This deep learning model is used to transform incorrect and non-standard addresses into standardized addresses. Address standardization is a process of formatting and correcting addresses in accordance with global standards. It includes all the required address elements (i.e., street number, apartment number, street name, city, state, and postal) and is used by the standard postal service.

      An address can be termed as non-standard because of incomplete details (missing street name or zip code), invalid information (incorrect address), incorrect information (typos, misspellings, formatting of abbreviations), or inaccurate information (wrong house number or street name). These errors make it difficult to locate a destination. Although a standardized address does not guarantee the address validity, it simply converts an address into the correct format. This deep learning model is trained on address dataset provided by openaddresses.io and can be used to standardize addresses from 10 different countries.



  Using the model


      Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check 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.Input
    Text (non-standard address) on which address standardization will be performed.

    Output
    Text (standard address)

    Supported countries
    This model supports addresses from the following countries:

      AT – Austria
      AU – Australia
      CA – Canada
      CH – Switzerland
      DK – Denmark
      ES – Spain
      FR – France
      LU – Luxemburg
      SI – Slovenia
      US – United States

    Model architecture
    This model uses the T5-base architecture implemented in Hugging Face Transformers.
    Accuracy metrics
    This model has an accuracy of 90.18 percent.

    Training dataThe model has been trained on openly licensed data from openaddresses.io.Sample results
    Here are a few results from the model.

This dataset represents the address point locations assigned by the Mat-Su Borough GIS/Addressing staff. Most of the parcels within the Mat-Su Borough that are road accessible have been assigned a physical address except where the access point is unknown, as with undeveloped corner lots. The address points in this dataset do not necessarily represent precise building locations as the data was originally based on the underlying parcel centroids. Data has historically been constructed and maintained using ArcView and ArcEditor applications. The current address assignment process involves using an ArcMap extension called MapSAG, which creates point features as directed by the GIS Addressing staff. Address information is populated at this time. As underlying parcel data accuracy has been spatially improved through field verification and mapping grade GPS equipment, address points have been shifted accordingly to fall within the appropriate parcels.

Group of web services that allow users to find and standardize addresses. Also provides a street name inventory, civic numbers, and postal address information. Folders/Services included are: Address PointsCommunitiesMunicipalitiesPostal CodesStreet Ranges

A map used in the Master Address Locator app to validate an address used to deliver public safety and other government services.

AddressNC has been prioritized by the North Carolina Geographic Information Coordinating Council (GICC) as a critical framework dataset. The AddressNC Program runs parallel to and is derived from the North Carolina 911 Board Next Generation 911 (NG911) Program.  Address data has been identified as mission critical for validation and accurate call routing within NG911 and AddressNC completes a full-circle approach of address maintenance and sustainability through applied enhancements and quality control beyond 911 requirements.  A primary goal of AddressNC is to continually develop and maintain quality address points on a continuous cycle through updates published in NG911. Various agencies in federal, state, and local government can benefit by applying practical applications of quality addressing in their own programs, negating the need to rely on outdated statewide addressing data and/or using paid address data sets from third party sources.

The new Oregon Address Geocoder is used to find the location coordinates for street addresses in the State of Oregon. This service is:FreePublicUpdated regularlyOutputs location coordinates in Oregon Lambert, feet (SRID 2992)Uses over 2 million address points and 288,000 streets for referenceIt is an ArcGIS multirole locator with two roles:Point Address - Generally more accurate results from rooftop location points. Includes a Subaddress if a unit number is located.Street Address - Less accurate results from an estimated distance along a street centerline address range if a Point Address was not found.Instructions for using the Geocoder via ArcGIS Pro, ArcGIS Online, and REST Services are below:ArcGIS ProWeb ServicesArcGIS Online

The zoning layer lookup aids the online zoning map to show zoning information by address and consolidates other important information such as Ward, ANC, SMD, Historic Districts, etc. in one reference dataset. The PREMISEADD field lists the address that is identified as the lot’s primary address for tax purposes. ADDRESS_OTHER contains a list of all other addresses on the lot, if any. Addresses may exist in a many-to-one relationship with property lots. ZONING is the primary field for zoning designation. ZONING_LABEL contains the zoning as well as any applicable IZ+ designation.

Land cover describes the surface of the earth. Land-cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to the earth's surface is required. Land-cover classification is a complex exercise and is difficult to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.There are a few public datasets for land cover, but the spatial and temporal coverage of these public datasets may not always meet the user’s requirements. It is also difficult to create datasets for a specific time, as it requires expertise and time. Use this deep learning model to automate the manual process and reduce the required time and effort significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check 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 very high-resolution (10 cm) imagery.OutputClassified raster with the 8 classes as in the LA county landcover dataset.Applicable geographiesThe model is expected to work well in the United States and will produce the best results in the urban areas of California.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 84.8%. The table below summarizes the precision, recall and F1-score of the model on the validation dataset: ClassPrecisionRecallF1 ScoreTree Canopy0.8043890.8461520.824742Grass/Shrubs0.7199930.6272780.670445Bare Soil0.89270.9099580.901246Water0.9808850.9874990.984181Buildings0.9222020.9450320.933478Roads/Railroads0.8696370.8629210.866266Other Paved0.8114650.8119610.811713Tall Shrubs0.7076740.6382740.671185Training dataThis model has been trained on very high-resolution Landcover dataset (produced by LA County).LimitationsSince the model is trained on imagery of urban areas of LA County it will work best in urban areas of California or similar geography.Model is trained on limited classes and may lead to misclassification for other types of LULC classes.Sample resultsHere are a few results from the model.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

