Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

Access APIAdministrative Boundaries Theme - Federal Electoral Division Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new …Show full description Access APIAdministrative Boundaries Theme - Federal Electoral Division Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.NSW Federal Electoral Division is a feature class which represents a gazetted area of a federal electoral division that has been defined by redistribution. NSW Federal Electoral Division is a feature class within the Administrative boundaries theme. It represents a gazetted area of a federal electoral division that has been defined by redistribution. Australian Electoral Commission is responsible for this dataset. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the Administrative boundaries. Features are typically positioned in alignment within the extents of the cadastral polygons and NSW Lot and Property data changes impact this dataset. This dataset is current as per last redistribution. Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the Feature Class and Domain Name descriptions for the NSW Administrative Boundaries Theme can be found in the NSW Cadastral Delivery Model Data Dictionary Some of Spatial Services Datasets are designed to work together for example NSW Address Point and NSW Address String (table), NSW Property (Polygon) and NSW Property Lot (table) and NSW Lot (polygons). To do this you need to add a Spatial Join. A Spatial Join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

Class imbalance is a major problem in classification, wherein the decision boundary is easily biased toward the majority class. A data-level solution (resampling) is one possible solution to this problem. However, several studies have shown that resampling methods can deteriorate the classification performance. This is because of the overgeneralization problem, which occurs when samples produced by the oversampling technique that should be represented in the minority class domain are introduced into the majority-class domain. This study shows that the overgeneralization problem is aggravated in complex data settings and introduces two alternate approaches to mitigate it. The first approach involves incorporating a filtering method into oversampling. The second approach is to apply undersampling. The main objective of this study is to provide guidance on selecting optimal resampling methods in imbalanced and complex datasets to improve classification performance. Simulation studies and real data analyses were performed to compare the resampling results in various scenarios with different complexities, imbalances, and sample sizes. In the case of noncomplex datasets, undersampling was found to be optimal. However, in the case of complex datasets, applying a filtering method to delete misallocated examples was optimal. In conclusion, this study can aid researchers in selecting the optimal method for resampling complex datasets.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-‐rectangular Public Land Survey System (PLSS) data set.

This data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-‐ Attribute section of this metadata describes these components in greater detail.

The CADNSDI or the Cadastral Publication Data Standard is the cadastral data component of the NSDI. This is the publication guideline for cadastral data that is intended to provide a common format and structure and content for cadastral information that can be made available across jurisdictional boundaries, providing a consistent and uniform cadastral data to meet business need that includes connections to the source information from the data stewards. The data stewards determine which data are published and should be contacted for any questions on data content or for additional information. The cadastral publication data is data provided by cadastral data producers in a standard form on a regular basis.

Cadastral publication data has two primary components, land parcel data and cadastral reference data. It is important to recognize that the publication data are not the same as the operation and maintenance or production data. The production data is structured to optimize maintenance processes, is integrated with internal agency operations and contains much more detail than the publication data. The publication data is a subset of the more complete production data and is reformatted to meet a national standard so data can be integrated across jurisdictional boundaries and be presented in a consistent and standard form nationally.

