Lidar collected from a helicopter of the Country of Grenada. Building footprints and 3D Buildings were extracted from the lidar using the points classified as buildings. 3D Basemap solution tools were used to create the building footprints and 3D buildings.

Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.

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..

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente-Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

The Digital Bedrock Geologic-GIS Map of the Twin Bridges Quadrangle, Tennessee is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (twbr_bedrock_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (twbr_bedrock_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (obed_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (obed_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (twbr_bedrock_geology_metadata_faq.pdf). Please read the obed_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Tennessee Division of Geology. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (twbr_bedrock_geology_metadata.txt or twbr_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Digital Geologic-GIS Map of the Twin Rocks 7.5' Quadrangle, Utah is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (twro_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (twro_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (care_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (care_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (twro_geology_metadata_faq.pdf). Please read the care_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Utah Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (twro_geology_metadata.txt or twro_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Location Intelligence Market size was valued at USD 18.5 Billion in 2023 and is projected to reach USD 63.15 Billion by 2030, growing at a CAGR of 15.63% during the forecasted period 2024 to 2030.Global Location Intelligence Market DriversThe growth and development of the Location Intelligence Market drivers. These factors have a big impact on how Location Intelligence are demanded and adopted in different sectors. Several of the major market forces are as follows:Proliferation of Spatial Data: A rich source of data for location intelligence and analytics is made possible by the exponential increase of spatial data produced by sources including GPS-enabled devices, Internet of Things sensors, and geographic information systems (GIS). In order to extract meaningful insights, there is a growing need for sophisticated tools and technologies due to the volume and diversity of spatial data.Location-Based Services (LBS) are Growing: The demand for location intelligence and analytics solutions is fueled by the widespread use of location-based services including ride-sharing services, navigation apps, and location-based marketing. Companies use location data to target services based on local context, optimize operations, and improve customer experiences.Need for Real-time information: To make wise judgments swiftly in the hectic business world of today, businesses need to have real-time access to location-based information. Businesses may increase agility and responsiveness by using location intelligence and analytics solutions to monitor events, identify patterns, and react to changes in real-time.The amalgamation of location: intelligence and analytics with nascent technologies such as artificial intelligence (AI) and the Internet of Things (IoT) amplifies their potential and value proposition. Through the integration of sensor data, AI algorithms, and location data, enterprises may gain more profound understanding, anticipate future patterns, and streamline their decision-making procedures.Urbanization and Smart City Initiatives: The use of location intelligence and analytics solutions is fueled by the global trend toward urbanization and the growth of smart city initiatives. These technologies help municipalities, urban planners, and government agencies create sustainable and effective urban environments by optimizing infrastructure development, city planning, and service delivery.Cross-Industry Applications: Location analytics and intelligence are useful in a variety of industries, such as banking, logistics, healthcare, and retail. Businesses use location-based data to increase risk management, streamline supply chains, target customers more effectively, and increase operational efficiency across a range of company operations.Regulatory Compliance and Risk Management: The use of location intelligence and analytics solutions for regulatory compliance and risk management is influenced by compliance requirements relating to location-based data, such as privacy laws and geospatial standards. These products are purchased by organizations to guarantee data governance, reduce risks, and prove compliance with legal and regulatory obligations.The need for location-based: marketing is growing as companies use location analytics and intelligence to create more focused advertising and marketing campaigns. Organizations may increase customer engagement and conversion rates by providing tailored offers, promotions, and content depending on the geographic context of their customers by evaluating location data and consumer activity patterns.Emergence of Digital Twin Technology: This technology opens up new possibilities for location intelligence and analytics by building virtual versions of real assets or environments. Organizations can improve decision-making processes in a variety of fields, such as manufacturing, infrastructure management, and urban planning, by incorporating location data into digital twin models and simulating scenarios.

The Digital Geologic-GIS Map of parts of the Twin Peaks and Blanco Peak Quadrangles, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (tpbp_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (tpbp_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (tpbp_geology_metadata_faq.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (tpbp_geology_metadata.txt or tpbp_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Biodiversity objects are to date almost absent from the BIM world. Developing the biodiversity theme in the BIM environment offers unexplored research opportunities with strong impact at the same time for biodiversity and transport infrastructure management. Efficient mainstreaming of biodiversity in transport infrastructure would require the GIS, BIM, Digital Twin dedicated software interoperability.

Zip bestand met hierin de gekarteerde panden (LOD 2.1) en BAG extrusie panden.Bestandsformaat: Autodesk 3ds.Actualisatie: 17/10/2022.(deze set wordt telkens uitgebreid zodat Capelle volledig in LOD 2.1 als Digital Twin model is te gebruiken)