Use Constraints:This mapping tool is for reference and guidance purposes only and is not a binding legal document to be used for legal determinations. The data provided may contain errors, inconsistencies, or may not in all cases appropriately represent the current boundaries of PWSs in California. The data in this map are subject to change at any time and should not be used as the sole source for decision making. By using this data, the user acknowledges all limitations of the data and agrees to accept all errors stemming from its use.Description:This mapping tool provides a representation of the general PWS boundaries for water service, wholesaler and jurisdictional areas. The boundaries were created originally by collection via crowd sourcing by CDPH through the Boundary Layer Tool, this tool was retired as of June 30, 2020. State Water Resources Control Board – Division of Drinking Water is currently in the process of verifying the accuracy of these boundaries and working on a tool for maintaining the current boundaries and collecting boundaries for PWS that were not in the original dataset. Currently, the boundaries are in most cases have not been verified. Map Layers· Drinking Water System Areas – representation of the general water system boundaries maintained by the State Water Board. This layer contains polygons with associated data on the water system and boundary the shape represents.· LPA office locations – represents the locations of the Local Primacy Agency overseeing the water system in that county. Address and contact information are attributes of this dataset.· LPA office locations – represents the locations of the Local Primacy Agency overseeing the water system in that county. Address and contact information are attributes of this dataset· California Senate Districts – represents the boundaries of the senate districts in California included as a reference layer in order to perform analysis with the Drinking Water System Boundaries layers.· California Senate Districts – represents the boundaries of the assembly districts in California included as a reference layer in order to perform analysis with the Drinking Water System Boundaries layers.· California County – represents the boundaries of the counties in California included as a reference layer in order to perform analysis with the Drinking Water System Boundaries layers.Informational Pop-up Box for Boundary layer· Water System No. – unique identifier for each water system· Water System Name – name of water system· Regulating Agency – agency overseeing the water system· System Type – classification of water system.· Population the approximate population served by the water system· Boundary Type – the type of water system boundary being displayed· Address Line 1 – the street or mailing address on file for the water system· Address Line 2 – additional line for street or mailing address on file for the water system, if applicable· City – city where water system located or receives mail· County – county where water system is located· Verification Status – the verification status of the water system boundary· Verified by – if the boundary is verified, the person responsible for the verification Date Created and Sources:This web app was most recently updated on July, 21, 2021. Each layer has a data created date and data source is indicated in the overview/metadata page and is valid up to the date provided.

This layer is utilized in Next Generation 911 for both geospatial call routing and location validation functions. Creation and maintenance of data is performed by local Public Safety Answering Points in partnership with counties, GIS vendors, and other public safety agencies and with support from the Nebraska Public Service Commission. Disparate datasets are aggregated at the state level and provisioned to the NG911 core services by the 911 department of the PSC. The component datasets have been standardized sufficiently to serve the purposes of NG911 core services, but differences in methodology may persist depending on the use cases for local jurisdictions; for example, some counties may develop points to represent the points of actual structures, while others may develop points representing access points to properties. Similarly, some jurisdictions may have sub-address points for locations with multiple structures or units sharing an address, while others may only have a single point to represent such locations.

This geocoding service provides the ability to perform tabular geocoding, reverse geocoding, and identifying results for locations that contain sub-addresses. This service and the supporting data are provided by the AddressNC program.A geocoding locator file is also available for users of ArcGIS Pro or ArcGIS Desktop in an offline/disconnected environment.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network _location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address _location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear _location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection _location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of _location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event _location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.

© 2018 County of Santa Clara, all rights reserved

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Ventura map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Ventura map area data layers. Data layers are symbolized as shown on the associated map sheets.

Updated Continually

About the dataLand use land cover (LULC) maps are an increasingly important tool for decision-makers in many industry sectors and developing nations around the world. The information provided by these maps helps inform policy and land management decisions by better understanding and quantifying the impacts of earth processes and human activity.ArcGIS Living Atlas of the World provides a detailed, accurate, and timely LULC map of the world. The data is the result of a three-way collaboration among Esri, Impact Observatory, and Microsoft. For more information about the data, see Sentinel-2 10m Land Use/Land Cover Time Series.About the appOne of the foremost capabilities of this app is the dynamic change analysis. The app provides dynamic visual and statistical change by comparing annual slices of the Sentinel-2 10m Land Use/Land Cover data as you explore the map.Overview of capabilities:Visual change analysis with either 'Step Mode' or 'Swipe Mode'Dynamic statistical change analysis by year, map extent, and classFilter by selected land cover classRegional class statistics summarized by administrative boundariesImagery mode for visual investigation and validation of land coverSelect imagery renderings (e.g. SWIR to visualize forest burn scars)Data download for offline use

REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/OA11_RGN17_EW_LU_1a961565e8f243f6acdc4b2eae2fcea0/FeatureServer