The Forest Service's Remote Sensing Applications Center (RSAC) is in Salt Lake City, Utah, co-located with the agency's Geospatial Service and Technology Center. Guided by national steering committees and field sponsors, RSAC provides national assistance to agency field units in applying the most advanced geospatial technology toward improved monitoring and mapping of natural resources. RSAC's principal goal is to develop and implement less costly ways for the Forest Service to obtain needed forest resource information. Resources in this dataset:Resource Title: GTAC External Products, Data and Services. File Name: Web Page, url: https://www.fs.usda.gov/about-agency/gtac These are examples of the work we are involved in. Contact us if you're interested in learning more. Data and Services: Forest Service Base Map Products, Insect and Disease Area Designations, National Land Cover Database, Tree Canopy Cover, Landscape Change Monitoring System, Terrestrial Ecological Unit Inventory (TEUI) and GTAC TEUI Toolkit, Orthomosaicking Historical Aerial Photography Scans

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

Chicago sites that offer free or affordable technology resources and services, like computers with Internet access, Wi-Fi hotspots and technology training. Call or visit the organization's website before going to the location. For more information, visit http://locations.weconnectchicago.org/.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Geospatial Multimodal AI Platform Market Outlook

According to our latest research, the global Geospatial Multimodal AI Platform market size in 2024 stands at USD 3.8 billion, reflecting robust momentum across industries integrating AI-driven spatial analytics. The market is expected to reach USD 17.2 billion by 2033, progressing at a strong CAGR of 18.2% during the forecast period. This remarkable growth is primarily propelled by the surging demand for advanced geospatial intelligence, the proliferation of sensor-enabled IoT devices, and the convergence of multimodal data sources to power next-generation applications in urban planning, transportation, defense, and environmental monitoring.

The primary growth driver for the Geospatial Multimodal AI Platform market is the rapid technological advancement in artificial intelligence, particularly in machine learning and deep learning algorithms. These advancements are enabling platforms to process, analyze, and interpret vast volumes of geospatial data from multiple modalities—such as text, images, audio, video, and sensor data—delivering actionable insights with unprecedented accuracy and speed. This capability is especially valuable for smart city initiatives, where real-time analysis of multimodal data can optimize urban mobility, infrastructure management, and public safety. The integration of AI with geospatial analytics is thus transforming traditional GIS solutions into intelligent, predictive platforms that support data-driven decision-making across sectors.

Another significant factor fueling market expansion is the exponential growth of IoT devices and remote sensing technologies. The proliferation of sensors, drones, satellites, and connected devices is generating massive streams of spatial data, which, when combined with AI, unlock new possibilities for monitoring, forecasting, and automating complex processes. For example, in agriculture, multimodal AI platforms can synthesize satellite imagery, weather data, and sensor inputs to optimize crop yields and resource utilization. Similarly, in disaster management, these platforms enable real-time situational awareness by integrating video feeds, social media text, and sensor data, thereby enhancing emergency response and resilience.

Geospatial Analytics AI is becoming increasingly pivotal in the evolution of geospatial multimodal AI platforms. By leveraging advanced AI techniques, these platforms can process and analyze complex geospatial datasets with greater precision and speed. This capability is essential for industries that rely on real-time data interpretation, such as urban planning and disaster management. The integration of AI into geospatial analytics not only enhances data accuracy but also enables predictive modeling, which is crucial for proactive decision-making. As AI technologies continue to evolve, their application in geospatial analytics is expected to expand, offering new opportunities for innovation and efficiency across various sectors.

Furthermore, the increasing adoption of cloud-based deployment models is accelerating the accessibility and scalability of geospatial multimodal AI platforms. Cloud infrastructure allows organizations to process and store large datasets cost-effectively, while also facilitating collaborative analytics and integration with other enterprise systems. This trend is particularly evident among government agencies and large enterprises seeking to modernize their spatial intelligence capabilities without the constraints of on-premises hardware. Additionally, the growing emphasis on sustainability and environmental monitoring is driving demand for platforms that can analyze diverse data sources to track climate change, manage natural resources, and mitigate environmental risks.

