The agsview extension for CKAN provides view plugins designed to display Esri ArcGIS Server data directly within CKAN resources. Specifically, it enables visualization of ArcGIS Map services and Feature layer services, leveraging an Esri Leaflet Viewer for interactive display. As such, this extension enhances CKAN by providing native support for displaying commonly used geospatial data formats, increasing the usability of CKAN for geospatial data catalogs through built-in rendering capabilities when used by your organization's CKAN end users. Key Features: ArcGIS Feature Layer Viewer (agsfsview): Allows visualization of ArcGIS Feature Layers found within either MapServices or FeatureServices, offering a means by which you or your end users can expose specific layers, enabling selective display of datasets. Configuration option: ags_url: Specifies the ArcGIS Server layer endpoint, including the layer ID for targeted data access ensuring the correct layer or service is connected to your CKAN resource. Configuration option: basemapurl: Allows customization of the basemap by specifying either an Esri basemap name or a generic tile URL template, to tailor the visual context of the displayed ArcGIS data. ArcGIS MapService Viewer (agsmsview): Provides functionality to render ArcGIS MapServices, giving control over which layers within the service are displayed. Configuration option: ags_url: Defines the ArcGIS Server MapService endpoint, directing the viewer to the desired MapService resource for inclusion in your CKAN resource. Configuration option: list_ids: Enables filtering of layers within the MapService by providing a comma-delimited list of layer IDs for selective display. An empty list will display all layers, offering you flexibility in configuring the data viewed in CKAN. Configuration option: basemapurl: Permits customization of the basemap, accepting either an Esri basemap name or a generic tile URL template, ensuring flexibility in the map presentation. Configurable Default Basemap: Using your CKAN .ini configuration, you can set a default basemap for all ArcGIS views, providing consistency and improving usability. You can specify either an Esri basemap name or a tile URL template as the default. Technical Integration: The agsview extension integrates with CKAN by adding view plugins (agsfsview and agsmsview). To enable the extension, you must add the plugin names to the ckan.plugins setting in the CKAN configuration file (e.g., production.ini). After updating you CKAN file, and restarting the CKAN instance, the ArcGIS viewers become available options when creating a CKAN resource in the 'View' section assuming the resource has URLs that are supported by the viewing feature. Benefits & Impact: By implementing the agsview extension, CKAN instances can natively display ArcGIS Server MapServices and Feature Layers, eliminating the need for external viewers or custom development. This significantly enhances CKAN's utility for organizations managing and sharing geospatial data, as users can readily visualize Esri ArcGIS data directly within the CKAN interface. The configuration options further allow for customization of the display, improving the user experience.

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

This FREE extension enables discovering and using GIS resources available in a GEOSS Clearinghouse directly from ArcGIS Desktop and ArcGIS Explorer. The CS-W Client for ArcGIS can search many implementations of CS-W implementing CS-W 2.0.0, 2.0.1, 2.0.2 and a number of Application Profiles (OGCCORE, APISO, EBRIM). Providers can extend the CS-W Client by creating a profile of their CS-W service and including that in the CS-W client configuration. View the title, abstract, or footprints of search results or view and download the full metadata. Add referenced live map services (ArcGIS Server, ArcIMS, WMS) to an ArcMap document or ArcGIS Explorer globe. ArcGIS Desktop 9.3 is required to install the ArcMap component of the CS-W Clients for ArcGIS. The CS-W Clients for ArcGIS component for ArcGIS Explorer requires ArcGIS Explorer 380 or higher.

This web map shows List of Registered Fire Service Installation Contractors Willing to Provide Selling Services of Approved Portable Equipment in Hong Kong. It is a set of data made available by the Fire Services 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 then 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 CSDI Portal at https://portal.csdi.gov.hk.

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.