Kasviplanktontietojärjestelmä sisältää lähinnä ympäristö- ja aluehallinnon sisä- ja rannikkovesien seurantojen ja velvoitetarkkailujen kvantitatiivisia kasviplankton aineistoja. Aineisto sisältää ajallista ja paikallista tietoa kasviplanktonin määrästä, leväryhmien keskinäisistä suhteista ja lajiston koostumuksesta Suomen sisä- ja rannikkovesien sekä avomeren havaintopaikoista, sekä pitkäaikaisia trendejä seurannan havaintopaikoilla kasviplanktonin määrästä ja koostumuksesta. Järjestelmään pääsee Hertta ja Avoin tieto -palveluiden kautta. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0). Seuranta-aineisto vuodesta 1963 lähtien. Rekisteri sisältää myös muita tietoja, joista ensimmäiset ovat vuodelta 1921. Lajitieto osio kattaa koko Itämeren lajiston. Käyttötarkoitus: Aineisto on tarkoitettu lähinnä ympäristöhallinnon käyttöön. Kasviplanktonin määrä ja koostumus on tärkeä mittari vesipuitedirektiivin edellyttämässä vesien tilan luokittelussa. Kasviplanktonaineistoa on käytetty ekologisten luokkarajojen asettamiseen ja järvien tilan luokittelutekijänä. Aineiston avulla on kehitetty luokittelumuuttujia. Aineistoa käytetään myös enenevässä määrin yhteistyössä yliopistojen kanssa, esimerkiksi tutkimushankkeissa ja opinnäytetöissä. Lisätietoja: Erikostutkija Marko Järvinen, Syke, Vesikeskus Phytoplankton information system – KPLANK Phytoplankton information system contains mainly environment- and regional administrations’ quantitative phytoplankton data from statutory and environmental monitoring of inland and coastal waters. Data contains temporal and spatial information such as, abundance of phytoplankton, interrelation between taxonomic groups and species composition in Finnish inland and coastal waters, but also offshore monitoring stations. Long-term trends of composition and abundance of phytoplankton from monitoring stations is also available. The data is part of Syke's open access datasets (CC BY 4.0). The information system is accessible by Hertta and Open data-services. Monitoring data is available from 1963 onwards. The database also contains other information from 1921 onwards. Species list covers all the phytoplankton species in the Baltic Sea. Purpose of use: Data is meant mainly for environment administrations use. Abundance and species composition of phytoplankton are important indicators when assessing state of the water bodies as required by the Water Framework Directive. Phytoplankton data has been used for setting ecological class boundaries and classification factor for the lakes. Data has been used for developing classification variables. Data is also increasingly used in collaboration with universities, for example in research projects and theses. More information: Head of unit Marko Järvinen, Syke, Freshwater Centre

This dataset is refreshed on a weekly basis from the datasets the team works on daily.Last update date: 06 March 2025.National Highways Operational Highway Boundary (RedLine) maps out the land belonging to the highway for the whole Strategic Road Network (SRN). It comprises two layers; one being the an outline and another showing the registration status / category of land of land that makes up the boundary. Due to the process involved in creating junctions with local highway authority (LHA) roads, land in this dataset may represent LHA highway (owned by National Highways but the responsibility of the LHA to maintain). Surplus land or land held for future projects does not form part of this dataset.The highway boundary is derived from:Ordnance Survey Mastermap Topography,HM Land Registry National Polygon Service (National Highway titles only), andplots researched and digitised during the course of the RedLine Boundary Project.The boundary is split into categories describing the decisions made for particular plots of land. These categories are as follows:Auto-RedLine category is for plots created from an automated process using Ordnance Survey MasterMap Topography as a base. Land is not registered under National Highways' name. For example, but not limited to, unregistered ‘ancient’ highway vested in Highways England, or bridge carrying highways over a rail line.NH Title within RedLine category is for plots created from Land Registry Cadastral parcels whose proprietor is National Highways or a predecessor. Land in this category is within the highway boundary (audited) or meets a certain threshold by the algorithm.NH Title outside RedLine category is for plots created in the same way as above but these areas are thought to be outside the highway boundary. Where the Confidence is Low, land in this category is yet to be audited. Where the Confidence is High, land in this category has been reviewed and audited as outside our operational boundary.National Highways (Technician) Data category is for plots created by National Highways, digitised land parcels relating to highway land that is not registered, not yet registered or un-registerable.Road in Tunnel category, created using tunnel outlines from Ordnance Survey MasterMap Topography data. These represent tunnels on Highways England’s network. Land is not registered under National Highways' name, but land above the tunnel may be in National Highways’ title. Please refer to the definitive land ownership records held at HM Land Registry.The process attribute details how the decision was made for the particular plot of land. These are as follows:Automated category denotes data produced by an automated process. These areas are yet to be audited by the company.Audited category denotes data that has been audited by the company.Technician Data (Awaiting Audit) category denotes data that was created by National Highways but is yet to be audited and confirmed as final.The confidence attribute details how confident you can be in the decision. This attribute is derived from both the decisions made during the building of the underlying automated dataset as well as whether the section has been researched and/or audited by National Highways staff. These are as follows:High category denotes land that has a high probability of being within the RedLine boundary. These areas typically are audited or are features that are close to or on the highway.Moderate category denotes land that is likely to be within the highway boundary but is subject to change once the area has been audited.Low category denotes land that is less likely to be within the highway boundary. These plots typically represent Highways England registered land that the automated process has marked as outside the highway boundary.Please note that this dataset is indicative only. For queries about this dataset please contact the GIS and Research Team.

