Mature Support Notice: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results. OpenStreetMap 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 and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

An ArcGIS Pro project may contain maps, scenes, layouts, data, tools, and other items. It may contain connections to folders, databases, and servers. Content can be added from online portals such as your ArcGIS organization or the ArcGIS Living Atlas of the World.In this tutorial, you'll create a new, blank ArcGIS Pro project. You'll add a map to the project and convert the map to a 3D scene.Estimated time: 10 minutesSoftware requirements: ArcGIS Pro

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap 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 and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

From ArcGIS Pro, you can author and share web scene layers that are stored as items in ArcGIS Online. You can share 3D point, multipatch, building, LAS datasets, and voxel data as a web scene layer from a 3D scene. If the layer you want to share is one of the above mentioned data, skip to step 2.

Discover how to display and symbolize both 2D and 3D data. Search, access, and create new map symbols. Learn to specify and configure text symbols for your map. Complete your map by creating an effective layout to display and distribute your work.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap 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 and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

Scene Layer Package used on Website. Has data from assessor table in it. This is used in our scene layers. It is the entire City's buildings based off the LiDAR.This is the official layer that was created using Local Government 3D modeling software from ArcGIS Pro.

Microsoft Buildings Footprints with Heights from service: https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/MS_Buildings_Training_Data_with_Heights/FeatureServer (restrictions, do not use)Source: Approx. 9.8 million building footprints for portions of metro areas in 44 US States in Shapefile format.Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and CityAlabamaGreater Phoenix City, Mobile, and MontgomeryArizonaTucsonArkansasLittle Rock with 5 buildings just across the river from MemphisCaliforniaBakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to CloverdaleColoradoInterior of DenverConnecticutEnfield and Windsor LocksDelawareDoverFloridaTampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and GainesvilleGeorgiaColumbus, Atlanta, and AugustaIllinoisEast St. Louis, downtown area, Springfield, Champaign and UrbanaIndianaIndianapolis downtown and Jeffersonville downtownIowaDes MoinesKansasTopekaKentuckyLouisville downtown, Covington and NewportLouisianaShreveport, Baton Rouge and center of New OrleansMaineAugusta and PortlandMarylandBaltimoreMassachusettsBoston, South Attleboro, commercial area in Seekonk, and SpringfieldMichiganDowntown DetroitMinnesotaDowntown MinneapolisMississippiBiloxi and GulfportMissouriDowntown St. Louis, Jefferson City and SpringfieldNebraskaLincolnNevadaCarson City, Reno and Los VegasNew HampshireConcordNew JerseyCamden and downtown Jersey CityNew MexicoAlbuquerque and Santa FeNew YorkSyracuse and ManhattanNorth CarolinaGreensboro, Durham, and RaleighNorth DakotaBismarckOhioDowntown Cleveland, downtown Cincinnati, and downtown ColumbusOklahomaDowntown Tulsa and downtown Oklahoma CityOregonPortlandPennsylvaniaDowntown Pittsburgh, Harrisburg, and PhiladelphiaRhode IslandThe greater Providence areaSouth CarolinaGreensville, downtown Augsta, greater Columbia area and greater Charleston areaSouth Dakotagreater Pierre areaTennesseeMemphis and NashvilleTexasLubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus ChristiUtahSalt Lake City downtownVirginiaRichmondWashingtonGreater Seattle area to Tacoma to the south and Marysville to the northWisconsinGreen Bay, downtown Milwaukee and MadisonWyomingCheyenne

