Discover the booming Geographic Information System (GIS) market! This in-depth analysis reveals a $25 billion market in 2025, projected for significant growth driven by smart city initiatives, location-based services, and AI. Explore key trends, leading companies, and regional insights to understand this lucrative sector.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 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 GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. 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: 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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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 Google Earth, ArcGIS, 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).

Discover the booming photogrammetry software market! This in-depth analysis reveals key trends, growth drivers, and leading companies shaping this industry. Explore market size projections, regional breakdowns, and future opportunities in 3D modeling and mapping.

The "Map Imager Layer - Administrative Boundaries" is a Map Image Layer of Administrative Boundaries. It has been designed specifically for use in ArcGIS Online (and will not directly work in ArcMap or ArcPro). This data has been modified from the original source data to serve a specific business purpose. This data is for cartographic purposes only.The Administrative Boundaries Data Group contains the following layers: Populated Places (USGS)US Census Urbanized Areas and Urban Clusters (USCB)US Census Minor Civil Divisions (USCB)PLSS Townships (MnDNR, MnGeo)Counties (USCB)American Indian, Alaska Native, Native Hawaiian (AIANNH) Areas (USCB)States (USCB)Countries (MPCA)These datasets have not been optimized for fast display (but rather they maintain their original shape/precision), therefore it is recommend that filtering is used to show only the features of interest. For more information about using filters please see "Work with map layers: Apply Filters": https://doc.arcgis.com/en/arcgis-online/create-maps/apply-filters.htmFor additional information about the Administrative Boundary Dataset please see:United States Census Bureau TIGER/Line Shapefiles and TIGER/Line Files Technical Documentation: https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlUnited States Census Bureau Census Mapping Files: https://www.census.gov/geographies/mapping-files.htmlUnited States Census Bureau TIGER/Line Shapefiles: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html and https://www.census.gov/cgi-bin/geo/shapefiles/index.php

Discover the explosive growth of the Knowledge Area Mapping Map market! Our comprehensive analysis reveals key trends, drivers, and restraints shaping this $500 million (2025 est.) industry, segmented by application and region. Project your business strategy with our forecast to 2033. Explore market share data and competitive landscape insights.

Discover the booming G Suite Creative Tools market! Explore its $2 billion valuation, 15% CAGR, key players (Google, Square, Meister), and regional insights. Learn about market drivers, trends, and challenges shaping this dynamic sector.

A digital raster graphic (DRG) is a scanned image of an U.S. Geological Survey (USGS) standard series topographic map, including all map collar information. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator projection. The horizontal positional accuracy and datum of the DRG matches the accuracy and datum of the source map. The map is scanned at a minimum resolution of 250 dots per inch. DRG's are created by scanning published paper maps on high-resolution scanners. The raster image is georeferenced and fit to the UTM projection. Colors are standardized to remove scanner limitations and artifacts. The average data set size is about 6 megabytes in Tagged Image File Format (TIFF) with PackBits compression. DRG's can be easily combined with other digital cartographic products such as digital elevation models (DEM) and digital orthophoto quadrangles (DOQ). DRG's are stored as rectified TIFF files in geoTIFF format. GeoTIFF is a relatively new TIFF image storage format that incorporates georeferencing information in the header. This allows software, such as ArcView, ARC/INFO, or EPPL7 to reference the image without an additional header or world file. Within the Minnesota Department of Natural Resources Core GIS data set the DRG's have been processed to be in compliance with departmental data standards (UTM Extended Zone 15, NAD83 datum) and the map collar information has been removed to facilitate the display of the DRG's in a seamless fashion. These DRG's were clipped and transformed to UTM Zone 15 using EPPL7 Raster GIS.

DRGs are a useful backdrop for mapping a variety of features. Colors representing various items in the image (contours, urban areas, vegetation, etc.) can be turned off or highlighted depending on the mapping application. In ArcView this is done by choosing the "Colormap" option when editing the DRG theme's legend. A variety of other ArcView tools exist to make working with DRGs easier.