Overview

This dataset comprises cloud masks for 513 1022-by-1022 pixel subscenes, at 20m resolution, sampled random from the 2018 Level-1C Sentinel-2 archive. The design of this dataset follows from some observations about cloud masking: (i) performance over an entire product is highly correlated, thus subscenes provide more value per-pixel than full scenes, (ii) current cloud masking datasets often focus on specific regions, or hand-select the products used, which introduces a bias into the dataset that is not representative of the real-world data, (iii) cloud mask performance appears to be highly correlated to surface type and cloud structure, so testing should include analysis of failure modes in relation to these variables.

The data was annotated semi-automatically, using the IRIS toolkit, which allows users to dynamically train a Random Forest (implemented using LightGBM), speeding up annotations by iteratively improving it's predictions, but preserving the annotator's ability to make final manual changes when needed. This hybrid approach allowed us to process many more masks than would have been possible manually, which we felt was vital in creating a large enough dataset to approximate the statistics of the whole Sentinel-2 archive.

In addition to the pixel-wise, 3 class (CLEAR, CLOUD, CLOUD_SHADOW) segmentation masks, we also provide users with binary
classification "tags" for each subscene that can be used in testing to determine performance in specific circumstances. These include:

• SURFACE TYPE: 11 categories
• CLOUD TYPE: 7 categories
• CLOUD HEIGHT: low, high
• CLOUD THICKNESS: thin, thick
• CLOUD EXTENT: isolated, extended

Wherever practical, cloud shadows were also annotated, however this was sometimes not possible due to high-relief terrain, or large ambiguities. In total, 424 were marked with shadows (if present), and 89 have shadows that were not annotatable due to very ambiguous shadow boundaries, or terrain that cast significant shadows. If users wish to train an algorithm specifically for cloud shadow masks, we advise them to remove those 89 images for which shadow was not possible, however, bear in mind that this will systematically reduce the difficulty of the shadow class compared to real-world use, as these contain the most difficult shadow examples.

In addition to the 20m sampled subscenes and masks, we also provide users with shapefiles that define the boundary of the mask on the original Sentinel-2 scene. If users wish to retrieve the L1C bands at their original resolutions, they can use these to do so.

Please see the README for further details on the dataset structure and more.

Contributions & Acknowledgements

The data were collected, annotated, checked, formatted and published by Alistair Francis and John Mrziglod.

Support and advice was provided by Prof. Jan-Peter Muller and Dr. Panagiotis Sidiropoulos, for which we are grateful.

We would like to extend our thanks to Dr. Pierre-Philippe Mathieu and the rest of the team at ESA PhiLab, who provided the environment in which this project was conceived, and continued to give technical support throughout.

Finally, we thank the ESA Network of Resources for sponsoring this project by providing ICT resources.