A. SUMMARY This dataset lists intersections that have at least one continental crosswalk and meet the following criteria: Installed after January 1st, 2015 Installed before January 1st, 2015 and on the high injury network Continental crosswalks are marked with bold, wide stripes to indicate safe places for pedestrians to cross the road. Their high-visibility design helps alert drivers and cyclists to watch for people crossing. B. HOW THE DATASET IS CREATED Locations of continental crosswalks collected at the intersection level. Pre-2015 data was collected in the summer of 2019 as a one-time effort to locate every intersection with continental crosswalks on the city's High Injury Network. Crosswalks painted post-2015 are collected as part of Vision Zero data reporting. "Shops reports" are used as the data source. Shops reports include data citywide. Crosswalks marked "UNDETERMINED" in the "CONTINENTAL" field may or may not have continental crosswalks and require additional scrutiny. These two data sources were joined with an intersection nodes layer to create the feature class. The dataset is made available by SFMTA via their ArcGIS server/ feature server. C. UPDATE PROCESS The dataset is updated by MTA quarterly and published to the Open Data Portal automatically. D. HOW TO USE THIS DATASET This dataset includes: (1) all continental crosswalks citywide that were installed after 1/1/2015, and (2) all continental crosswalks that were installed before 12/31/2014 on the High Injury Network. It does not include continental crosswalks off the High Injury Network that were painted before 2015.

Feature service used to publish information regarding locations of water quality monitoring sites. Intended for use with ArcGIS for State Government's Water Quality Status.Water Quality Status is a configuration of an ArcGIS Online web application template. To configure the app, create a feature service that contains the location of the water quality monitoring sites and point the application template to the service. The application can either be run as a hosted application that runs within ArcGIS Online, or the application can be downloaded and hosted on your local web server. This specific configuration of the application template enables the user to determine water quality of waterways, lakes and beaches. Try It NowDownloadSupportIf you need support, please contact Esri Support Services. If you'd like to get help from other state government community members, post your question on the GeoNet. Additional help can be found on our Solution Site.If you have general questions or comments about this map or application, feel free to post them in the comments sections on this page.Release Datev2.0 - September 2016v1.0 - April 2014

This is a fine-tuned model for New Zealand, derived from a pre-trained model from Esri. It has been trained using LINZ aerial imagery (0.075 m spatial resolution) for Wellington You can see its output in this app https://niwa.maps.arcgis.com/home/item.html?id=1ca4ee42a7f44f02a2adcf198bc4b539Solar power is environment 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 Proor ArcGIS Enterprise – ArcGIS Image Server with Raster Analytics configuredor ArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the Esri guide to using their USA Solar Panel detection model (https://www.arcgis.com/home/item.html?id=c2508d72f2614104bfcfd5ccf1429284). 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.InputHigh resolution (5-15 cm) RGB imageryOutputFeature class containing detected solar panelsApplicable geographiesThe model is expected to work well in New ZealandModel architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.9244444449742635NOTE: Use at your own risk_Item Page Created: 2022-02-09 02:24 Item Page Last Modified: 2025-04-05 16:30Owner: NIWA_OpenData

This web map shows the Address of Registered Fire Service Installation Contractors in Hong Kong. It is a set of the data made available by the Fire Services 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 then 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.

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.

Location and status of small cell permits in Arlington, TX.On August 22, 2017, the Arlington City Council approved Resolution No. 17-223 amending the Wireless Services Design Manual to be consistent with SB 1004 (Chapter 284 of the Texas Local Government Code) which authorizes a Network Provider to install its Wireless Facilities within the public Right-of-Way.

The Wireless Services Design Manual for the City of Arlington, Texas, provides installation and construction details for placement of Wireless Facilities in the public Right-of-Way by Network Providers.

Previously, on May 9, 2017, the Arlington City Council approved Resolution No. 17-104 adopting the Wireless Services Design Manual prior to the final enactment of SB 1004.

A permit must be obtained. For more information on permits, please visit the Arlington, TX Small Cells Webpage.

