The goal of the USGS 3D Elevation Program (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in Entwine Point Tiles format, which a lossless, full-density, streamable octree based on LASzip (LAZ) encoding. The second resource is a Requester Pays of the original, Raw LAZ (Compressed LAS) 1.4 3DEP format, and more complete in coverage, as sources with incomplete or missing CRS, will not have an ETP tile generated. Resource names in both buckets correspond to the USGS project names.

Documentation

https://github.com/hobu/usgs-lidar/

Update Frequency

Periodically

License

US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.

LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3. This dataset, managed by the Office of the Chief Technology Officer (OCTO), through the direction of the District of Columbia GIS program, contains tiled point cloud data for the entire District along with associated metadata.

Documentation

2015 data, 2018 data

Update Frequency

The most recent data is from 2018 and 2015 data is available as well. A new data acquisition is planned for 2020.

License

See Washington, DC Terms of Use

The shapefile provides the exact location and the attributes of the LiDAR transects including sampling criteria, if the field data is available, who is the field data owner, the average metrics, and other relevant information. The metrics were extracted from the original point cloud, without any outlier cleaning. The EBA project collects the LiDAR transects between 2016 and 2018. Each transect covered 375 ha (12.5 km × 300 m) by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per square meters, the field of view was equal to 30°, the flying altitude was 600 m, and transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was set to be below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and under 0.5 m, respectively. Some transects were randomly spread over the mature forest layer retrieved from the PRODES database (INPE 2019) and the secondary forest layer retrieved from the TerraClass database (INPE 2018), and other were selected to overlad field data from partners institutions.

In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.

We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (https://doi.org/10.5281/zenodo.4968706).

To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.

This deposit delivers data from Acre (1 zip file) and Rondônia (1 zip file).

Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.

Data sets contain:Sample sites Pool Location in (shp)Object Detection Models (OD)BlendConvex_ODN°PoolBlendSlope_ODN°PoolPixel Segmentation Models (PS)SLN°Pool_LRPCN°Pool_LRStatisticsGraphs_LossAccuracy (script R)Results_Tensorboard (xlsx data set used inside the script)ResultsPredicted BBOX P.Convexity-DTM132_020.shp (OD)Predicted BBOX P.Convexity-DTM522_017.shp (OD)Predicted OilPool Slope-DTM132_020.shp (PS)Predicted OilPool Slope-DTM522_017.shp (PS)

The ABLE 2A and 2B (Atmospheric Boundary Layer Experiments) data consists of estimates of the rate of exchange of a wide variety of aerosols and gases between the Amazon Basin and its atmospheric boundary layer, and the processes by which these aerosols and gases are moved between the boundary layer and the free troposphere. The data are presented in gzipped ASCII text files in Global Tropospheric Experiment (GTE) format.

The ABLE-2 project consisted of two expeditions: the first in the Amazonian dry season (ABLE-2A, July-August 1985); and the second in the wet season (ABLE-2B, April-May 1987). The ABLE-2 core research data were gathered by NASA Electra aircraft flights that stretched from Belem, at the mouth of the Amazon River, west to Tabatinga, on the Brazil-Colombia border, from a base at Manaus in the heart of the forest. See Figure 1. These observations were supplemented by ground based chemical and meteorological measurements in the dry forest, the Amazon floodplain, and the tributary rivers through use of enclosures, an instrumented tower in the jungle, a large tethered balloon, and weather and ozone sondes.

This study showed air above the Amazon jungle to be extremely clean during the wet season but air quality deteriorated dramatically during the dry season as the result of biomass burning, performed mostly at the edges of the forest. Biomass burning is also a source of greenhouse gases carbon dioxide and methane, as well as other pollutants (carbon monoxide and oxides of nitrogen). Amazonian ozone deposition rates were found to be 5 to 50 times higher than those previously measured over pine forests and water surfaces. The Amazon River floodplain is a globally significant source of methane, supplying about 12% of the estimated worldwide total from all wetlands sources. Over Amazonia, carbon monoxide is enhanced by factors ranging from 1.2 to 2.7 by comparison with adjacent regions due to isoprene oxidation and biomass burning. Over the rainforest individual convective storms transport 200 megatons of air per hour, of which 3 megatons is water vapor that releases 100,000 megawatts of energy into the atmosphere through condensation into rain.