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

Stamp Out COVID-19An apple a day keeps the doctor away.Linda Angulo LopezDecember 3, 2020https://theconversation.com/coronavirus-where-do-new-viruses-come-from-136105SNAP Participation Rates, was explored and analysed on ArcGIS Pro, the results of which can help decision makers set up further SNAP-D initiatives.In the USA foods are stored in every State and U.S. territory and may be used by state agencies or local disaster relief organizations to provide food to shelters or people who are in need.US Food Stamp Program has been ExtendedThe Supplemental Nutrition Assistance Program, SNAP, is a State Organized Food Stamp Program in the USA and was put in place to help individuals and families during this exceptional time. State agencies may request to operate a Disaster Supplemental Nutrition Assistance Program (D-SNAP) .D-SNAP Interactive DashboardAlmost all States have set up Food Relief Programs, in response to COVID-19.Scroll Down to Learn more about the SNAP Participation Analysis & ResultsSNAP Participation AnalysisInitial results of yearly participation rates to geography show statistically significant trends, to get acquainted with the results, explore the following 3D Time Cube Map:Visualize A Space Time Cube in 3Dhttps://arcg.is/1q8LLPnetCDF ResultsWORKFLOW: a space-time cube was generated as a netCDF structure with the ArcGIS Pro Space-Time Mining Tool : Create a Space Time Cube from Defined Locations, other tools were then used to incorporate the spatial and temporal aspects of the SNAP County Participation Rate Feature to reveal and render statistically significant trends about Nutrition Assistance in the USA.Hot Spot Analysis Explore the results in 2D or 3D.2D Hot Spotshttps://arcg.is/1Pu5WH02D Hot Spot ResultsWORKFLOW: Hot Spot Analysis, with the Hot Spot Analysis Tool shows that there are various trends across the USA for instance the Southeastern States have a mixture of consecutive, intensifying, and oscillating hot spots.3D Hot Spotshttps://arcg.is/1b41T43D Hot Spot ResultsThese trends over time are expanded in the above 3D Map, by inspecting the stacked columns you can see the trends over time which give result to the overall Hot Spot Results.Not all counties have significant trends, symbolized as Never Significant in the Space Time Cubes.Space-Time Pattern Mining AnalysisThe North-central areas of the USA, have mostly diminishing cold spots.2D Space-Time Mininghttps://arcg.is/1PKPj02D Space Time Mining ResultsWORKFLOW: Analysis, with the Emerging Hot Spot Analysis Tool shows that there are various trends across the USA for instance the South-Eastern States have a mixture of consecutive, intensifying, and oscillating hot spots.Results ShowThe USA has counties with persistent malnourished populations, they depend on Food Aide.3D Space-Time Mininghttps://arcg.is/01fTWf3D Space Time Mining ResultsIn addition to obvious planning for consistent Hot-Hot Spot Areas, areas oscillating Hot-Cold and/or Cold-Hot Spots can be identified for further analysis to mitigate the upward trend in food insecurity in the USA, since 2009 which has become even worse since the outbreak of the COVID-19 pandemic.After Notes:(i) The Johns Hopkins University has an Interactive Dashboard of the Evolution of the COVID-19 Pandemic.Coronavirus COVID-19 (2019-nCoV)(ii) Since March 2020 in a Response to COVID-19, SNAP has had to extend its benefits to help people in need. The Food Relief is coordinated within States and by local and voluntary organizations to provide nutrition assistance to those most affected by a disaster or emergency.Visit SNAPs Interactive DashboardFood Relief has been extended, reach out to your state SNAP office, if you are in need.(iii) Follow these Steps to build an ArcGIS Pro StoryMap:Step 1: [Get Data][Open An ArcGIS Pro Project][Run a Hot Spot Analysis][Review analysis parameters][Interpret the results][Run an Outlier Analysis][Interpret the results]Step 2: [Open the Space-Time Pattern Mining 2 Map][Create a space-time cube][Visualize a space-time cube in 2D][Visualize a space-time cube in 3D][Run a Local Outlier Analysis][Visualize a Local Outlier Analysis in 3DStep 3: [Communicate Analysis][Identify your Audience & Takeaways][Create an Outline][Find Images][Prepare Maps & Scenes][Create a New Story][Add Story Elements][Add Maps & Scenes] [Review the Story][Publish & Share]A submission for the Esri MOOCSpatial Data Science: The New Frontier in AnalyticsLinda Angulo LopezLauren Bennett . Shannon Kalisky . Flora Vale . Alberto Nieto . Atma Mani . Kevin Johnston . Orhun Aydin . Ankita Bakshi . Vinay Viswambharan . Jennifer Bell & Nick Giner

