This dataset consists of annotated high-resolution aerial imagery of roof shapes/geometries in Bonn, Germany, in the Ultralytics YOLO instance segmentation dataset format. Aerial imagery was sourced from OpenAerialMap, specifically from the Maxar Open Data Program. Roof shape labels and building outlines were sourced from OpenStreetMap. Images and labels are split into training, validation, and test sets, meant for future machine learning models to be trained upon, for both building segmentation and roof shape/geometry classification.The dataset is intended for applications such as informing studies on solar capacity estimation, urban morphology, 3D modeling, risk assessments, and other related fields. There are seven roof shape types: gabled, flat, skillion, hipped, gambrel, half-hipped, pyramidal, and mansard. Note: The data is in a .zip due to file upload limits. Please find a more detailed dataset description in the README.md

The purpose of the Virginia Address Point Geospatial Data Standard is to implement, as a Commonwealth ITRM Standard, the data file naming conventions, geometry, map projection system, common set of attributes, dataset type and specifications, and level of precision for the Virginia Address Point Dataset, which will be the data source of record at the state level for administrative boundary spatial features within the Commonwealth of Virginia.

This dataset represents synthetic generated data from CALLISTO data in RDF form. It contains the equivalent of 2 billion triples in TTL format.

Each entity contains:

• Crop category: "Grasland" and "Bouwland"
• Geo information: as Multipolygon in Well Known Text (WKT) format
• Geometry area
• Geometry length
• Object id
• Parcel
• Rdf:type owl:NamedIndividual

Trail Access Lines geospatial data.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

The Observation Well Project Geospatial Data layer is composed of 5 sublayers with point and line type geometries depicting observation wells and canals located near Cambridge canal in Nebraska. There are 2 point geometry type layers: bartley_canal_wells and Well_Water_Levels. There are 3 line geometry type layers: bartley_canal, cambridge_canal, and red_willow_canal.

There are 10 attribute fields for the Well_Water_levels layer: Canal Name, Condition, Code, Inspector, Tapedown Measurement, Notes, Date and Time, Cutline Measurement, Water Level, and Photos and Files. There are two attribute fields for the bartley_canal_wells. There are 10 attribute fields for bartley_canal, cambridge_canal, and red_willow_canal layers: Department of Natural Resources Use, Measurement (Feet), Plan, Legal Description, Sub Description, Section, Township, Range, Range Direction, and Canal Name.

This data did not undergo a quality assurance and quality control process and as a result there are some errors and inconsistencies in the data. For example, in the Well_Water_levels layer the Date and Time field contains illogical dates and times, the Condition field sometimes contains text and numbers, and the Canal Name field has spelling and capitalization inconsistencies.

The schemas were created by Frenchman Cambridge Irrigation District. Frenchman Cambridge Irrigation District collected the data using a GPS unit for the S&T Project 19042: Developing a Collaborative Environment for Sharing Geographic Information Systems (GIS) Data Between Reclamation and Irrigation Districts.

Unlock the power of 164M+ verified locations across 220+ countries with high-precision geospatial data. Featuring 50+ enriched attributes including coordinates, building type, and geometry. Our AI-powered dataset ensures unmatched accuracy through advanced deduplication and enrichment. With 30+ years of industry expertise, we deliver trusted, customizable data solutions for mapping, navigation, urban planning, and marketing, empowering smarter decision-making and strategic growth.

Key use cases of Geospatial data have helped our customers in several areas:

1. Gain a Competitive Edge with Smarter Mapping : Use geospatial data to analyse competitors, identify high-traffic zones, and optimize locations for maximum impact.
2. Enhance Navigation & Location-Based Engagement : Improve turn-by-turn navigation, EV charging station discovery, and real-time travel insights for seamless customer experiences.
3. Find High-Value Locations for Business Growth : Leverage geospatial intelligence to select profitable retail sites, franchise locations, and warehouses with precision.
4. Streamline Deliveries & Address Validation : Improve shipping accuracy, reduce failed deliveries, and optimize courier routes for better customer satisfaction.
5. Drive Smarter Decisions with Spatial Analysis : Utilize location intelligence for disaster risk assessment, public health campaigns, and agricultural planning.

