australasia|——— GIRES_v10_rivers_eu.shp : Europe|——— GIRES_v10_rivers_gr.shp : Greenland|——— GIRES_v10_rivers_na.shp : North America|——— GIRES_v10_rivers_sa.shp : South America|——— GIRES_v10_rivers_si.shp : Siberia|——— GIRES_v10_stations.shp : points with streamflow summary statistics and metadata— Other_technical_documentations.zip/ : directory containing three documentation files (zipped)|——— HydroATLAS_TechDoc_v10.pdf : documentation for river network framework|——— RiverATLAS_Catalog_v10.pdf : documentation for river network hydro-environmental attributes|——— Readme_GSIM_part1.txt : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive— README_Technical_documentation_GIRES_v10.pdf : full documentation for this repository3. Data format and projectionThe geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).4. License and citations4.1 License agreement This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

Population Density : This vector dataset provides the population density by commune in Cambodia, as provided by Cambodian Demographic Census 2008 (Ministry of Planning, National Institute of Statistics). Dataset were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.Urban Density in Cambodia (2011) : This vector dataset provides the urban density in Cambodia, as given by the United Nations Population Fund (UNFPA). Dataset were provided to Open Development Cambodia (ODC) by Save Cambodia's Wildlife's Atlas Working Group.Population Projections for 2030 in Cambodia (2010) : This dataset provides projected population of 2030, projected annual growth rate in each province in Cambodia, given by National Institute of Statistics and the United Nations. Data were provided to Open Development Cambodia (ODC) in vector format by Save Cambodia's Wildlife's Atlas Working Group.River networks of Cambodia : Vector polyline data of river networks in Cambodia. Attributes include: name of river, name of basin, name of sub-basin, Strahler number.Canals in Cambodia (2008) : This dataset is included geographical locations of canals and types of canal such as earthen, levee and masonry. The data is released by Department of Geography of Ministry of Land Management, Urban Planning, and Construction of Cambodia, and then it is contributed by Office for the Coordination of Humanitarian Affairs (OCHA) and shared on Humanitarian Data Exchange (HDX). ODC's map and data team has collected the data from HDX website in Shapefile format and re-published it on ODC's website.Special economic zone in Cambodia (2006-2019) : This dataset describes the information of special economic zone (SEZ) in Cambodia from 2006 to 2019. The total number of 42 SEZ is recorded. The data was collected from many sources by ODC’s mappers such as the royal gazette of Cambodia's government, and reports of the governmental ministries in hard and soft copies of pdf format. Geographic data is encoded in the WGS 84, Zone 48 North coordinate reference system.Road and railway networks in Cambodia (2012- 2019) : Road networks are produced by Open Street Map. ODC's map and data team extracted the data in vector format. Moreover, the polyline data of railway​ given by Save Cambodia's Wildlife's Atlas Working Group in Cambodia for two statuses such as existing, proposed new lines in Cambodia.Forest cover in Cambodia (2015-2018) : This forest cover is extracted from the Forest Monitoring System (https://rlcms-servir.adpc.net/en/forest-monitor/) which is developed by SERVIR-Mekong and the Global Land Analysis and Discovery Lab (GLAD) from University of Maryland. The definition of forest for this dataset is the tree canopy greater than 10% with height more than 5 meters.Schools in flood-prone area 2013 (information 2012-2014) : This dataset is created by clipping between Cambodia flood-prone areas in 2013 dataset and Basic information of school dataset to identify schools are under the flood extend in 2013. The basic information of school contains the spatial location of school, the attribute information in 2014, and total enrollment in 2012.Basic map of Cambodia (2014) : These datasets contain three different types of administrative boundary levels: provincial, district and commune which were contributed by Office for the Coordination of Humanitarian Affairs (OCHA) to Humanitarian Data Exchange (HDX). The datasets were obtained from the Department of Geography of Ministry of Land Management, Urban Planning and Construction (MLMUPC) in 2008 and then unofficially updated in 2014 by referring to Sub-decrees on administrative modifications. Most Recent Changes: New province added (Tbong Khmum), with underlying districts and communes.Land cover in Cambodia (2012- 2016) : The land cover is extracted from the Regional Land Cover Monitoring System (https://rlcms-servir.adpc.net/en/landcover/) which is developed by SERVIR-Mekong. The primitives are calculated from remote sensing indices which were made from yearly Landsat surface reflectance composites. The training data were collected by combining field information with high-resolution satellite imagery.Cropland in Cambodia : This dataset contains information of cropland and location of croplands in Cambodia which was downloaded from World Food Programme GeoNode (WFPGeoNode) using data in 2013 from​ the Department of Land and Geography of the Ministry of Land Management, Urban Planning and Construction.Community Fisheries Map for Cambodia (2011) : This dataset provides 2011 geographic boundaries, size and the number of villages covered by each community fishery for which coordinates are available in Cambodia, as given by the Fisheries Administration. For those community fisheries sites without coordinates, locations are given as the center points of communes and metrics are taken from the Commune Database of 2011. Geographic data is encoded in the WGS 84 coordinate reference system. Data were provided to ODC in vector format by Save Cambodia's Wildlife's Atlas Working Group.Digital Elevation Model (DEM 12.5 m) in 2010 : This raster dataset provides the Digital Elevation Model in the world. Dataset were provided to ASF Data Search Vertex by EarthData. This dataset has high resolution terrain at 12.5 meter. Alaska Satellite Facility (ASF) : making remote-sensing data accessible. ASF operates the NASA archive of synthetic aperture radar (SAR) data from a variety of satellites and aircraft, providing these data and associated specialty support services to researchers in support of NASA’s Earth Science Data and Information System (ESDIS) project.Function Area : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Tourism area (Museum, Attraction) : This dataset are produced by Open Street Map. The data extracted the data in vector format (point feature).Entity : Royal Government of Cambodia, Ministry of Planning, National Institute of Statistics; Cambodian Demographic Census 2008. Phnom Penh, 2008; Save Cambodia's Wildlife; In Atlas of Cambodia: maps on socio-economic development and environment;Time period : 2006-2018Frequency of update : Always up-to-dateGeo-coverage() : NationalGeo-coverage: National() : Cambodia