Also included:
Metadata for the scanned USGS 24k Topograpic Map Series (also known as 24k Digital Raster Graphic). Each scanned map is represented by a polygon in the layer and the map date, photo revision date, and photo interpretation date are found in the corresponding attribute record. This layer facilitates searching for DRGs which were created or revised on or between particular dates. Also useful for ascertaining when a particular map sheet was created.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

In May 2014, staff at the San Bernardino National Wildlife Refuge (SBNWR) requested the production of a vegetation map to document the ongoing restoration of the refuge. Utilizing object-based image analysis (OBIA) a 9 class vegetation map was produced. This was a piloted effort to develop a simple, repeatable and low-cost land cover mapping framework that could be carried out on other refuges. Thus, iterative steps were taken and refined as part of the mapping process. This document has a Digital Object Identifier: http://dx.doi.org/10.7944/W3WC7M

Pangenome Graph Mapping Services Market Outlook

According to our latest research, the global pangenome graph mapping services market size reached USD 1.12 billion in 2024, reflecting the growing adoption of advanced genomic technologies across multiple sectors. The market is expected to expand at a robust CAGR of 15.4% during the forecast period, reaching USD 3.36 billion by 2033. This growth is propelled by the increasing demand for high-resolution genomic analysis, the rising prevalence of complex diseases requiring precision diagnostics, and the expanding application of genomics in agriculture and drug discovery. The integration of artificial intelligence and machine learning with pangenome graph mapping is further fueling market expansion, as these technologies enhance the accuracy and efficiency of genomic data interpretation.

The primary growth driver for the pangenome graph mapping services market is the escalating need for comprehensive genomic analysis, particularly in clinical diagnostics and personalized medicine. As genomic data becomes increasingly complex, traditional linear reference genomes are proving inadequate for capturing the full spectrum of genetic variation within populations. Pangenome graph mapping addresses this limitation by enabling the visualization and analysis of diverse genetic sequences, thereby supporting more accurate disease diagnosis and treatment planning. The surge in rare and complex disease cases, coupled with the necessity for tailored therapeutic strategies, is compelling healthcare providers and researchers to adopt advanced pangenome-based solutions. This shift is not only enhancing patient outcomes but also reducing the time and cost associated with genetic testing and drug development.

Another significant factor contributing to market growth is the rapid technological advancement in sequencing platforms and computational biology. The decreasing cost of next-generation sequencing (NGS) technologies has made large-scale genomic studies more accessible to both academic and commercial entities. Furthermore, the integration of cloud-based platforms and high-performance computing infrastructures is enabling the efficient handling and analysis of massive genomic datasets. Companies offering pangenome graph mapping services are leveraging these advancements to deliver scalable, high-throughput, and cost-effective solutions to a wide range of end-users. The development of user-friendly software tools and visualization platforms is also democratizing access to sophisticated genomic analysis, fostering innovation and collaboration across the global scientific community.

A third growth catalyst is the increasing application of pangenome graph mapping in agriculture and evolutionary studies. In agriculture, these services are instrumental in identifying genetic traits associated with crop yield, disease resistance, and environmental adaptation. By constructing comprehensive pangenome graphs for various plant and animal species, researchers can accelerate breeding programs and develop more resilient agricultural products. In evolutionary biology, pangenome analysis provides critical insights into the genetic diversity and evolutionary history of populations, facilitating the study of adaptation, speciation, and natural selection. The cross-disciplinary utility of pangenome graph mapping is thus broadening its market reach and solidifying its role as a cornerstone technology in modern genomics.

From a regional perspective, North America currently dominates the pangenome graph mapping services market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of advanced healthcare infrastructure, substantial investments in genomic research, and a high concentration of leading biotechnology and pharmaceutical companies. Europe follows closely, driven by robust government funding for life sciences and a strong network of research institutions. The Asia Pacific region is emerging as a high-growth market, propelled by increasing R&D activities, rising healthcare expenditure, and the expansion of genomics initiatives in countries such as China, Japan, and India. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by growing awareness and improving healthcare infrastructure.