The purpose of the Virginia Public Safety Answering Point (PSAP) and Emergency Service Boundary Geospatial Data Standard is to implement, as a Commonwealth ITRM Standard, the data file naming conventions, geometry, map projection system, common set of attributes, dataset type and specifications, and level of precision for the Virginia Public Safety Answering Point (PSAP) and Emergency Service Boundary Datasets, which will be the data source of record at the state level for these types of spatial features within the Commonwealth of Virginia.

The purpose of the Virginia Administrative Boundary Geospatial Data Standard is to implement, as a Commonwealth ITRM Standard, the data file naming conventions, geometry, map projection system, common set of attributes, dataset type and specifications, and level of precision for the Virginia Administrative Boundaries Dataset, which will be the data source of record at the state level for administrative boundary spatial features within the Commonwealth of Virginia.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

The data have been collected in the context of a study aiming to investigate which geometric properties of the located object affects the apprehension of a spatial description and disentangle whether the information concerning its orientation (axis), its direction (front/rear) or a combination of these two factors, gives rise to conflict. In the repository people can find accuracy as well as latency data. R scripts used for Multinomial Logistic and Multilevel analyses have also been included.

The Old Cambridge Canal Wells Geospatial Data layer is a point type geometry layer depicting observation wells along Cambridge canal in Nebraska. It was created from legacy data developed in ArcMap that was then imported into ArcGIS Pro. There are two attribute fields for this layer: ID, and Location. This data did not undergo a quality assurance and quality control process and as a result there are some errors and inconsistencies in the data.The schema was created by Frenchman Cambridge Irrigation District. Frenchman Cambridge Irrigation District collected the data using a GPS unit for the S&T Project 19042: Developing a Collaborative Environment for Sharing Geographic Information Systems (GIS) Data Between Reclamation and Irrigation Districts.RISE Catalog Item 128534: https://data.usbr.gov/catalog/7980/item/128534To download data, please use the RISE Geospatial Open Data site: https://rise-usbr.opendata.arcgis.com/datasets/ab8e2de29ccb4b64a2f013e2b75807d6

A conference paper describing GIS tools developed in support of the blast loss estimation capability for the Australian Reinsurance Pool Corporation. The paper focus is on GIS tools developed for: …Show full descriptionA conference paper describing GIS tools developed in support of the blast loss estimation capability for the Australian Reinsurance Pool Corporation. The paper focus is on GIS tools developed for: exposure database construction and integration of a number of datasets including 3D building geometry

Unlock the power of 51M+ verified locations across Europe with high-precision geospatial data. Featuring 50+ enriched attributes including coordinates, building type, and geometry. Our AI-powered dataset ensures unmatched accuracy through advanced deduplication and enrichment. With 30+ years of industry expertise, we deliver trusted, customizable data solutions for mapping, navigation, urban planning, and marketing, empowering smarter decision-making and strategic growth.

Key use cases of Geospatial data have helped our customers in several areas:

1. Gain a Competitive Edge with Smarter Mapping : Use geospatial data to analyse competitors, identify high-traffic zones, and optimize locations for maximum impact.
2. Enhance Navigation & Location-Based Engagement : Improve turn-by-turn navigation, EV charging station discovery, and real-time travel insights for seamless customer experiences.
3. Find High-Value Locations for Business Growth : Leverage geospatial intelligence to select profitable retail sites, franchise locations, and warehouses with precision.
4. Streamline Deliveries & Address Validation : Improve shipping accuracy, reduce failed deliveries, and optimize courier routes for better customer satisfaction.
5. Drive Smarter Decisions with Spatial Analysis : Utilize location intelligence for disaster risk assessment, public health campaigns, and agricultural planning.