The ABLE was a collaboration of U.S. and Brazilian scientists sponsored by NASA and Instituto Nacional de Pesquisas Espaciais (INPE) and supported by the Global Tropospheric Experiment (GTE) component of the NASA Tropospheric Chemistry Program.

This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.

The objective of the Mapa 3D Digital da Cidade (M3DC) of the São Paulo City Hall is to publish LiDAR point cloud data. The initial data was acquired in 2017 by aerial surveying and future data will be added. This publicly accessible dataset is provided in the Entwine Point Tiles format as a lossless octree, full density, based on LASzip (LAZ) encoding.

This dataset contains a feature layer of orthophoto and lidar tile boundaries. This includes AA07-E14. The attribute table has links for LiDAR downloads via Amazon Web Services, upon clicking the link the download will start right away.Disclaimer: The User acknowledges and agrees that the Data is provided by the City to the User for the User's convenience and reference, and that the City makes no guarantees, representations or warranties, whether express or implied, as to the Data or as to any results to be or intended to be achieved from use of the Data, including without limitation guarantees, representations or warranties as to the accuracy, quality or completeness of the Data, merchantability or fitness for use for any particular purpose, and the User hereby waives all guarantees, representations and warranties in respect of the Data, whether express, implied by statute or otherwise. The User hereby releases The City from, and hereby indemnifies and holds harmless The City from and against, any liability, obligation, costs (including without limitation legal costs), expenses, claims, actions, proceedings, damages and penalties to the User or any other person or legal entity resulting from or related to the use, disclosure or reproduction of or reliance on the Data.

Those data represent the Brazilian Amazon by 68,629,072 250-m pixels, and 141,032 pixels have LiDAR information and therefore had the AGB estimated, and converted to Mg ha-1 (file trainning_dataset.csv). To generate a wall-to-wall map of the Brazilian Amazon at 250-m resolution, we trained a Random Forest (RF) model 29 using the AGB estimated pixels and remote sensing layers formed by: MODIS vegetation indices, Shuttle Radar Topography Mission (SRTM) data, Tropical Rainfall Measuring Mission (TRMM), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, along with the central coordinates of each 250m pixel, stored in amazon_*.csv files.

Derived from MODIS, we used the Vegetation Indices 16-Day L3 Global 250m temporal series (MOD13Q1) products from 2016, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), from MODIS. From TRMM we used the 3B43 V6 precipitation data, with each pixel value representing the monthly accumulated precipitation from 1998 to 2016 at a resolution of 0.25 degrees. From PALSAR, we used the L-band image in the HH and HV polarizations, acquired in 2015. When necessary, the remote sensing products were resampled by means to a 250 m grid.

Random Forest models were tested using the H2O_Flow platform and produced the best model based on RMSE and R², containing NDVI q3, PALSAR HV, TRMM mean, X, SRTM, Y, PALSAR HH, EVI q1, EVI mean, NDVI mean, NDVI q1, EVI q3.

3D topographic representation of the territory in the form of a point cloud. LiDAR (Light Detection and Ranging) technology makes it possible to represent the Earth's surface topographically in three dimensions using a laser system mounted on board an aircraft. The very large number of 3D points recorded (up to 400,000 per second) makes it possible to obtain a multitude of details at the level of the ground and surface elements. LiDAR technology quickly, easily, and above all accurately provides the altitude of ground details and elements above ground, even in the presence of dense vegetation. The uses are: creation of a digital terrain model (DTM), creation of level curves, creation of level curves, volume calculation, planning, calculation of tree heights, mapping of building roofs, 3D modeling of cities, etc. Source: XEOS imagery inc.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

LiDAR generated point cloud acquired in March 2013 for the entire area of Jefferson County, Alabama (1,124 Square Miles).

This metadata supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV is leveraging the Entwine Point Tiles (EPT) hosted by USGS on Amazon Web Services.

