The global 3D mapping and modeling market is expected to grow significantly in the next few years as demand increases for detailed and accurate representations of physical environments in three-dimensional space. Estimated to be valued at USD 38.62 billion in the year 2025, the market was expected to grow at a CAGR of 14.5% from 2025 to 2033 and was estimated to reach an amount of USD 90.26 billion by the end of 2033. The high growth rate is because of improvement in advanced technologies with the development of high-resolution sensors and methods of photogrammetry that make possible higher-resolution realistic and immersive 3D models.Key trends in the market are the adoption of virtual and augmented reality (VR/AR) applications, 3D mapping with smart city infrastructure, and increased architecture, engineering, and construction utilization of 3D models. Other factors are driving the growing adoption of cloud-based 3D mapping and modeling solutions. The solutions promise scalability, cost-effectiveness, and easy access to 3D data, thus appealing to business and organizations of all sizes. Recent developments include: Jun 2023: Nomoko (Switzerland), a leading provider of real-world 3D data technology, announced that it has joined the Overture Maps Foundation, a non-profit organization committed to fostering collaboration and innovation in the geospatial domain. Nomoko will collaborate with Meta, Amazon Web Services (AWS), TomTom, and Microsoft, to create interoperable, accessible 3D datasets, leveraging its real-world 3D modeling capabilities., May 2023: The Sanborn Map Company (Sanborn), an authority in 3D models, announced the development of a powerful new tool, the Digital Twin Base Map. This innovative technology sets a new standard for urban analysis, implementation of Digital Cities, navigation, and planning with a fundamental transformation from a 2D map to a 3D environment. The Digital Twin Base Map is a high-resolution 3D map providing unprecedented detail and accuracy., Feb 2023: Bluesky Geospatial launched the MetroVista, a 3D aerial mapping program in the USA. The service employs a hybrid imaging-Lidar airborne sensor to capture highly detailed 3D data, including 360-degree views of buildings and street-level features, in urban areas to create digital twins, visualizations, and simulations., Feb 2023: Esri, a leading global provider of geographic information system (GIS), location intelligence, and mapping solutions, released new ArcGIS Reality Software to capture the world in 3D. ArcGIS Reality enables site, city, and country-wide 3D mapping for digital twins. These 3D models and high-resolution maps allow organizations to analyze and interact with a digital world, accurately showing their locations and situations., Jan 2023: Strava, a subscription-based fitness platform, announced the acquisition of FATMAP, a 3D mapping platform, to integrate into its app. The acquisition adds FATMAP's mountain-focused maps to Strava's platform, combining with the data already within Strava's products, including city and suburban areas for runners and other fitness enthusiasts., Jan 2023: The 3D mapping platform FATMAP is acquired by Strava. FATMAP applies the concept of 3D visualization specifically for people who like mountain sports like skiing and hiking., Jan 2022: GeoScience Limited (the UK) announced receiving funding from Deep Digital Cornwall (DDC) to develop a new digital heat flow map. The DDC project has received grant funding from the European Regional Development Fund. This study aims to model the heat flow in the region's shallower geothermal resources to promote its utilization in low-carbon heating. GeoScience Ltd wants to create a more robust 3D model of the Cornwall subsurface temperature through additional boreholes and more sophisticated modeling techniques., Aug 2022: In order to create and explore the system's possibilities, CGTrader worked with the online retailer of dietary supplements Hello100. The system has the ability to scale up the generation of more models, and it has enhanced and improved Hello100's appearance on Amazon Marketplace.. Key drivers for this market are: The demand for 3D maps and models is growing rapidly across various industries, including architecture, engineering, and construction (AEC), manufacturing, transportation, and healthcare. Advances in hardware, software, and data acquisition techniques are making it possible to create more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations.

. Potential restraints include: The acquisition and processing of 3D data can be expensive, especially for large-scale projects. There is a lack of standardization in the 3D mapping modeling industry, which can make it difficult to share and exchange data between different software and systems. There is a shortage of skilled professionals who are able to create and use 3D maps and models effectively.. Notable trends are: 3D mapping and modeling technologies are becoming essential for a wide range of applications, including urban planning, architecture, construction, environmental management, and gaming. Advancements in hardware, software, and data acquisition techniques are enabling the creation of more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations..