From a regional perspective, North America currently leads the market, accounting for the largest share in 2024, driven by significant investments in smart infrastructure, defense modernization, and advanced research. However, Asia Pacific is emerging as the fastest-growing region, with governments and private sectors in countries like China, Japan, and India heavily investing in geospatial technologies for urbanization and disaster management. Europe is also witnessing substantial growth, fueled by initiatives in environmental monitoring and transportation. Overall, the

The 3DNTM State Planning Guide outlines the critical role of integrating elevation and hydrography data to achieve a seamless geospatial foundation for applications such as disaster response, infrastructure development, and environmental protection. By focusing on the USGS's 3DEP and 3DHP programs, the guide emphasizes the importance of state partnerships in creating high-resolution, accurate, and consistent datasets for the 3D National Topography Model. It provides an in-depth overview of the value of these datasets, including improved modeling, data consistency, and cost savings through coordinated acquisition strategies.The guide offers practical steps for developing data acquisition and management plans, including forming project teams, identifying areas of interest (AOIs), and engaging stakeholders across federal, state, and local levels. It also addresses challenges such as funding gaps, cross-border coordination, and the integration of hydrography and elevation data into a single framework. States are encouraged to align their efforts with USGS specifications while tailoring their plans to meet local needs and priorities.Additionally, the guide provides templates and examples to facilitate plan development and promotion, ensuring stakeholders and decision-makers understand the benefits and cost-effectiveness of the initiative. By fostering collaboration and leveraging modern geospatial technologies, the guide aims to help states achieve sustainable data management and support broader national goals for an integrated 3D topographic model.

Geospatial Analytics AI Market Outlook

According to our latest research, the global geospatial analytics AI market size reached USD 15.4 billion in 2024, driven by rapid technological advancements and increasing demand for location-based insights across industries. The market is expanding at a robust CAGR of 13.2% and is projected to attain USD 41.6 billion by 2033. This impressive growth is primarily fueled by the integration of artificial intelligence with geospatial data to deliver actionable intelligence for decision-making in sectors such as urban planning, disaster management, and transportation.

One of the key growth factors for the geospatial analytics AI market is the exponential increase in the volume and variety of geospatial data generated by satellites, drones, IoT devices, and mobile applications. Organizations are leveraging this data to gain real-time insights into spatial patterns, trends, and anomalies. The rise of smart cities and the need for efficient infrastructure management have significantly accelerated the adoption of geospatial analytics AI, as city planners and municipal authorities seek to optimize resource allocation, traffic flows, and emergency response strategies. Additionally, the integration of AI algorithms with geospatial data is enabling more accurate predictive modeling, which is essential for disaster preparedness and mitigation.

Another major driver is the growing application of geospatial analytics AI in environmental monitoring and sustainable resource management. As climate change and environmental degradation become increasingly pressing global issues, governments and organizations are investing heavily in advanced analytics solutions to monitor deforestation, track wildlife, assess water quality, and predict natural disasters. The ability of AI-powered geospatial tools to process massive datasets and generate timely, actionable insights is proving invaluable for environmental agencies and NGOs. Furthermore, the agricultural sector is adopting geospatial AI for precision farming, crop monitoring, and yield prediction, resulting in enhanced productivity and reduced operational costs.

The proliferation of cloud computing and advances in hardware capabilities are also propelling the market forward. Cloud-based deployment models are making geospatial analytics AI solutions more accessible and scalable, allowing organizations of all sizes to benefit from sophisticated spatial analysis without the need for extensive on-premises infrastructure. Enhanced hardware, including high-resolution sensors and edge computing devices, is facilitating the collection and processing of geospatial data in real time. These technological advancements are lowering barriers to entry and enabling a broader range of industries to harness the power of geospatial analytics AI for competitive advantage and operational efficiency.

Regionally, North America continues to dominate the geospatial analytics AI market, accounting for over 38% of the global revenue in 2024, owing to the presence of leading technology companies and robust investments in R&D. However, the Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 15.7% through 2033, driven by rapid urbanization, government initiatives for smart city development, and increasing adoption of AI-driven technologies across various sectors. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by digital transformation initiatives and infrastructural modernization.

