MetadataThis dataset shows proposed and installed wind turbine locations. Until installed, all proposed locations and turbine specifications are subject to change. For the proposed locations, in most cases, the full arrays of turbines shown are the maximum proposed build out within the individual design envelope of the project. In some cases, more than one proposed layout is provided. If no specific configuration option is shown in the attributes, then all the locations are considered the preferred option. These data are subject to change and will be updated as new publicly available information is released. Data are provided by the lessee for project planning review and permitting uses.

Wind turbines are an important source of renewable energy. There is a rapid growth in the number of wind turbine installations across the globe. These installations are visible in high resolution aerial imagery. However, it can be tedious to analyze imagery and mark these installations manually. This deep learning model can automate the detection of wind turbines by interpreting high resolution imagery.Using the modelFollow the guide to use the model. This model requires deep learning libraries to be installed, install them using Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (60 cm) imagery.OutputFeature class containing detected wind turbines.Applicable geographiesThe model is expected to work well across USA and Netherlands.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.96.Training dataThis model has been trained on an Esri proprietary wind turbines dataset.Sample resultsHere are a few results from the model.

CDFW BIOS GIS Dataset, Contact: USWTDB United States Wind Turbine Database, Description: This data set provides industrial-scale onshore wind turbine locations in the United States through July 22, 2013, corresponding facility information, and turbine technical specifications.

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. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group, the U.S. Geological Survey (USGS) Energy Resources Program, and the American Clean Power Association (ACP). The database is being continuously updated through collaboration among LBNL, USGS, and ACP. Wind turbine records are collected and compiled from various public and private sources, digitized or position-verified from aerial imagery, and quality checked. Technical specifications for turbines are obtained directly from project developers and turbine manufacturers, or they are based on data obtained from public sources.Data accessed from here: https://eerscmap.usgs.gov/uswtdb/

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

Last update: August 20, 2024OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity.Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key.Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/

This feature service provides a publicly available, spatially referenced, national dataset of wind turbine locations and their corresponding facility information and turbine technical specifications. The project compiled wind turbine information from the Federal Aviation Administration (FAA), Lawrence Berkeley National Laboratory (LBNL), the American Wind Energy Association (AWEA), and United States Geological Survey (USGS) dataset, as well as online sources.About the Database

The information in the abstract is translated from the archeological report: A new wind turbine with a connection road, a crane site and an electric cable that will be connected to a nearby power line, are planned within the property Rävsjö 1:1, Fivelstad parish, Motala municipality, Östergötland county. Several stone axes and other prehistoric remains have been found in the vicinity. The Swedish National Heritage Board's contract archeology division, UV Öst, has thus performed a preliminary investigation, conducted as an archaeological inspection, in April 2008. A total of 14 trenches were excavated on the field. A prehistoric hearth (RAÄ 85) was found in one of the trial trenches on the spot the wind turbine was planned. The hearth was excavated and interpreted as being solitary, since no other remains were found in the nearby trenches. The electric cable will be laid along an existing road and connected between the wind turbine and Rävsjö farm. Three trial trenches were excavated along the highest point of the cable stretch. In one of the trenches two hearths were found and excavated, of which one might be a furnace. The remains are probably part of the outskirt of a prehistoric settlement, of which the most part is judged to be located east of the road. The developer was able to avoid the the settlement remains (registered as RAÄ 84) and no further archaeological measures are thus suggested.

Purpose:

The information in the purpose is translated from the archeological report: The aim of the preliminary investigation was to determine whether the construction of the new wind turbine would affect any archaeological remains. Any remains found were to be avoided, if possible, by locating the power line, road and wind turbine where the remains would not be damaged.

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

City Development Plan Policy and Proposals

Web Map Service that supports the IRENA Global Atlas for Renewable Energy. Location and capacity of existing wind farms.

Wind Turbine Build OutThe Wind Turbine Build Out display provides an overview of determined and proposed wind turbine/Met Tower (w/WT Farm) projects within the continental United States. When latitude/longitude coordinates are submitted, the map will re-center and display the wind turbine build out within a 48 nautical mile radius of the specified location. This display is provided to assist developers during the planning phase and to identify areas where cumulative impact may become a factor in the aeronautical study process. The use of this display does not exempt any person(s) from the filing requirements described in Title 14 of the Code of Federal Regulations Part 77.

The Geospatial Energy Mapper (GEM) provides mapping data and analysis tools for planning energy infrastructure in a geographic context. GEM is an interactive web-based decision support system that allows users to locate areas with high suitability for clean power generation and potential energy transmission corridors in the United States. Users can browse and download data layers, or create a custom suitability model to identify areas for energy development. GEM is built on the core data and capabilities of the Energy Zones Mapping Tool (EZMT). GEM features an improved user interface, updated data, and additional capabilities. Argonne National Laboratory hosts the tool with funding from the U.S. Department of Energy (DOE) Office of Electricity.This model is specific to offshore wind turbines (120 m). We modified the default model parameters to remove the habitat and protected areas elements. As a result, this layer represents the threat of energy development from a utility perspective and does not consider whether development is suitable from a conservation perspective. We considered a threat-only layer to be more complementary to the Southeast Blueprint, which already depicts conservation priorities. In addition, the source data for the habitat and protected areas elements were outdated and provided inconsistent coverage across the SECAS geography. We kept the default weights for all remaining model input layers as shown in the bulleted list below. The default and customizable models are downloadable from the GEM viewer. These data are provided for use in combination with the Blueprint and other data available on the SECAS Atlas. We chose the 120 m model (rather than the 80 m or 100 m models) based on the overall trend of increasing turbine size, and the reduction in wind shear and increase in available wind speed at higher altitudes (Department of Energy 2023).Bathymetry (weight = 2)Distance (m) to Substation (>= 115kV) (weight = 1)Distance in Meters to a Shoreline (weight = 1)Mean Annual Wind Speed (Offshore at 120m) (weight = 5)Navigable Waterways (weight = 2)Unexploded Ordnance (Offshore) (weight = 1)For more information on these model parameters or to view and download this layer from its native mapper:Visit https://gem.anl.gov/toolSelect "Find suitable areas" from the sidebar on the leftChoose wind technologyChoose the Land-based wind turbine (120 m) modelView/download the default model or customize as described abovePlease direct any questions to gem@anl.gov.

Data extracted from OpenStreetMap using Overpass Turbo.

Gritting routes extracted from OpenStreetMap using Overpass Turbo and collated from Local Authorities within West Midlands Combined Authority conurbation.

Wind turbine locations from planning applications in The Highland Council area. Includes the following statuses: Constructed / Constructed-Removed / Under Construction / Approved / In Planning / Refused / Expired / Withdrawn / Scoping and Screening.Gemini metadata record is at https://www.spatialdata.gov.scot/geonetwork/srv/eng/catalog.search#/metadata/1ab6d829-9f6d-4fee-bd4a-f267d01bb292