Development Maps (2020)Series of Maps and Statistics detailing Proposed, Approved, Under Construction, and Completed Development in the City of Jersey City, Hudson County, New Jersey.

This is the open access data set from the SySTEM 2020 online map of informal and non-formal science learning accross Europe.

https://system2020.education/the-map/

The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States from 1990-2020. These data are useful within a GIS for mapping and analysis at national, state, and local levels. Data are available as a geodatabase and include information such as housing densities for 1990, 2000, 2010, and 2020; wildland vegetation percentages for 1992, 2001, 2011, and 2019; as well as WUI classes in 1990, 2000, 2010, and 2020.This WUI feature class is separate from the WUI datasets maintained by individual forest unites, and it is not the authoritative source data of WUI for forest units. This dataset shows change over time in the WUI data up to 2020.Metadata and Downloads

Ballot to submit your vote for best maps of the 2020 KY GIS Conference

The original source of this data set comes from an extract out of the California Ag Permits (CAP) system in September 2020. The CAP system is used to track and inventory pesticide use permits. The information contained in the CAP database was created/edited during the pesticide use permit application process. The CAP data extract was refined and the boundaries edited to better display in a cartographic setting. The ranch map is a complex and constantly changing data set that must be viewed as a work in progress. While every effort has been made to produce data as accurately as possible, there may be, for various reasons, some missing or inaccurate boundaries and labels. In many cases the data is only as accurate as the source information and maps provided by the permittees/applicants.Due to the nature of ranches in Monterey County, there are many ranches and/or permittees working the same location. As a result, there are often multiple features stacked upon each other.DATA FIELDS:Field Name = DescriptionPermNum = Permit Number: the permittee’s ID number.Permittee = Permittee: the permittee’s name.RanchName = Ranch Name: the ranch's name.SiteID = Site ID: the ranch's site ID number.RMGISAcres = Ranch Map GIS Acres: acreage of the ranch's polygon.RanchPoly = Ranch Polygons: distinguish if the ranch is a single or multiple polygons. - single polygon (ranch is a single polygon). - multiple polygons (ranch is split into multiple polygons).

Final approved map by the 2020 California Citizens Redistricting Commission for California's United States Congressional Districts; the authoritative and official delineations of California's United States Congressional Districts drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.

This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of Lāna‘i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini et al. 2024. Full citation is listed in the larger work section of this XML file. Inputs: Ground control polygons used for model training and evaluation Ground control points used for independent pixel-level model validation Outputs: Raster 1. Species-specific land ...

Land use map based on LiDAR data and retrieved metrics. Spatial coverage is the LiDAR areas. The map reflects the status of the land use at the LiDAR acquisitions in January/February 2020. The spatial resolution of the maps is 10mx10m and 50mx50m resolution.

Final approved map by the 2020 California Citizens Redistricting Commission for the California State Senate; the authoritative and official delineations of the California State Senate drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.

Calculated trend values for the horizontal position of the coastline in relation to the Base Coastline for the year 2020. Figures and maps are processed annually in the coastal map book published by RWS WVL. Based on this book, the supplementation schedule for the year 2 years after measurement is determined. Depicted is the trend in deviation of the position of the coast line to be tested (TKL) in relation to the Base Coastline. In the underlying table all test parameters calculated by testing software MorphAn. The colouring indicates the direction of the trend (seaward/landward) and indicates the location of the tkl (seaward/landward).

Calculated trend values ​​for the horizontal position of the coastline in relation to the Basic Coastline for the year 2020. Figures and maps are processed annually in the coastline map book published by RWS WVL. The supplementation schedule for the year 2 years after measurement is determined on the basis of this book. The trend in deviation of the position of the coastline to be tested (TKL) is shown in relation to the Base Coastline. In the underlying table all test parameters that are calculated by testing software MorphAn. The coloring indicates the direction of the trend (seaward/landward) and indicates the location of the tkl (seaward/landward).

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Training data were collected through using a combination of the following sources:Habitat Map of Scotland (ground polygons)2022 National Forest InventoryOrdnance SurveyHigh resolution imageryIn all cases the ground data were not used naively: a careful combination of at least two data sources were used to create each polygon, and checking against recent high resolution imagery to ensure each polygon was ‘pure’ (i.e. included only one class) and up to date (for example, if it was a forest polygon, the trees had not been cleared since the data were collected).Satellite remote sensing datasets used for mapping were Optical Sentinel 2 (S2), Synthetic Aperture Radar (SAR) Sentinel-1, descending and ascending ALOS-PALSAR 2. A complex set of machine learning algorithms were used to produce a Prediction Model, and ultimately a prediction of a class for each pixel.Through the project duration the sophistication of the models used increased, increasing accuracy and efficiency. For commercial reasons the details of the final algorithms used will not be revealed here. This dataset is a change map, which shows the land cover change that is predicted to have occurred between 2020 and 2022. The map is produced through a simple comparison between the 2020 and 2022 maps, where each instance of change identified is interpreted and assigned one of the following descriptors:

(i) Afforestation (ii) Tree removal (iii) Agriculture related (iv) Urban development (v) Forest growth (vi) Water gain (vii) Water loss (viii) Other changes

Please note, we believe these predicted changes, and others, are inaccurate, mainly due to inaccuracies we have identified in the 2020 map, along with improved methodologies and processes developed at Space Intelligence since the creation of the 2020 map.

Set of maps used for the Atlas of social and medico-social policies of the Métropole de Lyon with the titles, the card number, the legend and the page number in the Atlas.

This data package includes 40 geospatial rasters (maps) depicting various metrics about eelgrass (Zostera marina) coverage at Izembek Lagoon, Alaska, during 2016 and 2020. Two maps were produced from two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020. Spectral classes derived from each satellite image were annotated and mapped based on data collected in the field at plots within each spectral class.

These are the variable codes for the datasets released as part of the 2020 decennial census redistricting data.

This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (*.zip) file.

This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2020), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training areas were extracted as training data. An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results: overall accuracy: 88.4% class: user's accuracy / producer's accuracy (number of reference points n) forest: 95.0% / 93.8% (1410) low vegetation: 73.4% / 86.5% (844) water: 98.5% / 92.8% (69) built-up: 98.9% / 95.8% (983) bare soil: 23.9% / 82.9% (41) agriculture: 94.6% / 83.2% (1653) Incora report with details on methods and results: pending

The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 15 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 Radar and Sentinel-2 Optical Satellite images during the period late January 2020 – September 2020. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. The data has been split into the Ordnance Survey Ceremonial Counties and each county is given a three letter code. Please refer to the CROME specification document to see which county each CODE label represents.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial mapping products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. The imagery was classified using Random Forest (RF) Modeling to produce land cover maps with three main classifications - bare, vegetation, and shadows. An updated roads and trails map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate road and trail densities in the watershed. Separate metadata records for each product (Land_Cover_Maps_Scotts_Creek_Watershed_CA_2018_2020_2022_metadata.xml, and Roads_and_Trails_Map_Upper_Scotts_Creek_Watershed_CA _2022_metadata.xml) are provided on the ScienceBase page for each child item. Users should be aware of the inherent errors in remote sensing products.