Component Analysis

The geospatial analytics AI market is segmented by component into software, hardware, and services. The software segment holds the largest share, accounting for nearly 52% of the total market revenue in 2024. This dominance is attributed to the proliferation of advanced analytics platforms, mapping tools, and AI-powered visualization solutions that enable organizations to derive actionable insights from complex geospatial datasets. Softw

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structure ...

This repository is the dataset of the related paper "Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques". The data presented here are collected and gathered together from several separate locations. All the probable original sources of the dataset are open-source or free to distribute licensed. The dataset has the following items: 1. Road network of Dhaka city. 2. Bus Route network of Dhaka city. 3. Future metro Route network of Dhaka city. 4. All the bus stands in Bangladesh. 5. All planned metro station in Dhaka city. 6. The output of some sample random two points shortest or cheapest path from the related paper.

This collection contains Environmental Response Management Application (ERMA) GIS layers used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP), including outputs from Synthetic Aperture Radar (SAR) imagery, helicopter flights surveys (observations) of marine mammal and turtles, Mississippi Canyon 252 wellhead location, wellhead buffers, and supporting bathymetric contour data, infrared and photographic images from EPA's airborne spectral photometric environmental collection technology (ASPECT) with geospatial, chemical and radiological information, boom-related response observations, nearshore tissue and sediment samples, forensic and Total Polycyclic Aromatic Hydrocarbon (TPAH) results, stranded oil forensic classification data, and other types of chemistry data, Submerged Aquatic Vegetation (SAV) classifications, seabed sampling and transect data, sample locations for workplan cruises, deep-sea area injury toxicity results and total polycyclic aromatic hydrocarbon (TPAH) results, habitat injury zones, footprint impacts on mesophotic reef resources and other types of benthic habitat data, overflight imagery of the flight path for the NOAA King Air flights taken in October of 2010 and contains post-oiling images collection in support of Natural Resource Damage Assessment (NRDA) marsh monitoring, turtle survey overflight observations, loggerhead sea turtle density grids, sea turtle capture observations and transect analysis, sea turtle strandings, as well as probabilities of oiling and other related datasets, trawl locations, Southeast Area Monitoring and Assessment Program (SEAMAP) plankton trawls, workplan cruise samples, and other related data, delineation of the areas impacted with additional fresh water due to the opening of the diversions in 2011 as part of the Deepwater Horizon oil spill response, surface shoreline oiling characteristics as observed by field surveys performed by Shoreline Cleanup Assessment Techniques (SCAT) teams, marine mammal surveys, observations, telemetry and abundance data including Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, and other related Marine Mammal field observations and surveys, presence and spatial distribution of synthetic-based mud (SBM) in deep-sea sediments around the Macondo well, surface sediment, residual kriging, and other oiling analytical data, oyster recruitment and abundance sampling results, estimates of subtidal habitat, estimates of oyster resource, seafloor substrate mapping layers, percent cover, nearshore and subtidal quadrat abundance data, and other related datasets, shoreline exposure model for beach and marsh oiling, wave exposure, habitat classifications, wetland monitoring datasets, and related shoreline datasets, compilation of all the individual Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons, a variety of cumulative oiling datasets including the Texture Classifying Neural Network Algorithm (TCNNA) from Synthetic Aperture Radar (SAR) satellite polygon layers, burn locations, dispersant operation datasets including estimations of where aerial dispersants were applied via aerial flight paths, dispersant airport locations, daily flight tracks, and vessel dispersant tracks, as well as locations of subsurface dispersant data, marine mammal surveys, observations, telemetry and abundance data collected including synoptic surveys, helicopter surveys, Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, other related marine mammal field observations and surveys, and sea turtle data, and other data related to the Deepwater Horizon oil spill in the Northern Gulf of Mexico. Some of these data were collected during the response to the Mississippi Canyon 252 Deepwater Horizon oil spill in the Northern Gulf of Mexico.