Will be updated as new data becomes availableProjectionNew Zealand Transverse Mercator 2000 (NZTM2000).Vertical DatumNew Zealand Vertical Datum 2016 (NZVD2016).The NZ Elevation layer is an elevation surface for use in 3D applications in the NZTM projection. By adding this layer to a Scene in ArcGIS Pro or in the Scene Viewer it will be define the base height in your application.See the metadata layer with information about the data here.NZTM Basemaps can be used on top of this service, providing it shares the same tiling scheme. When combining it with the NZ Basemaps provided by Eagle Technolgy, make sure to use the raster basemaps with the updated tiling scheme or one of the vector basemaps. All the compatible basemaps can be found in this group. When creating your own basemap or tiled layer make sure to use the tiling scheme provided here.The elevation service is made up of the available publicly-owned 1m and 2m dems. For areas where 1m/2m elevation data is not available the 8m dem provided by LINZ is being used. Outside of the coverage of the 8m dem, a 0m dem is used for visual purposes.This service is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers layers and maps that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or remarks about the content, please let us now at livingatlas@eagle.co.nz

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package of the Halbouty pump station has various file types: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This metadata set comprises aerial photos of the above locations, as well as 3D models, point clouds, and the animation video of Halbouty Pump Station. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

This data contains general information about Pedestrian Network in Hong Kong. Pedestrian Network is a set of 3D line features derived from road features and road furniture from Lands Department and Transport Department. A number of attributes are associated with the pedestrian network such as spatially related street names. Besides, the pedestrian network includes information like wheelchair accessibility and obstacles to facilitate the digital inclusion for the needy. Please refer to this video to learn how to use 3D Pedestrian Network Dataset in ArcGIS Pro to facilitate your transportation analysis.The data was provided in the formats of JSON, GML and GDB by Lands Department and downloaded via GEODATA.GOV.HK website.

The original data files were processed and converted into an Esri file geodatabase. Wheelchair accessibility, escalator/lift, staircase walking speed and street gradient were used to create and build a network dataset in order to demonstrate basic functions for pedestrian network and routing analysis in ArcMap and ArcGIS Pro. There are other tables and feature classes in the file geodatabase but they are not included in the network dataset, users have to consider the use of information based on their requirements and make necessary configurations. The coordinate system of this dataset is Hong Kong 1980 Grid.

The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.

For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Lands Department at https://geodata.gov.hk/gs/download-datadict/201eaaee-47d6-42d0-ac81-19a430f63952.

For details about the data, source format and terms of conditions of usage, please refer to the website of GEODATA STORE at https://geodata.gov.hk.Dataset last updated on: 2022 Oct