3D Mapping and 3D Modeling Software Market Outlook

The global 3D mapping and 3D modeling software market size was valued at approximately USD 4.5 billion in 2023 and is expected to reach USD 9.8 billion by 2032, growing at a CAGR of 9.1% during the forecast period. This significant growth is driven by continuous advancements in technology, surging demand across various industries, and the increasing adoption of 3D graphics in visualization, simulation, and planning processes.

One of the primary growth factors of the 3D mapping and 3D modeling software market is the rapid technological advancements in computing power, graphics processing units (GPUs), and software algorithms. These advancements have made it possible to create highly detailed and realistic 3D models, which are being utilized in a wide range of applications including urban planning, disaster management, and entertainment. Furthermore, the growing trend of digital twins in the manufacturing and construction sectors is further propelling the demand for advanced 3D mapping and modeling solutions.

In addition to technological advancements, the growing adoption of 3D mapping and modeling software in the healthcare industry is another crucial factor driving market growth. Medical professionals are increasingly using 3D models for pre-surgical planning, patient diagnosis, and treatment simulation. The ability to create accurate anatomical models aids in better understanding of complex medical conditions, thereby improving patient outcomes. Moreover, the integration of 3D technology with virtual and augmented reality is opening new frontiers in medical training and remote surgeries.

The entertainment industry also plays a significant role in the expansion of the 3D mapping and modeling software market. The application of 3D technology in film production, gaming, and virtual reality experiences has revolutionized the way content is created and consumed. High demand for immersive and interactive experiences is pushing the boundaries of what 3D software can achieve. This has led to substantial investments in research and development, further enhancing the capabilities of these software solutions.

Building 3D Modeling Software has become an integral part of the technological advancements driving the 3D mapping and modeling software market. These tools are essential for creating detailed and accurate representations of physical spaces, which are crucial for various applications such as architecture, urban planning, and construction. By enabling users to visualize and manipulate complex structures in a virtual environment, Building 3D Modeling Software enhances design precision and efficiency. This capability is particularly beneficial for architects and engineers who need to test different design scenarios and optimize their projects before actual construction begins. The software's ability to integrate with other technologies, such as virtual reality and augmented reality, further expands its utility, offering immersive experiences that facilitate better communication and collaboration among project stakeholders.

Regional outlook reveals that North America holds the largest share in the 3D mapping and 3D modeling software market, owing to the early adoption of advanced technologies and strong presence of key market players. However, the Asia-Pacific region is expected to witness the highest growth rate during the forecast period. This growth is attributed to the rapid urbanization, expanding construction activities, and increasing investments in infrastructure development in countries like China and India.

Component Analysis

In the component segment, the market is categorized into software and services. The software component dominates the market due to its extensive use in creating detailed 3D models across various industries. The continuous evolution of software capabilities, driven by advancements in artificial intelligence and machine learning, has enabled the development of more accurate and efficient modeling tools. These tools are essential for planning and simulation in sectors like architecture, construction, and urban planning.

The services component, though smaller in comparison to software, is gaining traction due to the increasing need for customization and technical support. Services include consulting, implementation, training, and maintenance, which are crucial for the effective deployment and utilization of 3D mappi

Comprehensive design tools market map featuring 100+ companies across UI/UX design, graphic design, prototyping, animation, design systems, and collaboration tools.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sahi_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 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 GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_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 (sahi_geology_metadata_faq.pdf). Please read the sahi_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. 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: 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 (sahi_geology_metadata.txt or sahi_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 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 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 Google Earth, ArcGIS, 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).

MRI Datasets from the GUFI Traveling Heads experiment at 7T.