Geospatial Data Clean-Room Market Outlook

According to our latest research, the global geospatial data clean-room market size in 2024 stands at USD 1.4 billion, driven by the surging need for secure and collaborative geospatial data environments across multiple industries. The market is projected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching a forecasted market size of USD 6.3 billion by 2033. This remarkable growth is fueled by increasing concerns over data privacy, the proliferation of location-based services, and the mounting regulatory requirements for secure data collaboration and analytics.

One of the primary growth factors for the geospatial data clean-room market is the exponential increase in the volume and variety of geospatial data generated by IoT devices, drones, satellites, and mobile applications. Organizations across sectors such as transportation, urban planning, and logistics are leveraging this data to derive actionable insights. However, the sensitive nature of location data and the need to comply with global privacy regulations such as GDPR and CCPA necessitate secure environments for data aggregation and analysis. Geospatial data clean-rooms provide a controlled and compliant infrastructure for multiple parties to collaborate on sensitive datasets without exposing raw data, thus unlocking value while minimizing risk.

Another significant driver is the digital transformation initiatives undertaken by governments and enterprises worldwide. As smart city projects and digital twin technologies gain traction, the demand for secure, scalable, and interoperable platforms to process and analyze geospatial data is surging. Clean-room solutions offer advanced capabilities such as federated analytics, privacy-preserving computation, and policy-driven data governance. These features are particularly crucial for sectors like healthcare, BFSI, and defense, where the confidentiality of location data is paramount. Additionally, the integration of artificial intelligence and machine learning algorithms within clean-room platforms is enhancing the accuracy and utility of geospatial analytics, further accelerating market adoption.

The geospatial data clean-room market is also benefiting from the evolving landscape of data monetization and data sharing partnerships. Companies are increasingly seeking ways to collaborate with external partners, suppliers, or governmental organizations to unlock new revenue streams and improve operational efficiency. Clean-rooms act as a trusted intermediary, enabling secure, permissioned access to geospatial datasets while preserving data sovereignty and intellectual property rights. This collaborative approach is fostering innovation across industries such as retail, energy, and utilities, where location intelligence can drive targeted marketing, resource optimization, and risk management.

From a regional perspective, North America currently dominates the geospatial data clean-room market, accounting for the largest revenue share, followed by Europe and the Asia Pacific. The presence of leading technology providers, stringent regulatory frameworks, and early adoption of advanced analytics solutions are key factors contributing to North America's leadership. Meanwhile, the Asia Pacific region is expected to witness the fastest growth over the forecast period, propelled by rapid urbanization, government investments in smart infrastructure, and the burgeoning digital economy. Europe remains a critical market due to its strong emphasis on data privacy and cross-border data collaboration initiatives.

Component Analysis

The component segment of the geospatial data clean-room market is categorized into software, services, and hardware. Software solutions form the backbone of clean-room platforms, offering functionalities such as data ingestion, anonymization, access control, and analytics. The software segment holds the largest market share, primarily due t

Per the USFS website:This geospatial dataset was created by the USFS Forest Inventory and Analysis (FIA) program and the Geospatial Technology and Applications Center (GTAC) to show the extent, distribution, and forest type composition of the nation’s forests.The dataset was created by modeling forest type from FIA plot data as a function of more than one hundred geospatially continuous predictor layers.This process results in a view of forest type distribution in greater detail than is possible with the FIA plot data alone.Nearly one-half million FIA sample plots nationwide were used to develop these models.Among the predictor layers used were digital elevation models (DEM) and DEM derivatives; Moderate Resolution Spectroradiometer (MODIS) multi-date composites, vegetation indices and vegetation continuous fields; class summaries from the 1992 National Land Cover Dataset (NLCD); various ecologic zones; and summarized PRISM climate data.Modeling was performed using a data mining package, Cubist/See5TM, which was loosely coupled with Leica Geosystems ImagineTM image processing software.https://data.fs.usda.gov/geodata/rastergateway/forest_type/index.php

Digital Terrain Database Market Outlook

According to our latest research, the global Digital Terrain Database market size in 2024 stands at USD 2.54 billion, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 9.2% from 2025 to 2033, reaching a forecasted value of USD 5.67 billion by 2033. This growth is primarily driven by the increasing adoption of advanced geospatial technologies across various sectors, including defense, civil engineering, and urban planning, as organizations seek to leverage high-precision terrain data for enhanced decision-making and operational efficiency.