Description for i03_DAU_county_cnty2018 is as follows:Detailed Analysis Unit-(DAU) Convergence via County Boundary cnty18_1 for Cal-Fire, (See metadata for CAL-FIRE cnty18_1), State of California.The existing DAU boundaries were aligned with cnty18_1 feature class.Originally a collaboration by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive feature class to create i03_DAU_COUNTY_cnty24k09 alignment which has been updated to create i03_DAU_COUNTY_cnty18_1.This version was derived from a preexisting “dau_v2_105, 27, i03_DAU_COUNTY_cnty24k09” Detailed Analysis Unit feature class's and aligned with Cal-Fire's 2018 boundary.Manmade structures such as piers and breakers, small islands and coastal rocks have been removed from this version. Inlets waters are listed on the coast only.These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Fire cnty18_1.DAU BackgroundThe first investigation of California's water resources began in 1873 when President Ulysses S. Grant commissioned an investigation by Colonel B. S. Alexander of the U.S. Army Corps of Engineers. The state followed with its own study in 1878 when the State Engineer's office was created and filled by William Hammond Hall. The concept of a statewide water development project was first raised in 1919 by Lt. Robert B. Marshall of the U.S. Geological Survey.In 1931, State Engineer Edward Hyatt introduced a report identifying the facilities required and the economic means to accomplish a north-to-south water transfer. Called the "State Water Plan", the report took nine years to prepare. To implement the plan, the Legislature passed the Central Valley Act of 1933, which authorized the project. Due to lack of funds, the federal government took over the CVP as a public works project to provide jobs and its construction began in 1935.In 1945, the California Legislature authorized an investigation of statewide water resources and in 1947, the California Legislature requested that an investigation be conducted of the water resources as well as present and future water needs for all hydrologic regions in the State. Accordingly, DWR and its predecessor agencies began to collect the urban and agricultural land use and water use data that serve as the basis for the computations of current and projected water uses.The work, conducted by the Division of Water Resources (DWR’s predecessor) under the Department of Public Works, led to the publication of three important bulletins: Bulletin 1 (1951), "Water Resources of California," a collection of data on precipitation, unimpaired stream flows, flood flows and frequency, and water quality statewide; Bulletin 2 (1955), "Water Utilization and Requirements of California," estimates of water uses and forecasts of "ultimate" water needs; and Bulletin 3 (1957), "The California Water Plan," plans for full practical development of California’s water resources, both by local projects and a major State project to meet the State's ultimate needs. (See brief addendum below* “The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region”)DWR subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), corresponding to the State’s major drainage basins. The next levels of delineation are the Planning Areas (PA), which in turn are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so are the smallest study areas used by DWR.The DAU/counties are used for estimating water demand by agricultural crops and other surfaces for water resources planning. Under current guidelines, each DAU/County has multiple crop and land-use categories. Many planning studies begin at the DAU or PA level, and the results are aggregated into hydrologic regions for presentation.Since 1950 DWR has conducted over 250 land use surveys of all or parts of California's 58 counties. Early land use surveys were recorded on paper maps of USGS 7.5' quadrangles. In 1986, DWR began to develop georeferenced digital maps of land use survey data, which are available for download. Long term goals for this program is to survey land use more frequently and efficiently using satellite imagery, high elevation digital imagery, local sources of data, and remote sensing in conjunction with field surveys.There are currently 58 counties and 278 DAUs in California.Due to some DAUs being split by county lines, the total number of DAU’s identifiable via DAU by County is 782.