2 Subjects 10 Sites

The same quantitative imaging protocol at all sites consisting of:

B1 and B0 mapping MP2RAGE QSM CEST Relaxometry

The sites were organized in the German Ultrahigh Field Imaging network (GUFI, www.mr-gufi.de) and discriminate by hard- and software differences of the 7T systems from different generations (same vendor): Configuration 1: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 24ch, Software: VB Datasets: BER_20181211, HEI_20190205 Configuration 2: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: ES_20181008, ES_20190813 Configuration 3: Magnet: Passively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: MAG_20190114, LEI_20190115, WIE_20190404 Configuration 4: Magnet: Actively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: BN_20181009 Configuration 5: Magnet: Actively shielded, Gradient Coil: 80mT/m, RFPA: 11kW, RF Coil: 32ch, Software: VE Datasets: ERL_20181019, ERL_20190226, ERL_20190618, JUL_20181212, JUL_20190604, WUE_20190125, WUE_20190617

One full dataset includes:

b0fieldHZ: B0 field mapped in Hz

b1map_mtflash_reg: rel. B1 map registered to the mtflash dataset for B1 correction of relaxometry data

b1rel: rel. B1 map original image space (100*measured flip/nominal flip)

brainmask_mp2rage: brain mask calculated with CBS tools, ANTS and FSL for MP2RAGE data

CEST_NOE: rNOE map derived from the CEST analysis

CEST_APT: APT map derived from the CEST analysis

CEST_MT: MT map derived from the CEST analysis

CEST3D06: CEST image data for B1=0.6uT

CEST3D06: CEST image data for B1=0.9uT

CEST3DWASABI: Correction data for the CEST calculation

gre_qsm: QSM Map calculated from the GRE data in ppB

gre_qsm_mag: Multiecho-GRE magnitude image data for QSM

gre_qsm_phs: Multiecho-GRE phase image data for QSM

mp2rage_inv1: MP2RAGE image data first inversion contrast

mp2rage_inv2: MP2RAGE image data second inversion contrast

mp2rage_T1_corr: MP2RAGE derived T1 map after additional transmit B1 correction with B1 data

mp2rage_T1_gdc_brain: MP2RAGE T1 map after brain extraction and gradient distortion correction (used for inter-site comparisons)

mp2rage_uni_corr: MP2RAGE uniform images after additional transmit B1 correction with B1 data

mp2rage_uni_gdc_brain: MP2RAGE uniform images after brain extraction and gradient distortion correction (used for inter-site comparisons)

mpm_PD: Proton Density map (in %) derived from the multiparametic analysis of the mtflash data

mpm_T1: T1 map (in s) derived from the multiparametic analysis of the mtflash data

mpm_T2s: T2* map (in ms) derived from the multiparametic analysis of the mtflash data

mtflash3dPD: Multiecho FLASH images in PD weighting for multiparametic analysis

mtflash3dT1: Multiecho FLASH images in T1 weighting for multiparametic analysis

Not all data may be available for every measurement.

For further information on the dataset and the methods used for analysis please refer to the corresponding paper: M. N. Voelker et al., “The Traveling Heads 2.0: Multicenter Reproducibility of Quantitative Imaging Methods at 7 Tesla,” Neuroimage, p. 117910, Feb. 2021. https://doi.org/10.1016/j.neuroimage.2021.117910 Please cite if you use the GUFI data!

The first upload (TH2_data_ES_s1.zip) consists of the one full dataset derived at the first measurement at configuration 2 of subject 1 and was intended for the review process (CEST results of this upload were refined during review) of the corresponding paper. The full dataset (TH2_alldata.zip) was uploaded as an update under this project number.

The distance between the atlas and individual brains at the anterior commissure, posterior commissure, and left and right anterior aspect of the caudate. All values are in millimeters.Landmark Variation.

The Audio Cartography project investigated the influence of temporal arrangement on the interpretation of information from a simple spatial data set. I designed and implemented three auditory map types (audio types), and evaluated differences in the responses to those audio types.

The three audio types represented simplified raster data (eight rows x eight columns). First, a "sequential" representation read values one at a time from each cell of the raster, following an English reading order, and encoded the data value as loudness of a single fixed-duration and fixed-frequency note. Second, an augmented-sequential ("augmented") representation used the same reading order, but encoded the data value as volume, the row as frequency, and the column as the rate of the notes play (constant total cell duration). Third, a "concurrent" representation used the same encoding as the augmented type, but allowed the notes to overlap in time.