This 3D model of Mount Saint Helens shows the topography using wood-textured contours set at 50m vertical spacing, with the darker wood grain color indicating the major contours at 1000, 1500, 2000, and 2500 meters above sea level. The state of the mountain before the eruption of May 13, 1980 is shown with thinner contours, allowing you to see the volume of rock that was ejected via the lateral blast.The process to create the contours uses CityEngine and ArcGIS Pro for data processing, symbolization, and publishing. The steps:Create a rectangular AOI polygon and use the Clip Raster tool on your local terrain raster. A 30m DEM was used for before, 10m for after.Run the Contour tool on the clipped raster, using the polygon output option - 50m was used for this scene.Run the Smooth Polygon tool on the contours. For Mount St. Helens, I used the PAEK algorithm, with a 200m smoothing tolerance. Depending on the resolution of the elevation raster and the extent of the AOI, a larger or smaller value may be needed. Write a CityEngine rule (see below) that extrudes and textures each contour polygon to create a stair-stepped 3D contour map. Provide multiple wood texture options with parameters for: grain size, grain rotation, extrusion height (to account for different contour depths if values other than 100m are used), and a hook for the rule to read the ContourMax attribute that is created by the Contour tool. Export CityEngine rule as a Rule Package (*.rpk).Add some extra features for context - a wooden planter box to hide some of the edges of the model, and water bodies.Apply the CityEngine-authored RPK to the contour polygons in ArcGIS Pro as a procedural fill symbol, adjust parameters for desired look & feel.Run Layer 3D to Feature Class tool to convert the procedural fill to multipatch features. Share Web SceneRather than create a more complicated CityEngine rule that applied textures for light/dark wood colors for minor/major contours, I just created a complete light- and dark-wood version of the mountain (and one with just the water), then shuffled them together.Depending on where this methodology is applied, you may want to clip out other areas - for example, glaciers, roads, or rivers. Or add annotation by inlaying a small north arrow in the corner of the map. I like the challenge of representing any feature in this scene in terms of wood colors and grains - some extruded, some recessed, some inlaid flat.

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

Alabama Greater Phoenix City, Mobile, and Montgomery

Arizona Tucson

Arkansas Little Rock with 5 buildings just across the river from Memphis

California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

Colorado Interior of Denver

Connecticut Enfield and Windsor Locks

Delaware Dover

Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

Georgia Columbus, Atlanta, and Augusta

Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

Indiana Indianapolis downtown and Jeffersonville downtown

Iowa Des Moines

Kansas Topeka

Kentucky Louisville downtown, Covington and Newport

Louisiana Shreveport, Baton Rouge and center of New Orleans

Maine Augusta and Portland

Maryland Baltimore

Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

Michigan Downtown Detroit

Minnesota Downtown Minneapolis

Mississippi Biloxi and Gulfport

Missouri Downtown St. Louis, Jefferson City and Springfield

Nebraska Lincoln

Nevada Carson City, Reno and Los Vegas

New Hampshire Concord

New Jersey Camden and downtown Jersey City

New Mexico Albuquerque and Santa Fe

New York Syracuse and Manhattan

North Carolina Greensboro, Durham, and Raleigh

North Dakota Bismarck

Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

Oklahoma Downtown Tulsa and downtown Oklahoma City

Oregon Portland

Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

Rhode Island The greater Providence area

South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

South Dakota greater Pierre area

Tennessee Memphis and Nashville

Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

Utah Salt Lake City downtown

Virginia Richmond

Washington Greater Seattle area to Tacoma to the south and Marysville to the north

Wisconsin Green Bay, downtown Milwaukee and Madison

Wyoming Cheyenne

This deep learning model is used for extracting windows and doors in textured building data displayed in 3D views. Manually digitizing windows/doors from 3D building data can be a slow process. This model automates the extraction of these objects from a 3D view and can help in speeding up 3D editing and analysis workflows. Using this model, existing building data can be enhanced with additional information on location, size and orientation of windows and doors. The extracted windows and doors can be further used to perform 3D visibility analysis using existing 3D geoprocessing tools in ArcGIS.This model can be useful in many industries and workflows. National Government and state-level law enforcement could use this model in security analysis scenarios. Local governments could use windows and door locations to help with tax assessments with CAMA (computer aided mass appraisal) plus impact-studies for urban planning. Public safety users might be interested in regards to physical or visual access to restricted areas, or the ability to build evacuation plans. The commercial sector, with everyone from real-estate agents to advertisers to office/interior designers, would be able to benefit from knowing where windows and doors are located. Even utilities, especially mobile phone providers, could take advantage of knowing window sizes and positions. To be clear, this model doesn't solve these problems, but it does allow users to extract and collate some of the data they will need to do it.Using the modelThis model is generic and is expected to work well with a variety of building styles and shapes. To use this model, you need to install supported deep learning frameworks packages first. See Install deep learning frameworks for ArcGIS for more information. The model can be used with the Interactive Object Detection tool.A blog on the ArcGIS Pro tool that leverages this model is published on Esri Blogs. We've also published steps on how to retrain this model further using your data.InputThe model is expected to work with any textured building data displayed in 3D views. Example data sources include textured multipatches, 3D object scene layers, and integrated mesh layers. OutputFeature class with polygons representing the detected windows and doors in the input imagery. Model architectureThe model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.Training dataThis model was trained using images from the Open Images Dataset.Sample resultsBelow, are sample results of the windows detected with this model in ArcGIS Pro using the Interactive Object Detection tool, which outputs the detected objects as a symbolized point feature class with size and orientation attributes.