**ADDENDUM**The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region[Detailed Analysis Units made up of a grouping of the Depletion Study Drainage Areas (DSA) boundaries occurred on the Eastern Foothills and Mountains within the Sacramento Region. Other DSA’s were divided into two or more DAU’s; for example, DSA 58 (Redding Basin) was divided into 3 DAU’s; 143,141, and 145. Mountain areas on both the east and west side of the Sacramento River below Shasta Dam went from ridge top to ridge top, or topographic highs. If available, boundaries were set adjacent to stream gages located at the low point of rivers and major creek drainages.Later, as the DAU’s were developed, some of the smaller watershed DSA boundaries in the foothill and mountain areas were grouped. The Pit River DSA was split so water use in the larger valleys (Alturas area, Big Valley, Fall River Valley, Hat Creek) could be analyzed. A change in the boundary of the Sacramento Region mountain area occurred at this time when Goose Lake near the Oregon State Line was included as part of the Sacramento Region.The Sacramento Valley Floor hydrologic boundary was at the edge of the alluvial soils and slightly modified to follow the water bearing sediments to a depth of 200 feet or more. Stream gages were located on incoming streams and used as an exception to the alluvial soil boundary. Another exception to the alluvial boundary was the inclusion of the foothills between Red Bluff and the Redding Basin. Modifications of the valley floor exterior boundary were made to facilitate analysis; some areas at the northern end of the valley followed section lines or other established boundaries.Valley floor boundaries, as originally shown in Bulletin 2, Water Utilization and Requirements of California, 1955 were based on physical topographic features such as ridges even if they only rise a few feet between basins and/or drainage areas. A few boundaries were based on drainage canals. The Joint DWR-USBR Depletion Study Drainage Areas (DSA) used drainage areas where topographic highs drained into one drainage basin. Some areas were difficult to study, particularly in areas transected by major rivers. Depletion Study Drainage Areas containing large rivers were separated into two DAU’s; one on each side of the river. This made it easier to analyze water source, water supply, and water use and drainage outflow from the DAU.Many of the DAUs that consist of natural drainage basins have stream gages located at outfall gates, which provided an accurate estimate of water leaving the unit. Detailed Analysis Units based on political boundaries or other criteria are much more difficult to analyze than those units that follow natural drainage basins.]**END ADDENDUM**.............................................................................................................................................cnty18_1 metadata Summary:(*See metadata for CAL-FIRE cnty18_1). CAL-FIRE cnty18_1 boundary feature class is used for cartographic purposes, for generating statistical data, and for clipping data. Ideally, state and federal agencies should be using the same framework data for common themes such as county boundaries. This layer provides an initial offering as "best available" at 1:24,000 scale.cnty18_1 metadata Description:(*See metadata for CAL-FIRE cnty18_1).cnty18_1 metadata Credits:CAL-FIRE cnty18_1 metadata comment:This specific dataset represents the full detailed county dataset with all coding (islands, inlets, constructed features, etc.). The user has the freedom to use this coding to create definition queries, symbolize, or dissolve to create a more generalized dataset as needed.In November 2015, the dataset was adjusted to include a change in the Yuba-Placer county boundary from 2010 that was not yet included in the 14_1 version of the dataset (ord. No. 5546-B). This change constitutes the difference between the 15_1 and 14_1 versions of this dataset.In March 2018, the dataset was adjusted to include a legal boundary change between Santa Clara and Santa Cruz Counties (December 11, 1998) as stated in Resolution No. 98-11 (Santa Clara) and Resolution No. 432-98 (Santa Cruz). This change constitutes the difference between the 18_1 and 15_1 versions of this dataset.(*See metadata for CAL-FIRE cnty18_1). - U.S. Bureau of Reclamation, California Department of Conservation, California Department of Fish and Game, California Department of Forestry and Fire protection

Statewide feature class of 2020 General Voting Tabulation Districts (VTDs) on 2020 Census geography. VTDs are voting precincts represented using 2020 Census TIGER geography. Some voting precinct boundaries do not use census geography, so the boundary of a VTD may differ from the actual corresponding voting precinct's boundary. The VTDs closely match the 9,014 precincts in effect for the 2020 General Election. On the occasion that a precinct is in two non-contiguous pieces, the VTD is suffixed. For example, if precinct 0001 is two non-contiguous areas, the corresponding VTDs would be VTD 0001A and VTD 0001B. If a voting precinct cannot be represented using census geography, it is consolidated into an adjacent precinct's corresponding VTD. There are 9,157 VTDs in the 2020 General Election VTDs feature class.Note: The 2020 General Election VTDs feature class is not the same as the Voting Districts in the Census Bureau's TIGER/Line Shapefile and do not correspond with the population data reported for the Voting Districts in the Census Bureau's 2020 Census State Redistricting Data (Public Law 94-171) Summary File.