This is a demonstration layer implementing INSPIRE Protected Sites (PS) - Nature Conservation (Surface) data according to the INSPIRE default data model. It is provided as a courtesy and should not be used for any purpose other than demonstration.DEMONSTRATION NOTE: This dataset uses the default INSPIRE data model (rather than flattened/streamlined Alternative Encoding). It is published from ArcGIS Pro to ArcGIS Enterprise with OGC map services enabled (WMS and WFS), then INSPIRE-specific custom capabilities are manually added to the INSPIRE View Service (WMS). This web service is then registered in ArcGIS Online and shared via the ArcGIS Hub catalog.About Protected SitesA protected site is an area designated or managed within a framework of international, Community and Member States' legislation to achieve specific conservation objectives. According to IUCN and adopted for the INSPIRE context a protected site is: An area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means.

Protected sites may be located in terrestrial, aquatic and/or marine environments, and may be under either public or private ownership. Learn more

This deep learning model is used to detect trees in low-resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.

This deep learning model is based on MaskRCNN and has been trained on data from the DM Dataset preprocessed and collected by the IST Team.

There is no need of high-resolution imagery you can perform all your analysis on low resolution imagery by detecting the trees with the accuracy of 75% and finetune the model to increase your performance and train on your own data.

Licensing requirements ArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Online

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.

Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.

Input 3-band low-resolution (70 cm) satellite imagery.

Output Feature class containing detected trees

Applicable geographies The model is expected to work well in the U.A.E.

Model architecture This model is based upon the MaskRCNN python package and uses the Resnet-152 model architecture implemented in pytorch.

Training data This model has been trained on the Satellite Imagery created and Labelled by the team and validated on the different locations with more diverse locations.

Accuracy metrics This model has an average precision score of 0.45.

Sample results Here are a few results from the model.

Discover the booming drone surveying software market! This in-depth analysis reveals key trends, growth drivers, and regional insights for 2025-2033, including leading companies and market segmentation. Learn how AI, cloud technology, and increasing demand are shaping this dynamic sector.

This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. The files were prepared at one-inch-equals-100-feet scale in urban areas of the County and one-inch-equals-200-feet scale in rural areas of the County. The original digital map files were in GenaMap format and then the GenaMap files were converted to ESRI shape file format. The ESRI shape files were converted to ESRI personal geodatabase and further converted to ESRI enterprise geodatabase files. The data was converted to ESRI Parcel fabric and the Local Government Information Model (LGIM). It is currently maintained in the LGIM Parcel fabric. The data is updated nightly with current ownership and geometry changes.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This Quarter Section feature class depicts PLSS Second Divisions . PLSS townships are subdivided in a spatial hierarchy of first, second, and third division. These divisions are typically aliquot parts ranging in size from 640 acres to 160 to 40 acres, and subsequently all the way down to 2.5 acres. The data in this feature class was translated from the PLSSSecondDiv feature class in the original production data model, which defined the second division for a specific parcel of land. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets. Why was this layer created? To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.Develop a national context inventory of riparian areas and their condition within national forests and rangelands.How was this layer created? Using freely available data.Develop cost effective modeling approach & technique.Multi-scale (national, regional, & local).Promote technology transfer to train/reach out to our partners.Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMapData Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The United States Section of the International Boundary and Water Commission (IBWC) has 15 miles of flood control levees in the Presidio area. A project was initiated in calendar year 2002 between the IBWC and ERDC-WES to perform a condition assessment of their levees using airborne geophysics and detailed geological mapping of the flood plain. The project includes electromagnetic-induction (EM) data, Light Detection and Ranging data (LIDAR), historical and current aerial photography, geologic and geomorphologic interpretations from historical photography, digital videography, and soils data. These data are part of an enterprise Geographical Information System (eGIS). The eGIS organizes and manipulates all pertinent data for the condition assessment of levees in addition to containing modeling and data visualization tools. Levee segments that were not captured in the IBWC and ERDC-WES project were digitized by GIT using engineering maps provided by IBWC and spatially adjusted with 2008 orthoimagery from 3001, Inc. or ESRI ArcGIS Online World Imagery. Some segments from the IBWC and ERDC-WES project were also field verified by GIT. The following layer iidentifies the levee centerline as digitized from 1996 aerial photography.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Note: This Wildfire Hazard Potential (WHP) image service has been deprecated. Previous versions—including 2014, 2018, 2020, and 2023 continuous and classified datasets—have been replaced by a unified WHP service containing the most current data.For the updated continuous WHP service, visit: https://usfs.maps.arcgis.com/home/item.html?id=c984aeeecfcc4bef887a3f72a5b4e65a For the updated classified WHP service, visit: https://usfs.maps.arcgis.com/home/item.html?id=13004659506b4032bf7998038176f1c3Wildfire hazard potential (WHP) is an index that depicts the relative potential for wildfire that would be difficult for suppression resources to contain, based on wildfire simulation modeling. This dataset produced by the USDA Forest Service, Fire Modeling Institute in 2020 shows WHP at a spatial resolution of 270 meters across the entire conterminous United States, classified into five WHP classes of very low, low, moderate, high, and very high. Areas mapped with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions, based primarily on 2014 landscape conditions. This WHP dataset is based on outputs of wildfire simulation modeling published in 2020. Starting with the 2020 version, the WHP dataset is integrated with the Wildfire Risk to Communities project. The 2020 dataset is the first version to include Alaska and Hawaii. There is a spatially-refined, 30-m resolution version of the WHP as part of the downloadable Wildfire Risk to Communities data, and related datasets that depict other components of wildfire hazard and risk to homes. This 2020 version supersedes all previous versions of Wildfire Hazard Potential (2018, 2014) or Wildland Fire Potential (2012, 2010, 2007). We generally do not advise direct comparisons between versions because changes can reflect improvements in methodology at all stages of the WHP calculation in addition to actual land cover changes. For more information and to download the raster data, please visit the Wildfire Hazard Potential website. Map author: Greg Dillon, USDA Forest Service, Rocky Mountain Research Station, Fire Modeling InstituteThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

***The data is updated nightly with current ownership and geometry changes.This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. The files were prepared at one-inch-equals-100-feet scale in urban areas of the County and one-inch-equals-200-feet scale in rural areas of the County. The original digital map files were in GenaMap format and then the GenaMap files were converted to ESRI shape file format. The ESRI shape files were converted to ESRI personal geodatabase and further converted to ESRI enterprise geodatabase files. The data was converted to ESRI Parcel fabric and the Local Government Information Model (LGIM). It is currently maintained in the LGIM Parcel fabric. The data is updated nightly with current ownership and geometry changes.

First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates for all 50 states and the District of Columbia. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.

Version 3 updates the constituent data sets with 2012-2016 American Community Survey data and makes several methodological tweaks, most notably moving to modeling at the Census tract level rather at the block group. As with Version 2, the inputs to the simultaneous equation model (SEM) include six endogenous variables—housing costs, car ownership, and transit usage for both owners and renters—and 18 exogenous variables, with vehicle miles traveled still modeled separately due to data limitations.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2012-2016 Data Dictionary: DD_Location Affordability Indev v.3.0LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

This feature class represents the mid-century (2030-2059) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002

NIWA's bathymetry model of New Zealand at a 250m resolution. The 2016 model is a compilation of data digitised from published coastal charts, digital soundings archive, navy collector sheets and digital multibeam data sourced from surveys by NIWA, LINZ, as well as international surveys by vessels from United States of America, France, Germany, Australia, and Japan. All data used is held at NIWA.Image service can be used for analysis in ArcGIS Desktop or ArcGIS Online - no need to download the data, just stream using this service and classify, symbolise, mask, extract or apply map algebra - just like you would with local raster files. https://enterprise.arcgis.com/en/server/latest/publish-services/windows/key-concepts-for-image-services.htmMap information and metadata Offshore representation was generated from digital bathymetry at a grid resolution of 250m. Sun illumination is from an azimuth of 315° and 45° above the horizon.Projection Mercator 41 (WGS84 datum). EPSG: 3994Scale 1:5,000,000 at 41°S. Not to be used for navigational purposes Bibliographic reference Mitchell, J.S., Mackay, K.A., Neil, H.L., Mackay, E.J., Pallentin, A., Notman P., 2012. Undersea New Zealand, 1:5,000,000. NIWA Chart, Miscellaneous Series No. 92Further Information: https://www.niwa.co.nz/our-science/oceans/bathymetry/further-informationLicence: https://www.niwa.co.nz/environmental-information/licences/niwa-open-data-licence-by-nn-nc-sa-version-1_Item Page Created: 2017-11-01 00:55 Item Page Last Modified: 2025-04-05 18:48Owner: NIWA_OpenData