These datasets come from Google Earth Engine and are used in ACEA challenge

The first is daily time series from Copernicus ECMWF ERA5 Daily aggregates, extracted using weather station geolocations. Time series range from 1998 to 2020. 48 different stations are located in Italy.
The extraction have been done with this script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series(lat, lon, start, end, product_name, sf):
  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .filterDate(start, end)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  return df

if _name_ == "_main_":
  ee.Initialize()
  for i in locations.keys(): # locations is a dictionnary containing latitude and longitude
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    while True:
      try: 
        output = extract_time_series(latitude,
                 longitude,
                 '1998-01-01',
                 '2020-01-01',
                 'ECMWF/ERA5/DAILY',
                 1)
        break
      except: 
        print(i + " 1 fail")
        continue              
    name =PATH + i + "_1.csv"        
    output.to_csv(name, index=True)

The second dataset is Forecasted Weather from Global Forecast System.
The purpose of this dataset is to provide forecasted rainfall and temperature for the 16 coming days. Creation_time column is the released date while forecast_hours is forecasted weather for horizon : creation_time + forecast_hours. Time series are daily and range from 2015 to 2020. Unfortunately, there are missing values.
Python script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series_gfs(lat, lon, start, end, product_name, sf, h):

  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .select(['total_precipitation_surface','temperature_2m_above_ground'])\
    .filterDate(start, end)\
    .filterMetadata('forecast_hours', 'equals', h)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  df=df[df["system:footprint"] == "LinearRing"]

  return df
if _name_ == "_main_":

  ee.Initialize()
  horizon = [i*24 for i in range(1,17)]
  for i in locations.keys():
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    
    for j in horizon:
      while True:
        try:
          output = extract_time_series_gfs(latitude,
                 longitude,
                 '2015-07-01',
                 '2020-08-01',
                 'NOAA/GFS0P25',
                 1,
                 j)
          break
        except:
          print(i + " " + str(j) +" 1 fail")
          continue
      name = PATH + i + "_" + str(j) +"_1.csv"
           
      output.to_csv(name, index=True)

To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

Existing methods for calculating directional relations in polygons (i.e. the directional similarity model, the cone-based model, and the modified cone-based model) were compared to human perceptions of change through an online survey. The results from this survey provide the first empirical validation of computational approaches to calculating directional relations in polygonal spatial data. We have found that while the evaluated methods generally agreed with each other, they varied in their alignment with human perceptions of directional relations. Specifically, translation transformations of the target and reference polygons showed greatest discrepancy to human perceptions and across methods. The online survey was developed using Qualtrics Survey Software, and participants were recruited via online messaging on social media (i.e., Twitter) with hashtags related to geographic information science. In total sixty-one individuals responded to the survey. This survey consisted of nine questions. For the first question, participants indicated how many years they have worked with GIS and/or spatial data. For the remaining eight questions, participants ranked pictorial database scenes according to degrees of their match to query scenes. Each of these questions represented a test case that Goyal and Egenhofer (2001) used to empirically evaluate the directional similarity model; participants were randomly presented with four of these questions. The query scenes were created using ArcMap and contained a pair of reference and target polygons. The database scenes were generated by gradually changing the geometry of the target polygon within each query scene. The relations between the target and reference polygon varied by the type of movement, the scaling change of the polygon, and changes in rotation. The scenarios were varied in order to capture a representative range of variability in polygon movements and changes in real world data. The R statistical computing environment was used to determine the similarity value that corresponds with each database scene based on the directional similarity model, the cone-based model, and the modified cone-based model. Using the survey responses, the frequency of first, second, third, etc. ranks were calculated for each database scene. Weight variables were multiplied by the frequencies to create an overall rank based on participant responses. A rank of one was weighted as a five, a rank of two was weighted as a four, and so on. Spearman’s rank-order correlation was used to measure the strength and direction of association between the rank determined using the three models and the rank determined using participant responses.

Digital data of 3D models featuring geometry model, texture map and textual attribute to represent the geometrical shape, appearance and position of three types of ground objects i.e. Building, Infrastructure and Terrain in Max, 3ds, FBX and VRML formats.

This download service allows access to the set of information layers published in the Spatial Data Infrastructure of Navarra and that correspond to the public data of the SITNA. A WFS service provides access to querying the attributes of a geographic phenomenon (feature), represented in vector mode, with a geometry described by a set of coordinates. Usually the data provided is in GML format. A WFS allows not only to visualise the information as a WMS allows, but also to consult it freely.

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. Wind turbine records are collected and compiled from various public and private sources, digitized and position-verified from aerial imagery, and quality checked. The USWTDB is available for download in a variety of tabular and geospatial file formats, to meet a range of user/software needs. Dynamic web services are available for users that wish to access the USWTDB as a Representational State Transfer Services (RESTful) web service. Archived from https://energy.usgs.gov/uswtdb/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives