As democratization, availability, and acquisition of data is increasing, privacy is becoming a concern. Regulations such as GDPR have been introduced to address the privacy rights of the users. To help maintain privacy, this model can be used to prevent the identification of vehicles by automatically blurring the license plates. Additionally, the model can also be used to blur human faces. 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 cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band (RGB) street-level image.OutputImage with blurred license plates.Applicable geographiesThis model is expected to work well in all regions globally. However, results can vary for imagery that are statistically dissimilar to training data.Model architectureOpen-source Anonymizer model by understand.ai.LimitationsThis model is trained to blur license plates in images recorded with sensors that are typically used in autonomous vehicles. It does not work on low-quality or grayscale images or other extreme camera configurations such as fish-eye.Sample resultsHere are a few results from the model.

Please note, the updated version of this toolbox is now available for download on this page. The COVID-19-Modeling-v1.zip file contains version 5 of the toolbox with updated documentation. Version 5 of the toolbox updates the CHIME Model v1.1.5 tool. The COVID-19Surge (CDC) model is unchanged in this version.More information about the toolbox can be found in the toolbox document. More information about the CHIME Model v1.1.5 tool, including the change log, can be found in the tool documentation and this video.More information about the COVID-19Surge (CDC) tool is included in the tool documentation and this video. CHIME Model v1.1.5 ToolVersion 4 - Updated 11 MAY 2020An implementation of Penn Medicine’s COVID-19 Hospital Impact Model for Epidemics (CHIME) for use in ArcGIS Pro 2.3 or later. This tool leverages SIR (Susceptible, Infected, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation. Version 4 of this tool is based on CHIME v1.1.5 (2020-05-07). Learn more about how CHIME works.Version 4 contains the following updates:Updated the CHIME tool from CHIME v1.1.2 to CHIME v1.1.5.Added a new parameter called Date of Social Distancing Measures Effect to specify the date when social distancing measures started showing their effects.Added a new parameter called Recovery to specify the number of recovered cases at the start of the model.COVID-19Surge (CDC) ToolVersion 1 - Released 04 MAY 2020An implementation of Centers for Disease Control and Prevention’s (CDC) COVID-19Surge for use in ArcGIS Pro 2.3 or later. This tool leverages SIICR (Susceptible, Infected, Infectious, Convalescing, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation based on the extent to which mitigation strategies such as social distancing or shelter-in-place recommendations are implemented. This tool is based on COVID-19Surge. Learn more about how COVID-19Surge works.Potential ApplicationsThe illustration above depicts the outputs of the COVID-19Surge (CDC) tool of the COVID-19 Modeling toolbox.A hospital systems administrator needs a simple model to project the number of patients the hospitals in the network will need to accommodate in the next 90 days due to COVID-19. You know the population served by each hospital, the date and level of current social distancing, the number of people who have recovered, and the number of patients that are currently hospitalized with COVID-19 in each facility. Using your hospital point layer, you run the CHIME Model v1.1.5 tool.An aid agency wants to estimate where and when resources will be required in the counties you serve. You know the population and number of COVID-19 cases today and 14 days ago in each county. You run the COVID-19Surge (CDC) tool using your county polygon data, introducing an Intervention Policy and New Infections Per Case (R0) driven by fields to account for differences in anticipated social distancing policies and effectiveness between counties.A county wants to understand how the lessening or removal of interventions may impact hospital bed availability within the county. You run the CHIME Model v1.1.5 and COVID-19Surge (CDC) tool, checking Add Additional Web App Fields in Summary in both tools. You display the published results from each tool in the Capacity Analysis configurable app so estimates can be compared between models.This toolbox requires any license of ArcGIS Pro 2.3 or higher in order to run. Steps for upgrading ArcGIS Pro can be found here.For questions, comments and support, please visit our COVID-19 GeoNet community.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks 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.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks 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.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Geospacial Mining Pit Esri

## Overview

Geospacial Mining Pit Esri is a dataset for semantic segmentation tasks - it contains Mining Pit FFuS annotations for 218 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

This layer shows the location with building licences issued to indigenous/ eligible villager in the New Territories to build house on his own agricultural land held under the Block Government Lease in Hong Kong. It is a set of data made available by the Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

This product set contains high-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N) and Barrow Triangle (156.13 - 157.08 deg W, 71.14 - 71.42 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM).

Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary and ArcInfo grid format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); a quarter-quadrangle index map for the 26 IFSAR tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format).

Unmodified IFSAR data comprise 26 data tiles across UTM zones 4 and 5. The DSM and DTM tiles (5 m resolution) are provided in floating-point binary format with header and projection files. The ORRI tiles (1.25 m resolution) are available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook.

The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest.

Data are provided on five DVDs, available through licensing only to National Science Foundation (NSF)-funded investigators. An NSF award number must be provided when ordering data.

No new tavern licenses can be issued to any location that is within 400 feet of existing businesses already licensed for the sale of alcoholic liquor in certain zoning districts. Measurements are made from the property line and exclude streets, alleys, and public ways. This prohibition does not apply to restaurants, hotels offering restaurant services, or not-for-profit clubs. There are nine major classes of liquor licenses in the City of Chicago, the most common being Tavern, Package Goods and Incidental - Consumption on Premises. These categories were established to help the City identify and regulate the various types of establishments serving alcoholic beverages. They are described at the following link: http://bit.ly/P0Gn4c. The Chicago City Council has passed a series of ordinances restricting the issuance of liquor licenses in various locations throughout the City in sections 4-60-022 and 4-60-023 of the Municipal Code. In general, consumption on premises liquor licenses are affected by the 022 moratorium and Package Goods are affected by the 023 moratorium. However, moratoriums can be complicated, so determining whether or not a moratorium applies to a specific license application is not always straightforward. Users of the data should contact the Department of Business Affairs and Consumer Protection if they have any questions. These GIS layers show the locations of moratoriums passed by the City Council. It is updated monthly if any restrictions have been added or deleted. Columns are as follows: • CODE refers to the specific ordinance passed by City County. If there are more than one, they are connected by an underscore. They are in the format ww.nnn where ww is the ward number and nnn is a sequential number. • ST_SDE refers to which side of the street the ordinance refers to. • CREADATE is the date that the ordinance was input into the digital system, usually within a week after passed by City Council. Dates before 2003 do not relect the actual date passed. To view or use these shapefiles, compression software, such as 7-Zip, and special GIS software, such as ESRI ArcGIS or QGIS, are required. To download this file, right-click the "Download" link above and choose "Save link as."

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode

Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelBefore 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.Input1. 8-bit, 3-band high-resolution (50 cm) imagery. The model was trained on 50 cm Airbus imagery2. Building footprints feature classOutputFeature class containing classified building footprints. Classname field value 1 indicates damaged buildings, and value 2 corresponds to undamaged structuresApplicable geographiesThe model was specifically trained and tested over Maui, Hawaii, in response to the Maui fires in August 2023.Accuracy metricsThe model has an average accuracy of 0.96.Sample resultsResults of the model can be seen in this dashboard.

This is an open source object detection model by TensorFlow in TensorFlow Lite format. While it is not recommended to use this model in production surveys, it can be useful for demonstration purposes and to get started with smart assistants in ArcGIS Survey123. You are responsible for the use of this model. When using Survey123, it is your responsibility to review and manually correct outputs.This object detection model was trained using the Common Objects in Context (COCO) dataset. COCO is a large-scale object detection dataset that is available for use under the Creative Commons Attribution 4.0 License.The dataset contains 80 object categories and 1.5 million object instances that include people, animals, food items, vehicles, and household items. For a complete list of common objects this model can detect, see Classes.The model can be used in ArcGIS Survey123 to detect common objects in photos that are captured with the Survey123 field app. Using the modelFollow the guide to use the model. You can use this model to detect or redact common objects in images captured with the Survey123 field app. The model must be configured for a survey in Survey123 Connect.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputCamera feed (either low-resolution preview or high-resolution capture).OutputImage with common object detections written to its EXIF metadata or an image with detected objects redacted.Model architectureThis is an open source object detection model by TensorFlow in TensorFlow Lite format with MobileNet architecture. The model is available for use under the Apache License 2.0.Sample resultsHere are a few results from the model.

Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelBefore 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.Input1. 8-bit, 3-band high-resolution (10 cm) imagery. The model was trained on 10 cm Vexcel imagery2. Building footprints feature classOutputFeature class containing classified building footprints. Classname field value 1 indicates damaged buildings, and value 2 corresponds to undamaged structuresApplicable geographiesThe model was specifically trained and tested over Maui, Hawaii, in response to the Maui fires in August 2023.Accuracy metricsThe model has an average accuracy of 0.96.Sample resultsResults of the models can be seen in this dashboard.

Federal Energy Regulatory Commission RegionsImportant Note: This item is in mature support as of November, 2024 and will be retired in March, 2025. This feature layer, utilizing data from Homeland Infrastructure Foundation Level Data (HIFLD), displays Federal Energy Regulatory Commission (FERC) regions within the United States. Per FERC, it "is an independent agency that regulates the interstate transmission of electricity, natural gas, and oil. FERC also reviews proposals to build liquefied natural gas (LNG) terminals and interstate natural gas pipelines as well as licensing hydropower projects."New York FERC RegionData currency: This cached Esri service is checked monthly for updates from its federal source (FERC Regions)Data modification(s): NoneFor more information, please visit: OverviewFor feedback please contact: ArcGIScomNationalMaps@esri.comHomeland Infrastructure Foundation-Level Data (HIFLD) SubcommitteePer HIFLD, "The Homeland Infrastructure Foundation-Level Data (HIFLD) Subcommittee was established…to address improvements in collection, processing, sharing, and protection of homeland infrastructure geospatial information across multiple levels of government, and to develop a common foundation of homeland infrastructure data to be used for visualization and analysis on all classification domains."

These points mark the change from salt water (license not required) to freshwater. A New Jersey fishing license is required at—and upstream of—these locations.

Animal Services Provides for the care and control of animals in the Louisville Metro area, including pet licensing and pet adoption.Data Dictionary:animal id - Unique identifying number assigned to each specific animaltag number - Unique identifying number assigned to each individual tagtag type- The type of tag or permit purchasedLIC 3Y 3yr spayed/neutered licenseLIC 3YSR 3yr senior spayed/neutered licenseLIC ALTERED 1 year spayed/neutered licenseLIC ASST 1 year assistance animal licenseLIC DD1 year dangerous dog licenseLIC MULTIPLE 1 year discounted license when they license more than 3 animalsLIC PDD 1 year potentially dangerous dog licenseLIC SR 1 year senior altered licenseLIC UNALTERED 1 year non spayed or neutered licensePER ADV Animal Drawn Vehicle permitPER BOARDING Boarding permitPER CIRCUS Circus permitPER CIRCUS EL Elephant Ride permitPER CLASS A Class A kennel licensePER HS Animal Welfare Group permitPER PS NOSELL Pet Shop not selling dogs, cats or ferrets permitPER PS SELL Pet shop selling dogs, cats or ferrets permitPER STABLE Stable permitPER SWINE Swine permitRAB VAC CERT. Rabies vaccination tagRABIES CERT Rabies vaccination tagtag date - The date the tag was issued to the ownertag expire - The date that the tag expirestag status - The status of the tag or permitCURRENT Tag is currentDEAD Pet is deceasedDUPLICATE Owner lost the tag and it is replacedEXPIRED Tag is expiredGAVE AWAY Owner gave pet away to new owner and did not transfer tagLOST Owner no longer has the petMOVED Owner no longer lives in Jefferson CountyMOVED JEFF Owner no longer lives in Jefferson CountyNO REASON Did not give a reasonNOT MY PET Owner no longer has the petNOT OWNER Owner no longer has the petRENEWED New license has been issuedREPLACED Owner lost the tag and it is replacedRETUR MAIL Owners moved and the tag is undeliverableREVOKED Tag or permit taken away due to violationsTEMPORARY Issued to a person until requirements are metTURN IN Owner no longer has the petUNKNOWN Did not give a reasonvax date- The date the animal was given a rabies vaccinationvax expire- The date the rabies vaccination expirescity, state, zip code - The city, state and zip code associated with the owner of the animals addressanimal type- Type of animal associated with the tagsex- The sex of the animalM maleF femaleN neuteredS spayedU unknownpet dob- The date of birth of the animalbites- Does the animal have a bite reported to MASY yesN nocolor - The color of the animalbreed - The breed of animalvet name - The name of the vet or agency that administered the rabies vaccination.Contact:Adam HamiltonAdam.Hamilton@louisvilleky.gov

This dataset indicates whether a property owner has a license for the current calendar year. To look up the license status of a property manager go to: Business Licenses (Tacoma) | Tacoma Open Data (arcgis.com).A business license is required for the activity of renting or leasing real property (residential dwellings, land, or commercial buildings) in the City of Tacoma. Business licenses are issued to the legal property owner's name or entity that is on record with Pierce County.To search business license records: Select the Filter Data iconSelect the field you want to search from (Property Owner Name, Rental Property Address, etc.)Enter text into the search boxSelect from the drop-down to display the recordPlease Note: The City requests owners to update their information as it changes, however, most updates are provided to the City during the license renewal process at the beginning of the calendar year. Some property owners may not be required to obtain a city business license. If you do not find your landlord on this list please contact your landlord. About the Data: This dataset includes the property owner's name(s), license number, current license status, rental property address(s), mailing address, owner site address, and industry code. Use the industry code to identify residential or commercial rental properties. Visit Tacoma's Rental Business page for more information about licensing requirements. For business license questions, contact the Tax and License division at 253-591-5252.This dataset is updated daily and is maintained by the Tax & License division.

This New Zealand solar panel detection Deep Learning Package can detect solar panels from high resolution imagery. This model is trained on high resolution imagery from New Zealand.Solar power is environmentally friendly and is being promoted by government agencies and power distribution companies. Government agencies can use solar panel detection to offer incentives such as tax exemptions and credits to residents who have installed solar panels. Policymakers can use it to gauge adoption and frame schemes to spread awareness and promote solar power utilization in areas that lack its use. This information can also serve as an input to solar panel installation and utility companies and help redirect their marketing efforts.Traditional ways of obtaining information on solar panel installation, such as surveys and on-site visits, are time consuming and error-prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of solar panel detection, reducing time and effort required significantly.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing 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.When using the Detect Objects using Deep Learning geoprocessing tool, ticking the Non Maximum Suppression box is recommended, for reference a Max Overlap Ratio of 0.3 was used for the example images below. Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (7.5 cm) RGB imagery.OutputFeature class containing detected solar panels.Applicable geographiesThe model is expected to work well in New Zealand.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.83.Sample resultsSome results from the model are displayed below: To learn how to use this model, see this story