_The link: * Access the data directory is available in the sectionDataset description sheets; Additional information. Products derived from LiDAR (Light Detection and Ranging) are generated as part of the _provincial LiDAR sensor data acquisition project. It is therefore to facilitate the use of raw LiDAR data and optimize its benefits that the Ministry of Natural Resources and Forests (MRNF) generated and made available products derived from LiDAR in a user-friendly format. LiDAR technology accurately provides ground altitude, forest cover height (canopy), and slopes. Here is the list of four derived products: + Digital terrain model (spatial resolution: 1 m) + Digital terrain model in shaded relief (spatial resolution: 2 m) + Canopy height model (spatial resolution: 1 m) + Slopes (spatial resolution: 2 m) By 2022, the territory covered by these data will be the majority of southern Quebec (south of the 52nd parallel). These products are distributed by map sheet at a scale of 1/20,000. Note: The resolution of these four accessible products has been slightly degraded in visualization in the interactive map to ensure efficient display.This third party metadata element was translated using an automated translation tool (Amazon Translate).*

Underway ALF Data for the Amazon River plume

Two maps are provided from a study of the aboveground carbon stocks of the Peruvian Andes and Amazon basin. The maps are based on airborne light detection and ranging (lidar) data, combined with satellite-based maps of forest cover and properties, acquired in 2012. The resulting maps are: (1) aboveground carbon density or ACD, and (2) uncertainty in ACD. Units for ACD are Mg C per hectare. Maps are provided at 1.0 ha spatial resolution.

Use of these data require citation of this dataset and the original journal paper that delivered the mapping method. These citations are as follows:

Asner, G.P., D.E. Knapp, R.E. Martin, R. Tupayachi, C.B. Anderson, J. Mascaro, F. Sinca, K.D. Chadwick, M. Higgins, W. Farfan, W. Llactayo, and M.R. Silman. 2014. Targeted carbon conservation at national scales with high-resolution monitoring. Proceedings of the National Academy of Sciences 111(47):E5016-E5022 doi:10.1073/pnas.1419550111

Asner, G.P., D.E. Knapp, R.E. Martin, R. Tupayachi, C.B. Anderson, J. Mascaro, ..., M.R. Silman. 2021. Global Airborne Observatory: Forest Carbon Stocks of Peru (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4626309

This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in Milenkovic et al. 2022. The code required to process the data is on: https://github.com/MilutinMM/SecFor-Regrowth.git.

Short descriptions of files:

• ATL08_gdf.json - ICESat-2 ATL08 segments in Rondonia
• ATL08_gdf_Para_MG.json - ICESat-2 ATL08 segments intersecting the two calibration sites
• ATL08_h5_fileNames_Rondonia.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)
• calibartionModels.zip - GEDI and ICESat-2 calibration models and statistics (xlsx files)
• deforested_poligons_2018_2019.zip - SPH file of a deforested polygon in the calibration site
• gedi_L2A_allTime_gdf_Para_MG.json - GEDI shots intersecting the two calibration sites
• gedi_L2A_allTime_MG_all.csv - GEDI shots within the FN calibration site
• gedi_L2A_allTime_Para_all.csv - GEDI shots within the TNF calibration site
• GEDI_L2A_fileNames_Rondonia.txt - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)
• gedi_L2A_gdf_Para_MG_sens_a2.json - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2
• gedi_L2A_gdf_sens_a2.json - GEDI shots in Rondonia
• gedi_L2A_MG_all_sens_a2.csv - GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)
• gedi_L2A_Para_all_sens_a2.csv - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)
• gedi_L2A_Rondonia_all_sens_a2.csv - GEDI shots in Rondonia
• MG_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site
• Para_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site
• svbr-rondonia-2018.tif - Forest age map for Rondonia (Silva Junior et al. 2020)
• svbr-rondonia-2018_bw_eroded.tif - a secondary forest extent mask with removed border pixels

References:

Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, Science of Remote Sensing, 2022, 100051, ISSN 2666-0172, https://doi.org/10.1016/j.srs.2022.100051.

Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci Data 7, 269 (2020). https://doi.org/10.1038/s41597-020-00600-4

This data set contains the configurations for the ED2 simulations presented in the following manuscript:

Longo, M., M. Keller, L. M. Kueppers, K. Bowman, O. Csillik, A. Ferraz, P. R. Moorcroft, J. P. Ometto, B. S. Soares-Filho, X. Xu, M. L. F. de Assis, E. B. Görgens, E. J. L. Larson, J. F. Needham, E. M. Ordway, F. R. S. Pereira, E. Rangel Pinagé, L. Sato, L. Xu and S. Saatchi. Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes. In review.

The configurations are organised into five compressed folders, corresponding to the simulation steps.

• R001_BrAmaz_s1c1t1l0f0.tgz. This is the spin up step of simulation R006_BrAmaz_s1c0t1l0f0 (Recovery in the manuscript). These runs were initialised with airborne lidar data and run with vegetation dynamics enabled and natural disturbance only. The final step of this result became the initial condition for R006_BrAmaz_s1c0t1l0f0 (Recovery), but otherwise this simulation was not analysed in the manuscript.
• R003_BrAmaz_s1c1t1l1f0.tgz. This is the spin up step of simulation R005_BrAmaz_s1c0t1l1f0 (Degradation in the manuscript). These runs were initialised with airborne lidar data and run with vegetation dynamics enabled and natural disturbance and anthropogenic disturbances. The final step of this result became the initial condition for R005_BrAmaz_s1c0t1l1f0 (Degradation), but otherwise this simulation was not analysed in the manuscript.
• R004_BrAmaz_s1c0t0l0f0.tgz. This is the simulation Control in the manuscript. This simulation was initialised with airborne lidar forest structure and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.
• R005_BrAmaz_s1c0t1l1f0.tgz. This is the simulation Degradation in the manuscript. This simulation was initialised with the last time step of simulation R003_BrAmaz_s1c1t1l1f0, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.
• R006_BrAmaz_s1c0t1l0f0.tgz. This is the simulation Recovery in the manuscript. This simulation was initialised with the last time step of simulation R001_BrAmaz_s1c1t1l0f0, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.

The https://dx.doi.org/10.5281/zenodo.14768399" target="_blank" rel="noopener">initial conditions and https://dx.doi.org/10.5281/zenodo.14773328" target="_blank" rel="noopener">boundary conditions are provided in the linked archives. In addition, in each directory, there are 350 sub-directories with a name structure that follows this example: ta0006_lon-60.50_lat-12.50_ifire00. In this example, ta0006 is the grid cell ID 0006, and lon-60.50 and lat-12.50 are the coordinates of the grid cell centre (60.5°W; 12.5°S, respectively). The key ifire00 is always zero, as a reminder that fires were disabled in all runs. . In addition, each dire

• ED2IN. This is the namelist used for the simulation of each individual grid cell. For additional information on the namelist variables, check the ED2 Wiki page.
• read_monthly.r. This script reads in the analysis output files, carries out some minimal processing of the monthly averages (e.g., unit conversion, simple aggregations), and saves R objects. These scripts require the folder Rsc (also provided, see below), and multiple packages. The script is old, so in case packages are missing and cannot be installed, try commenting out the package in Rsc/load.everything.r, because they may not be needed. The one obsolete package that is required is R package hdf5, which is also provided (see below).
• histo. This directory contains the first and last history (restart) files generated by the simulation.

In addition, the following files are provided outside the sub-folder structure

• 01_regional_gridded.r. This script concatenates the RData objects from each individual run, and creates a single RData file for each simulation, with a subset of variables of interest. These scripts require the folder Rsc (also provided, see below), and multiple packages. The script is old, so in case packages are missing and cannot be installed, try commenting out the package in Rsc/load.everything.r, because they may not be needed
• Rsc.tgz. A suite of R scripts that may be called by read_monthly.r or 01_regional_gridded.r. Make sure the correct path is given in read_monthly.r or 01_regional_gridded.r scripts, and these scripts should be automatically loaded.
• hdf5_1.6.12.tar.gz. This is the source code of the now obsolete hdf5 R package. To install it, start an R session, set the working directory to be the same path where hdf5_1.6.12.tar.gz is located, and run the following command: install.packages(“hdf5_1.6.12.tar.gz”,repos=NULL). Additional configuration may be needed if the C compiler and/or the hdf5 libraries are not in default locations.

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..