The Digital Terrain Database market is experiencing significant momentum due to the rising demand for accurate topographical information in mission-critical applications. The integration of digital terrain data in aerospace and defense operations, such as flight simulation, mission planning, and navigation, is a key growth factor. These sectors require precise elevation models to ensure safety, optimize routes, and enhance situational awareness. Furthermore, the proliferation of unmanned aerial vehicles (UAVs) and autonomous systems has intensified the need for real-time, high-resolution terrain data, propelling the adoption of sophisticated digital terrain databases. As defense budgets continue to prioritize geospatial intelligence, the market is poised for sustained expansion.

Another pivotal growth driver for the Digital Terrain Database market is the rapid urbanization and infrastructure development observed globally. Civil engineering and urban planning sectors are increasingly relying on detailed terrain models for designing resilient infrastructure, mitigating natural hazards, and optimizing land use. The surge in smart city initiatives, particularly in emerging economies, necessitates the deployment of advanced geospatial solutions. Digital terrain databases enable planners and engineers to simulate various scenarios, assess environmental impacts, and streamline construction processes. The integration of terrain data with Building Information Modeling (BIM) and Geographic Information Systems (GIS) further amplifies its value, fostering market growth across public and private sectors.

Technological advancements and the growing accessibility of cloud-based geospatial solutions are also catalyzing market expansion. Cloud deployment models are democratizing access to high-quality terrain data, enabling organizations of all sizes to leverage these resources without significant upfront investments in hardware or infrastructure. The evolution of data acquisition methods, such as LiDAR, satellite imagery, and photogrammetry, has enhanced the accuracy and granularity of digital terrain databases. This, coupled with the increasing emphasis on environmental monitoring, disaster management, and agricultural optimization, is broadening the application landscape and stimulating demand for digital terrain databases across diverse verticals.

From a regional perspective, North America currently dominates the Digital Terrain Database market, attributed to the presence of leading technology providers, robust defense spending, and widespread adoption of geospatial technologies. Europe follows closely, driven by stringent regulatory frameworks and substantial investments in infrastructure modernization. The Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, fueled by rapid urbanization, government-led smart city projects, and expanding applications in agriculture and environmental monitoring. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit from a lower base, as digital transformation initiatives gain traction across these regions.

Component Analysis

The Digital Terrain Database market by component is segmented into Software, Hardware, and Services, each playing a vital role in the overall ecosystem. Software solutions form the backbone

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from https://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, https://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.Data Download: https://ky.box.com/v/kymartian-us-national-grid-1km

A geospatial interface will be developed using ArcIMS software. The interface will provide a means of accessing information stored in the SOFIA database and the SOFIA data exchange web site through a geospatial query. The spatial data will be served using the ArcSDE software, which provides a mechanism for storing spatial data in a relational database. A spatial database will be developed from existing data sets, including national USGS data sets, the Florida Geographic Digital Library, and other available data sets. Additional data sets will be developed from the published data sets available from PBS and other projects.

The South Florida restoration effort requires multidisciplinary information relating to present and historical conditions for use in responsible decision-making. The South Florida Information Access (SOFIA) database is the cornerstone of information management for the South Florida place-based science program. Currently, the SOFIA web site and database have a minimal geospatial interface which relies on the Geo-Data Explorer (GeoDE) system developed by the USGS Energy Resources Program in Reston. A geospatial interface using currently available commercial software (ArcIMS) is needed to develop a more easily maintained and user-friendly interface. Developing an interface that is directly connected to the SOFIA website and database will provide a more stable long term solution to providing a geospatial interface.

AI for 3D GIS Analytics Market Outlook

According to our latest research, the AI for 3D GIS Analytics market size reached USD 2.38 billion in 2024, reflecting robust adoption across various industries. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, forecasting a value of USD 12.3 billion by 2033. The primary growth driver for this market is the increasing demand for advanced spatial analytics solutions that leverage artificial intelligence to enable more precise, real-time, and actionable insights from complex geospatial data sets.