Participants completed a training session in a computer-lab setting, where they were introduced to the audio types and practiced making a comparison between data values at two locations within the display based on what they heard. The training sessions, including associated paperwork, lasted up to one hour. In a second study session, participants listened to the auditory maps and made decisions about the data they represented while the fMRI scanner recorded digital brain images.

The task consisted of listening to an auditory representation of geospatial data ("map"), and then making a decision about the relative values of data at two specified locations. After listening to the map ("listen"), a graphic depicted two locations within a square (white background). Each location was marked with a small square (size: 2x2 grid cells); one square had a black solid outline and transparent black fill, the other had a red dashed outline and transparent red fill. The decision ("response") was made under one of two conditions. Under the active listening condition ("active") the map was played a second time while participants made their decision; in the memory condition ("memory"), a decision was made in relative quiet (general scanner noises and intermittent acquisition noise persisted). During the initial map listening, participants were aware of neither the locations of the response options within the map extent, nor the response conditions under which they would make their decision. Participants could respond any time after the graphic was displayed; once a response was entered, the playback stopped (active response condition only) and the presentation continued to the next trial.

Data was collected in accordance with a protocol approved by the Institutional Review Board at the University of Oregon.

• Additional details about the specific maps used in this are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

• Details of the design process and evaluation are provided in the associated dissertation, which is available from ProQuest and University of Oregon's ScholarsBank.

• Scripts that created the experimental stimuli and automated processing are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

Preparation of fMRI Data

Conversion of the DICOM files produced by the scanner to NiFTi format was performed by MRIConvert (LCNI). Orientation to standard axes was performed and recorded in the NiFTi header (FMRIB, fslreorient2std). The excess slices in the anatomical images that represented tissue in the next were trimmed (FMRIB, robustfov). Participant identity was protected through automated defacing of the anatomical data (FreeSurfer, mri_deface), with additional post-processing to ensure that no brain voxels were erroneously removed from the image (FMRIB, BET; brain mask dilated with three iterations "fslmaths -dilM").

Preparation of Metadata

The dcm2niix tool (Rorden) was used to create draft JSON sidecar files with metadata extracted from the DICOM headers. The draft sidecar file were revised to augment the JSON elements with additional tags (e.g., "Orientation" and "TaskDescription") and to make a more human-friendly version of tag contents (e.g., "InstitutionAddress" and "DepartmentName"). The device serial number was constant throughout the data collection (i.e., all data collection was conducted on the same scanner), and the respective metadata values were replaced with an anonymous identifier: "Scanner1".

Preparation of Behavioral Data

The stimuli consisted of eighteen auditory maps. Spatial data were generated with the rgeos, sp, and spatstat libraries in R; auditory maps were rendered with the Pyo (Belanger) library for Python and prepared for presentation in Audacity. Stimuli were presented using PsychoPy (Peirce, 2007), which produced log files from which event details were extracted. The log files included timestamped entries for stimulus timing and trigger pulses from the scanner.

• Log files are available in "sourcedata/behavioral".
• Extracted event details accompany BOLD images in "sub-NN/func/*events.tsv".
• Three column explanatory variable files are in "derivatives/ev/sub-NN".

References

Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. The name Audacity® is a registered trademark of Dominic Mazzoni.

FMRIB (Functional Magnetic Resonance Imaging of the Brain). FMRIB Software Library (FSL; fslreorient2std, robustfov, BET). Oxford, v5.0.9, Available: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

FreeSurfer (mri_deface). Harvard, v1.22, Available: https://surfer.nmr.mgh.harvard.edu/fswiki/AutomatedDefacingTools)

LCNI (Lewis Center for Neuroimaging). MRIConvert (mcverter), v2.1.0 build 440, Available: https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Peirce, JW. PsychoPy–psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2):8 – 13, 2007. Software Available: http://www.psychopy.org/

Python software is copyright © 2001-2015 Python Software Foundation. Web site: https://www.python.org

Pyo software is copyright © 2009-2015 Olivier Belanger. Web site: http://ajaxsoundstudio.com/software/pyo/.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: https://www.R-project.org/.

rgeos software is copyright © 2016 Bivand and Rundel. Web site: https://CRAN.R-project.org/package=rgeos