The aim of this exercise is to bring data from the previous exercises into ArcGIS Online's Scene Viewer to create a 3D model where we can visualise the data in 3D and understand how a flood depth of 1m in the flood alert areas might impact on buildings in these areas.

3D buildings. This dataset is a 3D building multipatch created using lidar point cloud bare earth points and building points to create a normalized data surface. Some areas have limited data. The lidar dataset redaction was conducted under the guidance of the United States Secret Service. All data returns were removed from the dataset within the United States Secret Service redaction boundary except for classified ground points and classified water points.The scene layer complies with the Indexed 3D Scene layer (I3S) format. The I3S format is an open 3D content delivery format used to disseminate 3D GIS data to mobile, web, and desktop clients.

A zip file containing two ArcGIS polygons of the FORGE site located in Fallon, Nevada. FallonFORGE3DGeologicModelRange is the 3D geologic model range and FallonFORGESite is the FORGE site location.

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.

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.

This is currently saved as a Scene Layer Package, which can be used by ArcGIS. To download the 3D Scene Layer, you have to first click the yellow "Open Content" button above, then this will take you to a separate screen where you can then click the blue "Download" button.If you are interested in a free trial of this software, you can visit the free trial section ESRI's Website HERE.

The Terrain 3D layer provides global elevation surface to use in ArcGIS 3D applicationsWhat can you do with this layer?Use this layer to visualize your maps and layers in 3D using applications like the Scene Viewer in ArcGIS Online and ArcGIS Pro. Show me how1) Working with Scenes in ArcGIS Pro or ArcGIS Online Scene Viewer2) Select an appropriate basemap or use your own3) Add your unique 2D and 3D data layers to the scene. Your data are simply added on the elevation. If your data have defined elevation (z coordinates) this information will be honored in the scene4) Share your work as a Web Scene with others in your organization or the publicDataset Coverage To see the coverage and sources of various datasets comprising this elevation layer, view the Elevation Coverage Map. Additionally, this layer uses data from Maxar’s Precision 3D Digital Terrain Models for parts of the globe.This layer is part of a larger collection of elevation layers. For more information, see the Elevation Layers group on ArcGIS Online.

Click here to open the ArcGIS Online 3D Map Viewer and work through the examples shown belowTo add 3D data to ArcGIS Online you will need a login for an ArcGIS Online account. We would recommend that you use a free schools subscription (full functionality) or the free public account (reduced functionality).Login to ArcGIS OnlineFind Mount Everest and save the 3D map so that it opens with an amazing view of the mountainShare your 3D map with a friend or colleague and get some feed back

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.

The aim of this exercise is to bring data from the previous exercises into ArcGIS Online's Scene Viewer to create a 3D model where we can visualise the data in 3D and understand how a flood depth of 1m in the flood alert areas might impact on buildings in these areas.

This layer shows the Level 1 3D building models of Hong Kong. The 3D models were derived from the building polygon of iB1000. It is a subset of Digital Topographic Map made available by Lands Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://www.hkmapservice.gov.hk/ (“HKMS 2.0”). The source data is in Esri File Geodatabase format and uploaded to Esri’s ArcGIS Online platform for sharing and referencing 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.

Use the 3D Viewer template to showcase your scene with default 3D navigation tools, including zoom controls, pan, rotate, and compass. Include a locator map and bookmarks to provide context to your scene and guide app viewers to points of interest. Line of sight, measure, and slice tools allow viewers to interpret 3D data. Set the option to disable scrolling in the app to seamlessly embed this app in another app or site. Examples: Present a detailed 3D view of a mountainous region at a large scale while the 2D inset map provides context of where you are in the world. Display a 3D plan for new urban development that app viewers can explore with slice and measurement tools. Allow users to visualize the impact of shadows on a scene using daylight animation. Data requirements The 3D Viewer template requires a web scene. Key app capabilities 3D navigation and Compass tool - Allow app users to pan or rotate the scene and orient their view to north. Locator map - Display an inset map with the app's map area in the context of a broader area. Line of sight - Visualize whether one or multiple targets are visible from an observer point. Measurement tools - Provide tools that measure distance and area and find and convert coordinates. Slice - Excludes specific layers to change the view of a scene. Bookmarks - Provide a collection of preset extents that are saved in the scene to which users can navigate the map. Disable scroll - Prevent the map from zooming when app users scroll Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

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 captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. 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 area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - 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 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.

