REST API endpoint that includes the city's mapping services.

The ArcGIS Javascript API lets developers build GIS web applications. The Javascript API is one of many that could be used but it's a great starting place. Students may also be interested in the Python API or others!

This Feature Service provides a database of worldwide airport locations and aviation information. It includes information about codenames, elevation, frequency, runways and wikipedia links.Additional Information All the data are crowdsourced by the community of OurAirports.com. SourceOurAirports.com/data/ | Last accessed on: 23.05.2025Processing procedureBy using the CSV table 'Airports' dated Feb 09, 2024, we have joined the tables 'Runways' and 'Airport-Frequencies' for additional information using the key:"ident". The points were projected and released as an ArcGIS Online feature service.Data2024

All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.

Lands Department of Hong Kong SAR has released Location Search API which is available in Hong Kong Geodata Store (https://geodata.gov.hk/gs/). This API is very useful to Esri Users in Hong Kong as it saves vast amount of time to carry out data conversion to support location searching. The API is HTTP-based for application developers to find any locations in Hong Kong by addresses, building names, place names or facility names.

This code sample contains sample HTML and JavaScript files. Users can follow This Guidelines to use the Location Search API with ArcGIS API for JavaScript to build web mapping applications with ArcGIS API for JavaScript.

Bu Notebook'da, Marmara Gölü'ndeki su ve bitki örtüsünü belirlemek için ArcGIS API for Python'ı kullanacaksınız. Yakın zamandaki bir uydu görüntüsünü seçecek ve üzerine NDVI ve NDWI spektral indislerini uygulayacaksınız. Ardından bu görüntülere eşik değer ve maskeleme işlemi uygulayarak bitki ve su piksellerini ayırt edilebilir ve görüntülenebilir bir hale getireceksiniz. Son olarak ise bir rastgele ölçeklendirmeyi bir raster görüntüsüne uygulayarak belirli bir renkle simgelemeyi ve spektral indis rasterları için histogramlar oluşturmayı öğreneceksiniz.

Service provided by HED articulates, as guidance, zones indicating to planners on when HED should be consulted in respect of proposals in the vicinity of heritage assets. It does not articulate the setting of assets but provides a baseline to ensure that HED are consulted in respect of applications within these areas. Data produced from HED data and updated monthly. Data contains attribution values providing unique id for each record, layer each record is derived from and buffer value (NULL where buffer=0).

The dataset contains locations and attributes of P56A and P56B flight restrictions in the District of Columbia. A dataset provided by the National Geospatial-Intelligence Agency (NGA) identified P56A and P56B flight restrictions in DC. The data specifically comes from NGA's Digital Aeronautical Flight Information File (DAFIF).

Webservices for HED data: • NI Sites and Monuments Record • Listed Buildings • Industrial Heritage Record • Built Heritage at Risk • Defense Heritage Record • Ship and Aircraft Wrecks • Areas of Archaeological Potential • Areas of Significant Archaeological Interest • Battlesites • Historic Parks, Gardens and Demesnes • Scheduled Zones

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.

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

Mature Support Notice: This item is in mature support as of February 2024. A new version of this item is available for your use. This web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis. Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.Quick access to the following band combinations and indices is provided: Agriculture : Highlights agriculture in bright green; Bands 6, 5, 2Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3 SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4Geology : Highlights geologic features; Bands 7, 6, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Panchromatic : Panchromatic images at 15m; Band 8Vegetation Index : Normalized Difference Vegetation Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinations The Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Stories tool will direct you to pre-selected interesting locations. The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript. The following Imagery Layers are being accessed : Multispectral Landsat - Provides access to 30m 8-band multispectral imagery and a range of functions that provide different band combinations and indices.Pansharpened Landsat - Provides access to 15m 4-band (Red, Green, Blue and NIR) panchromatic-sharpened imagery.Panchromatic Landsat - Provides access to 15m panchromatic imagery. These imagery layers can be accessed through the public group Landsat Community on ArcGIS Online.

Airport defines area on land or water intended to be used either wholly or in part for the arrival; departure and surface movement of aircraft/helicopters. This airport data is provided as a vector geospatial-enabled file format and depicted on Enroute charts.Airport information is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.Current Effective Date: 0901Z 02 Oct 2025 to 0901Z 27 Nov 2025

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy. Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca Available Products: ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/ Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/ Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022 Central Ontario Orthophotography Project (COOP) 2021 South-Western Ontario Orthophotography Project (SWOOP) 2020 Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 South Central Ontario Orthophotography Project (SCOOP) 2018 North-Western Ontario Orthophotography Project (NWOOP) 2017 Central Ontario Orthophotography Project (COOP) 2016 South-Western Ontario Orthophotography Project (SWOOP) 2015 Algonquin Orthophotography Project (2015) Additional Documentation: Ontario Web Raster Services User Guide (Word) Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every year Contact:Geospatial Ontario (GEO), geospatial@ontario.ca

This is a polygon feature class that models the flight coverage of available DWR historic aerial photography. The coordinate system utilized for this feature class is NAD1983_CaTM. The purpose of this dataset is to provide a spatial index of aerial photography acquisitions according to each acquisition's Project ID.