Rorden, C. dcm2niix, v1.0.20171215, Available: https://github.com/rordenlab/dcm2niix

spatstat software is copyright © 2016 Baddeley, Rubak, and Turner. Web site: https://CRAN.R-project.org/package=spatstat

sp software is copyright © 2016 Pebesma and Bivand. Web site: https://CRAN.R-project.org/package=sp

This is a vector tile service with labels for the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. Labels appear at scales greater than 1:10,000 and characterize stand height, stand canopy cover, stand map class, and stand impervious cover. This service is mean to be used in conjunction with the vector tile services of the polygon themselves (either the solid symbology service or the hollow symbology service). The key to the labels appears in the graphic below; the key to map class abbreviations can be found here. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

Context

I would like to write a quest scraper. A Tool that takes a look at an image of a Heroquest quest map and can derive all symbols with their positions correctly; turning the "dead" image once again into an editable quest file. On Heroscribe.org a great java-based tool for editing quest files can be downloaded. In ideal case, my tool can take an image and output the Heroscribe format.

That's a task for later. Today, we just want to do the recognition.

I took around 100 Maps from the ancient game Heroquest, cut them down to single square images and used them as training data set for a neural net. The incredible imbalance in the data set made it necessary that I made 100 more maps, to boost the underrepresented symbol appearances. All of the maps have been made in Heroscribe (downloadable at Heroscribe.org) and exported as png; like that they have the same size.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F9050fb998965fcf24ef4b76d4c9fe4d7%2F11-BastionofChaos_EU.png?generation=1570256920345210&alt=media" alt="EU format Heroquest map">

Now I have 13 thousand snippets of Heroquest Quest Maps, in three cut out factors (78, 42 and 34 pixel). In each sample, there can be one or more of the following things: Monsters, Furniture, Doors, and rooms. For each snippet, the position information is already preserved in the data set: It was taken during the cropping process. You know where you cut the image right now, so why not keeping that information right away?

In the easiest case, there is just one symbol in a square. In some cases there are two or three of them at the same time; like there can be one or more door, one monster, and the square itself is discolored because the room is a special room. So here we have do recognize several symbols at the same time.

The first (roughly half) of the dataset contains real data from real maps, in the second half I've made up data to fill gaps in the data coverage.

The Y-Labels

Y-Data is provided in an excel-formatted spreadsheet. One column is for single-square-items and furniture; four for doors and one for rooms. If there were too many items in one square, or sometimes when I was tired from labelling all the data, it could happen that I was putting a label in the wrong column or even put the wrong label. I guess that currently, around 0.5% of the data is mislabelled; except for the room symbol column; which is not at all well labeled.

I tried to train a resnet to recognize the Y-Data given and it was surprisingly difficult. The current best working solution has four convolutional layers and one dense layer; has nothing to do with the current state-of-the-art deep learning. The advantage is, it is trainable under an hour on any laptop; the disadvantage is does not yet always work as intended.

See some examples for the images and the difficulties: The "center pic" of a "table" symbol: It is difficult to recognize anything here.

https://i.imgur.com/yCP4pF9.png" alt="Table, small cutout">

And the same square in the "pic" cutout:

https://i.imgur.com/9a9scVN.png" alt="Table, big cutout">

"center pic" of a Treasure Chest: Sufficient to recognize it; easily!

https://i.imgur.com/KjX1QUV.png" alt="Treasure Chest, small cutout">

Big cutout of the same Treasure Chest: Distracting details in the surrounding.

https://i.imgur.com/OPBlWHV.png" alt="Treasure Chest, big cutout">

For each symbol, I also extracted the two main colors. There are maps in the EU format, which are completely black and white (see above picture). The other half of the maps is in US format: Monsters are green, furniture is dark red, traps and trapped furniture have a orange or turquoise background instead of white; Hero symbols are bright red. There is real information in those colors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F3900bd109f86618a48e619ec00ce892d%2F11-BastionofChaos_US.png?generation=1570257035192555&alt=media" alt="US format Heroquest map">

The symbols in the data set are black and white, all of them. The columns 'min_color' and 'max_color' preserve the color information. I planned to give it as an auxiliary input to the neural net, but didn't yet get round to do it. The color information can be distracting, too: In the US map format, sometimes otherwise normal furniture symbols are marked with trap colors when they thought about some special event for it.