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 earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics, giving superior results.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 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.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputRaster, mosaic dataset, or image service. (Preferred cell size is 10 meters.)Note: This model is trained to work on Sentinel-2 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified raster with the same classes as in Corine Land Cover (CLC) 2018.Applicable geographiesThis model is expected to work well in Europe and the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 82.41% with Level-1C imagery and 84.0% with Level-2A imagery, for CLC class level 2 classification (15 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassLevel-2A ImageryLevel-1C ImageryPrecisionRecallF1 ScorePrecisionRecallF1 ScoreUrban fabric0.810.830.820.820.840.83Industrial, commercial and transport units0.740.650.690.730.660.7Mine, dump and construction sites0.630.520.570.690.550.61Artificial, non-agricultural vegetated areas0.700.460.550.670.470.55Arable land0.860.900.880.860.890.87Permanent crops0.760.730.740.750.710.73Pastures0.750.710.730.740.710.73Heterogeneous agricultural areas0.610.560.580.620.510.56Forests0.880.930.900.880.920.9Scrub and/or herbaceous vegetation associations0.740.690.720.730.670.7Open spaces with little or no vegetation0.870.840.850.850.820.84Inland wetlands0.810.780.800.820.770.79Maritime wetlands0.740.760.750.870.890.88Inland waters0.940.920.930.940.910.92Marine waters0.980.990.980.970.980.98This model has an overall accuracy of 90.79% with Level-2A imagery for CLC class level 1 classification (5 classes). The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassPrecisionRecallF1 ScoreArtificial surfaces0.850.810.83Agricultural areas0.900.910.91Forest and semi natural areas0.910.920.92Wetlands0.770.700.73Water bodies0.960.970.96Sample ResultsHere are a few results from the model. To view more, see this story.

Swimming pools are important for property tax assessment because they impact the value of the property. Tax assessors at local government agencies often rely on expensive and infrequent surveys, leading to assessment inaccuracies. Finding pools that are not on the assessment roll (such as those recently constructed) is valuable to assessors and will ultimately mean additional revenue for the community.This deep learning model helps automate the task of finding pools from high resolution satellite imagery. This model can also benefit swimming pool maintenance companies and help redirect their marketing efforts. Public health and mosquito control agencies can also use this model to detect pools and drive field activity and mitigation efforts.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 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.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input8-bit, 3-band high resolution (5-7.5 centimeters) imageryOutputFeature class containing bounding boxes depicting pool locations with class BuiltinPool | PopupPoolApplicable geographiesThe model is expected to work well in the New Zealand.Model architectureThe model uses the MMDetection model architecture implemented using ArcGIS Pro Arcpy.Accuracy metricsThe model has an average precision score of 0.95.1 BuiltInPool2PopupPoolSample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object. If the confidence are below certain threshold e.g 5%)To learn how to use this model, see this story

Open the Data Resource: https://gis.chesapeakebay.net/server/rest/services/ChesapeakeProgress/cpFish_Passage_Streams_Opened/MapServer/1 This Chesapeake Bay Program indicator of progress toward the Fish Passage Outcome shows the fish passages that were installed after the 2011 baseline in order to open rivers and streams to targeted migratory and resident fish species (e.g., brook trout, eels, river herring and shad). Data were obtained from mulitple state and federal sources and edited by The Nature Conservancy for use in the Northeast Aquatic Connectivity project and in Chesapeake fish passage prioritization. These data were "snapped" to the U.S. Geological Survey's high-resolution (1:24,000) National Hydrography Dataset, so that dam points are geometrically coincident with stream lines. The data underwent several quality control checks to help ensure dams are in the proper location.

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images after natural and manmade disasters. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model is capable of detecting humans by looking at drone imagery and can draw bounding boxes around their exact location. 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 detection, reducing time and effort required significantly.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 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.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humansApplicable geographiesThe model is expected to work well in coastal areas of Africa but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 72.8 percent for humans and 67.1 for possibly a human class.Limitations • This model has a tendency to maximize detection of humans and errs towards producing false positives. • It has been noticed that a few features get missed when a cluster of features is reported.Sample results