Description

This dataset contains the projections of future freshwater demands of the energy sector (primary energy supply and transformation in power plants and oil refineries) in the EU and the United Kingdom for the period 2015-2050, in 5-year steps. The projections are estimated by the combination of water withdrawal and consumption factors for different energy technologies under six energy scenarios, at national and NUTS2 level, as described in the JRC technical report: Hidalgo Gonzalez, I., Medarac, H. and Magagna, D., Projected freshwater needs of the energy sector in the European Union and the UK, EUR 30266 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-19829-1 (online), doi:10.2760/796885 (online), JRC121030.

Contributors

"HIDALGO GONZÁLEZ, Ignacio MEDARAC, Hrvoje MAGAGNA, Davide"

Update date:

Jun 18, 2020

Resource:

Joint Research Centre

Authors

Hidalgo González, Ignacio; Medarac, Hrvoje; Magagna, Davide

Licence

Attribution 4.0 (CC BY 4.0)

Notes

All figures provided in the tab "Projections" are expressed in million m3. NUTS2 primary energy production WW: freshwater withdrawal for primary energy production at NUTS2 level NUTS2 primary energy production WC: freshwater consumption for primary energy production at NUTS2 level NUTS2 refining WW: freshwater withdrawal for oil refining at NUTS2 level NUTS2 refining WC: freshwater consumption for oil refining at NUTS2 level NUTS2 electricity generation PP WW: freshwater withdrawal for electricity generation at NUTS2 level NUTS2 electricity generation PP WC: freshwater consumption for electricity generation at NUTS2 level NUTS2 codes: https://ec.europa.eu/eurostat/web/nuts/background

Legal notice

The information made available is property of the Joint Research Centre of the European Commission. Neither the European Commission nor any person acting on its behalf is responsible for the use which might be made of these projections.

Permission to use

Reproduction of the data is authorized provided the source is appropriately acknowledged.

© European Union 2020

Reference

https://doi.org/10.5281/zenodo.3899962

The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive. This model is designed to extract distribution wires at the street level. Its predictions for high-tension transmission wires are less consistent with changes in geography as compared to street-level distribution wires. In the case of high-tension transmission wires, a lower ‘recall’ value is observed as compared to the value observed for low-lying street wires and poles.Using the modelFollow the guide to use the model. The model can be used with ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with point geometry (X, Y and Z values). Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: Classcode Class Description 0 Background Class 14 Distribution Wires 15 Distribution Tower/PolesApplicable geographiesThe model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the RandLANet model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Background (0) 0.999679 0.999876 0.999778 Distribution Wires (14) 0.955085 0.936825 0.945867 Distribution Poles (15) 0.707983 0.553888 0.621527Training dataThis model is trained on manually classified training dataset provided to Esri by AAM group. The training data used has the following characteristics: X, Y, and Z linear unitmeter Z range-240.34 m to 731.17 m Number of Returns1 to 5 Intensity1 to 4095 Point spacing0.2 ± 0.1 Scan angle-42 to +35 Maximum points per block20000 Extra attributesNone Class structure[0, 14, 15]Sample resultsHere are a few results from the model.