The rapid urbanization across the globe is significantly fueling the adoption of AI for 3D GIS Analytics. City planners and government agencies are increasingly relying on these advanced systems to model urban growth, optimize land use, and manage infrastructure development efficiently. The integration of AI with 3D GIS enables the processing of large-scale geospatial data, automating the analysis of urban expansion, transportation networks, and public utilities. This not only improves the accuracy of planning but also reduces the time and resources required for manual data interpretation. As urban populations swell, the need for smarter, data-driven city management solutions is propelling the growth of this market.

Another major growth factor is the rising emphasis on environmental monitoring and disaster management. Governments and organizations are leveraging AI for 3D GIS Analytics to assess environmental changes, predict natural disasters, and respond more effectively to emergencies. AI-powered 3D GIS platforms can analyze satellite imagery, sensor data, and historical records to identify patterns and predict potential risks such as floods, landslides, or wildfires. This proactive approach not only saves lives but also minimizes economic losses, making these solutions indispensable for both public and private sector stakeholders. The growing frequency of extreme weather events and environmental hazards is thus accelerating the adoption of AI-driven 3D GIS analytics worldwide.

Technological advancements in cloud computing and the proliferation of IoT devices have also played a crucial role in the expansion of the AI for 3D GIS Analytics market. The cloud-based deployment of 3D GIS solutions enables organizations to access and process vast geospatial datasets without the need for significant on-premises infrastructure investment. Meanwhile, IoT sensors continuously feed real-time data into these systems, enhancing the granularity and accuracy of spatial analysis. The convergence of AI, cloud, and IoT technologies is fostering a new era of intelligent geospatial analytics, enabling industries such as utilities, transportation, and real estate to optimize operations, reduce costs, and enhance service delivery.

Regionally, North America holds the largest share in the AI for 3D GIS Analytics market due to the early adoption of advanced technologies and substantial investments in smart city projects. Europe follows closely, driven by stringent regulations on environmental monitoring and urban planning. The Asia Pacific region is expected to witness the fastest growth, propelled by rapid urbanization, infrastructure development, and increasing government initiatives to harness AI for spatial analytics. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing awareness and investments in geospatial intelligence solutions.

Component Analysis

The AI for 3D GIS Analytics market by component is segmented into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment dominates the market, accounting for the largest revenue share in 2024. This dominance is attributed to the continuous innovations in AI algorithms and 3D visualization tools that enhance the capability of GIS platforms to process, analyze, and visualize complex spatial data. Leading software providers are integrating machine learning, deep learning, and computer vision technologies to automate feature extraction, anomaly detection,

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

Geographic Information System (GIS) is a system that captures, stores, manipulates, analyzes, manages, and presents spatial or geographical data. As this technological environment has been created to deal with space problems, it is perfectly adaptable to solve these type of issues in the context of vertebrate comparative morphology. The pectoral and pelvic girdles are key structures that relate the axial skeleton with the limbs in tetrapods. Owed to their importance in locomotion, the morphology, development, and morphogenesis of these structures have been widely studied. The complexity of the structures and tissues implied in the development of the girdles make quantitative approaches extremely difficult. The use of GIS technology provides a visual interpretation of the histological data, a general quantitative assessment of the processes taking place during the ontogeny of any structure, and would allow collecting information about the changes in the surface occupied by the different tissues across the ontogenetic processes of any vertebrate taxa. GIS technology applied to map morphological structures would be a main contribution to the construction of the vertebrate ontologies, as it would facilitate the identification and location of the structures. GIS technology would allow also us to construct a shared database of histological quantitative changes across the ontogeny in any vertebrate. The main objective of this study is to use GIS technology for spatial analysis of histological samples such as these of the pelvic girdle using histological cuts of anurans and chicken, allowing thus to construct a morphoscape, analogous to a landscape. This is the first attempt to apply GIS tools to ontogenetic series to infer biological properties of the spatial analysis in the context of comparative biology. More frequent use of this technology would contribute to obtaining more profitable and biologically informative results.

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.