Author: Megan Banaski (mbanaski@esri.com) and Max Ozenberger (mozenberger@esri.com)Last Updated: 1/1/2024Intended Environment: WebPurpose:Exercise F6: Create a JS API 4.x WebMap App or Create a JS API 4.x WebScene App This lab is part of GitHub repository that contains short labs that step you through the process of developing a web application with ArcGIS API for JavaScript.The labs start from ground-zero and work through the accessing different aspects of the API and how to begin to build an application and add functionality.Requirements: Here are the resources you will use for the labs.ArcGIS for Developers - Account, Documentation, Samples, Apps, DownloadsEsri Open Source Projects - More source codeA simple guide for setting up a local web server (optional)Help with HTML, CSS, and JavaScript

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

This is a point feature class that models the centroid locations of individual images of available DWR historic aerial photography. The coordinate system utilized for this feature class is NAD1983_CaTM. The purpose for this dataset is to provide a spatial index of aerial photography acquisitions according to each acquisition's Project ID.

Mature Support Notice: This item is in mature support as of February 2025. A new version of this item is available for your use. This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen monthsQuick access to the following band combinations and indices is provided: BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020 Agriculture : Highlights vigorous vegetation in bright green, stressed vegetation dull green and bare areas brown; Bands 11, 8, 2Natural Color : Bands 4, 3, 2Color Infrared : Healthy vegetation is bright red while stressed vegetation is dull red; Bands 8, 4 ,3 SWIR (Short-wave Infrared) : Highlights rock formations; Bands 12, 11, 4Geology : Highlights geologic features; Bands 12, 11, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Vegetation Index : Normalized Difference Vegetation Index(NDVI) with Colormap ; (Band 8 - Band 4)/(Band 8 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 8 - Band 11)/(Band 8 + Band 11)Normalized Burn Ratio : (Band 8 - Band 12)/(Band 8 + Band 12)Built-Up Index : (Band 11 - Band 8)/(Band 11 + Band 8)NDVI Raw : Normalized Difference Vegetation Index(NDVI); (Band 8 - Band 4)/(Band 8 + Band 4)NDVI - VRE only Raw : NDVI with VRE bands only; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - VRE only Colorized : NDVI with VRE bands only with Colormap; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - with VRE Raw : Also known as NDRE. NDVI with VRE band 5 and NIR band 8; (Band 8 - Band 5)/(Band 8 + Band 5)NDVI - with VRE Colorized : Also known as NDRE with Colormap; (Band 8 - Band 5)/(Band 8 + Band 5)NDWI Raw : Normalized Difference Water index with Green band and NIR band; (Band 3 - Band 8)/(Band 3 + Band 8)NDWI - with VRE Raw : Normalized Difference Water index with VRE band 5 and Green band 3; (Band 3 - Band 5)/(Band 3 + Band 5)NDWI - with VRE Colorized : NDWI index with VRE band 5 and Green band 3 with Colormap; (Band 3 - Band 5)/(Band 3 + Band 5)Custom SAVI : (Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 8 - Band 4)/(Band 8 + Band 4 + 0.5))Custom Water Index : Offset + Scale*(Band 3 - Band 12)/(Band 3 + Band 12) Custom Burn Index : Offset + Scale*(Band 8 - Band 13)/(Band 8 + Band 13)Urban Index : Offset + Scale*(Band 8 - Band 12)/(Band 8 + Band 12)Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinations The Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for indices like NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Bookmark tool will direct you to pre-selected interesting locations.NOTE: Using the Time tool to access imagery in the Sentinel-2 archive requires an ArcGIS account. The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript. The following Imagery Layer are being accessed : Sentinel-2 - Provides access to 10, 20, and 60m 13-band multispectral imagery and a range of functions that provide different band combinations and indices.

This is a line feature class that models the flight lines of available DWR historic aerial photography. The coordinate system utilized for this feature class is NAD1983_CaTM. The purpose for this dataset is to provide a spatial index of aerial photography acquisitions according to each acquisition's Project ID.