Target acceptance rates

Those are quite easy images on one side. Noiseless, size-fixed, no skew or zoom coming from photography... I even bootstrapped my data set by using K-Means to bulk-label some images. Yes, K-Means. It is easy to classify this data beyond the 95% recognition. So what's the catch?

First of all, the number of classes. It's not a single-class recognition problem; in this data set we have around 100 class...

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3D Mapping And Modeling Market Size 2025-2029

The 3d mapping and modeling market size is forecast to increase by USD 35.78 billion, at a CAGR of 21.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing adoption in smart cities and urban planning projects. This trend is attributed to the ability of 3D mapping and modeling technologies to provide accurate and detailed visualizations of complex urban environments, enabling more efficient planning and management. Another key driver is the emergence of digital twin technology, which allows for real-time monitoring and simulation of physical assets in a digital environment. However, the market also faces challenges, most notably privacy and security concerns. With the increasing use of 3D mapping and modeling in various applications, there is a growing risk of data breaches and unauthorized access to sensitive information. As such, companies must prioritize robust security measures to protect customer data and maintain trust. Additionally, the high cost of implementing and maintaining these technologies remains a barrier to entry for some organizations. Despite these challenges, the market's potential for innovation and growth is substantial, with opportunities for companies to capitalize on emerging trends and navigate challenges effectively.

What will be the Size of the 3D Mapping And Modeling Market during the forecast period?

Request Free SampleThe market continues to evolve, driven by advancements in spatial data acquisition, project management, and navigation systems. BIM software, artificial intelligence (AI), and 3D visualization services are increasingly integrated into infrastructure management, real estate development, and cultural heritage preservation. Image recognition and 3D scanning are revolutionizing asset management and virtual reality (VR) applications. In precision agriculture, AI development and machine learning enable object detection and scene understanding, while data analysis and processing facilitate more efficient crop management. Autonomous vehicles and remote sensing rely on 3D modeling software and point cloud processing for accurate environmental monitoring. Additive manufacturing and 3D printing services are transforming industries, from construction to healthcare, with advancements in 3D modeling software, materials, and processing techniques. Urban planning benefits from AI-driven data analytics and 3D model optimization for smarter city design. Deep learning and computer vision are enhancing object tracking and data visualization services, while software development and spatial analysis are improving facility management and location-based services. The ongoing integration of these technologies is shaping a dynamic and innovative market landscape.

How is this 3D Mapping And Modeling Industry segmented?

The 3d mapping and modeling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Product Type3D modeling3D mappingComponentSoftwareServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Type Insights

The 3d modeling segment is estimated to witness significant growth during the forecast period.The 3D modeling market encompasses the creation of three-dimensional digital representations of objects, environments, and surfaces, finding extensive applications in industries such as architecture, gaming, film production, product design, and medical imaging. This technology's integration has revolutionized these sectors, fostering more precise and efficient design, planning, and analysis. Key technologies driving this market include computer-aided design (CAD) software, 3D scanning and rendering, simulation and animation tools, and geospatial data. CAD is a cornerstone technology in architecture, engineering, and manufacturing, enabling professionals to create intricate and accurate designs. 3D scanning and rendering technologies convert physical objects into digital models, crucial for industries where exact replicas are required. Artificial intelligence (AI) and machine learning algorithms are increasingly integrated into 3D modeling, enhancing object detection, computer vision, and data analysis capabilities. Virtual reality (VR) and augmented reality (AR) technologies are transforming 3D modeling by providing immersive experiences for design visualization, enabling better scene understanding and spatial analysis. Precision agriculture employs 3D modeling for terrain modeling and crop monitoring, while infrastructure management uses it for asset management and urban planning. 3D modeling is also instrumental in heritage preservation, environmental monitoring, and additive manufacturing. A