Original source - The WUP_Stations geodatabase is a compilation of permitted water use withdrawal stations throughout parts of Florida and Georgia developed by the SJRWMD. This is part of the Water Use Permit (WUP) project led by Tammy Bader and former staff. The originating layer and associated tables are updated nightly from a script.Note: Permit Type FW used to indicate artesian wells with the potential to flow at land surface. These wells may have had or may currently have flow control devices. Most of these wells were identified through a groundwater modeling data consolidation effort performed in 2006.Truncated Shape File Field Name:Enterprise DB Field Name:DescriptionOBJECTIDOBJECTIDInternal feature number. Sequential unique whole numbers that are automatically generated.DISTPRMTSTDISTPRMTSTNConcatenate ID of District + Permit + StationDISTRICTDISTRICTPermit issuing agency.PRMT_IDPRMT_IDPermit ID.SEQ_NOSEQ_NOPermit sequence number or revision number where the station was last referenced. In the case of SFMWD, this is the Permit Application ID.SEQ_STTSSEQ_STTSStatus of this station’s sequence or revision.STN_IDSTN_IDSystem Generated Station ID (station header table)UTILITYUTILITYCommon name of public water supply utility.PROJECT_NAPROJECT_NAMEProject Name.STN_STTSSTN_STTSCurrent status of the station.SOURCESOURCESource of water withdrawn at the station.STN_TPSTN_TPStation type.CATEGORYCATEGORYCode indicating predominant permit level allocated water use.PERMITTYPEPERMITTYPECode indicating general permit level operations of the project: AG = Agriculture, CII = Commercial/Industrial/Institutional, LRA = Landscape/Recreational/Aesthetic, MD = Mining/Dewatering, PG = Power Generation, PS = Public Supply, DRN = Drainage, FW = Flowing Well, INJ = Injection, ASR = Aquifer Storage/Recovery, DOM = Domestic, ENV = Environmental, ESS = Essential, FP = Fire Protection, OTH = Other, UNK = Unknown. Note: Permit Type FW used to indicate artesian wells with the potential to flow at land surface. These wells may have had or may currently have flow control devices. Most of these wells were identified through a groundwater modeling data consolidation effort performed in 2006.SHAPESHAPECoordinates defining the features.WTRTYPEWTRTYPEWater use type.NFSEG_LAYENFSEG_LAYERNFSEG modeling.COUNTYCOUNTYThe county the project is located in.UPDATEDUPDATEDDate of latest record revision.JAN_1995JAN_1995Example Month, Year field with water use value for station, in MGD units.

Original source - The WUP_Stations geodatabase is a compilation of permitted water use withdrawal stations throughout parts of Florida and Georgia developed by the SJRWMD. This is part of the Water Use Permit (WUP) project led by Tammy Bader and former staff. The originating layer and associated tables are updated nightly from a script.Note: Permit Type FW used to indicate artesian wells with the potential to flow at land surface. These wells may have had or may currently have flow control devices. Most of these wells were identified through a groundwater modeling data consolidation effort performed in 2006.Truncated Shape File Field Name:Enterprise DB Field Name:DescriptionOBJECTIDOBJECTIDInternal feature number. Sequential unique whole numbers that are automatically generated.DISTPRMTSTDISTPRMTSTNConcatenate ID of District + Permit + StationDISTRICTDISTRICTPermit issuing agency.PRMT_IDPRMT_IDPermit ID.SEQ_NOSEQ_NOPermit sequence number or revision number where the station was last referenced. In the case of SFMWD, this is the Permit Application ID.SEQ_STTSSEQ_STTSStatus of this station’s sequence or revision.STN_IDSTN_IDSystem Generated Station ID (station header table)UTILITYUTILITYCommon name of public water supply utility.PROJECT_NAPROJECT_NAMEProject Name.STN_STTSSTN_STTSCurrent status of the station.SOURCESOURCESource of water withdrawn at the station.STN_TPSTN_TPStation type.CATEGORYCATEGORYCode indicating predominant permit level allocated water use.PERMITTYPEPERMITTYPECode indicating general permit level operations of the project: AG = Agriculture, CII = Commercial/Industrial/Institutional, LRA = Landscape/Recreational/Aesthetic, MD = Mining/Dewatering, PG = Power Generation, PS = Public Supply, DRN = Drainage, FW = Flowing Well, INJ = Injection, ASR = Aquifer Storage/Recovery, DOM = Domestic, ENV = Environmental, ESS = Essential, FP = Fire Protection, OTH = Other, UNK = Unknown. Note: Permit Type FW used to indicate artesian wells with the potential to flow at land surface. These wells may have had or may currently have flow control devices. Most of these wells were identified through a groundwater modeling data consolidation effort performed in 2006.SHAPESHAPECoordinates defining the features.WTRTYPEWTRTYPEWater use type.NFSEG_LAYENFSEG_LAYERNFSEG modeling.COUNTYCOUNTYThe county the project is located in.UPDATEDUPDATEDDate of latest record revision.JAN_1995JAN_1995Example Month, Year field with water use value for station, in MGD units.