Archive of ArcGIS data from the West Flank FORGE site located in Coso, California. Archive contains the following eight shapefiles: Polygon of the 3D geologic model (WestFlank3DGeologicModelExtent) Polylines of the traces 3D modeled faults (WestFlank3DModeledFaultTraces) Polylines of the fault traces from Duffield and Bacon, 1980 (WestFlankFaultsfromDuffieldandBacon) Polygon of the West Flank FORGE site (WestFlankFORGEsite) Polylines of the traces of the geologic cross-sections (cross-sections in a separate archive in the GDR) (WestFlankGeologicCrossSections) Polylines of the traces of the seismic reflection profiles through and adjacent to the West Flank site (seismic reflection profiles in a separate archive in the GDR) (WestFlankSiesmicReflectionProfiles) Points of the well collars in and around the West Flank site (WestFlankWellCollars) Polylines of the surface expression of the West Flank well paths (WestFlankWellPaths)

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

The final aim for this practical is to create a 3D model to visualise how a flood depth of 1m might impact buildings within flood alert areas in Shrewsbury, including a potential new building we are going to create a simple 3D model for. By the end of the exercises in this practical you should be able to use Arc Online Apps to create a 3D model that looks like this -The learning objectives for making this model are as follows:Be able to open and navigate in the Map ViewerBe able to find and add suitable data into Map ViewerBe able to create datasets that allow you to perform visual analysis to understand why areas may have been identified as flood risk areasBe able to build a query to identify and extract building data for buildings within the Flood Alert AreaBe able to create a model to represent a potential new buildingBe able to use Scene Viewer to put this all together in a 3D model that allows you visualise this data

A collection of multipatches features for all buildings within the Urban Development Boundary (UDB) and outside the UDB, approximately 938 square miles. Please contact the GIS Technical Support Team at gis@miamidade.gov for additional information.The source feature class can be downloaded from this link.Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

3D textured mesh (photomesh) representing all physical features (e.g. buildings, trees and terrain) across City of Melbourne. The 3D textured mesh is provided in object file format (.obj) and is accompanied by material (.mtl) and image texture (.jpg) files.

The data has been split into a series of tiles covering the entire municipality. An index file (Tile_Index.kml) is included to indicate the geo-spatial location of each tile. To position the mesh in its real world location, use the origin coordinates found in the metadata file (metadata.xml).

The 3D textured mesh is provided in different levels of detail, as indicated in the file name of the .obj filename. The levels of detail vary from L13 (lowest level of detail) to L20 (highest level of detail).

Capture Information
- Capture Date: May 2020
- Capture Pixel Size: 7.5cm ground sample distance
- Map Projection: MGA Zone 55 (MGA55)
- Vertical Datum: Australian Height Datum (AHD)
- Spatial Accuracy (XYZ): Supplied survey control used for control (Madigan Surveying)

Contents
The download is a zip file containing compressed:
- Object files (.obj)
- Material files (.mtl)
- Image textures (.jpg)
- Metadata (.xml)
- Tile index (.kml)

Preview Data:
For an interactive sample of the data please see the link below (WebGL browser required - Google Chrome recommended).
Photomesh 2020 - SLPK (arcgis.com)

Photomesh 2020 - OBJ (arcgis.com)

Usage:
Through the download an use of this data you agree to the licensing and disclaimer conditions.
While all due care has been taken to ensure the data of this website is accurate, current and available please note:
· there may be errors or omission in it
· there may be occasions where the data is not available and/or the website will be unavailable.
The City of Melbourne and its employees accept no responsibility for any loss, damage, claim, expense, cost or liability whatsoever (including in contract, tort including negligence, pursuant to statue and otherwise) arising in respect of or in connection with accessing, using or reliance upon the data in this website, or the unavailability of the data or the website.

Download Photomesh data:

To download the zip file containing all relevant files representing the 3D city mesh model, please click on the provided link.