Buildings are the foundation of any 3D city; they create a realistic visual context for understanding the built environment. This rule can help you quickly create 3D buildings using your existing 2D building footprint polygons. Create buildings for your whole city or specific areas of interest. Use the buildings for context surrounding higher-detail buildings or proposed future developments. Already have existing 3D buildings? Check out the Textured Buildings from Mass by Building Type rule.What you getA Rule Package file named Building_FromFootprint_Textured_ByBuildingType.rpk Rule works with a polygon layerGet startedIn ArcGIS Pro Use this rule to create Procedural Symbols, which are 3D symbols drawn on 2D features Create 3D objects (Multipatch layer) for sharing on the webShare on the web via a Scene LayerIn CityEngineCityEngine File Navigator HelpParametersBuilding Type: Eave_Height: Height from the ground to the eave, units controlled by the Units parameterFloor_Height: Height of each floor, units controlled by the Units parameterRoof_Form: Style of the building roof (Gable, Hip, Flat, Green)Roof_Height: Height from the eave to the top of the roof, units controlled by the Units parameterType: Use activity within the building, this helps in assigning appropriate building texturesDisplay:Color_Override: Setting this to True will allow you to define a specific color using the Override_Color parameter, and will disable photo-texturing.Override_Color: Allows you to specify a building color using the color palette. Note: you must change the Color_Override parameter from False to True for this parameter to take effect.Transparency: Sets the amount of transparency of the feature Units:Units: Controls the measurement units in the rule: Meters | FeetImportant Note: You can hook up the rule parameters to attributes in your data by clicking on the database icon to the right of each rule parameter. The database icon will change to blue when the rule parameter is mapped to an attribute field. The rule will automatically connect when field names match rule parameter names. Use layer files to preserve rule configurations unique to your data.For those who want to know moreThis rule is part of a the 3D Rule Library available in the Living Atlas. Discover more 3D rules to help you perform your work.Learn more about ArcGIS Pro in the Getting to Know ArcGIS Pro lesson

See the NZ Elevation Layer for more information on the NZ Elevation layerThe NZ Elevation - Metadata layer provides information about the data used for the NZ Elevation layer. You can identify what areas use 1m or 2m DEM's derived from LiDAR and what areas use the 8m DEM provided by LINZ. You can also find information, whenever available, about capture dates, point cloud density and links to the layer's in the LINZ Data Service.The NZ Elevation layer is an elevation surface for use in 3D applications in the NZTM projection. By adding this layer to a Scene in ArcGIS Pro or in the Scene Viewer it will be define the base height in your application.NZTM Basemaps can be used on top of this service, providing it shares the same tiling scheme. When combining it with the NZ Basemaps provided by Eagle Technolgy, make sure to use the raster basemaps with the updated tiling scheme or one of the vector basemaps. All the compatible basemaps can be found in this group. When creating your own basemap or tiled layer make sure to use the tiling scheme provided here.The elevation service is made up of the available publicly-owned 1m and 2m dems. For areas where 1m/2m elevation data is not available the 8m dem provided by LINZ is being used. Outside of the coverage of the 8m dem, a 0m dem is used for visual purposes.This service is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers layers and maps that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or remarks about the content, please let us now at livingatlas@eagle.co.nz