These geospatial data and their accompanying report outline many areas of coal in the United States beneath more than 3,000 ft of overburden. Based on depth, these areas may be targets for injection and storage of supercritical carbon dioxide. Additional areas where coal exists beneath more than 1,000 ft of overburden are also outlined; these may be targets for geologic storage of carbon dioxide in conjunction with enhanced coalbed methane production. These areas of deep coal were compiled as polygons into a shapefile for use in a geographic information system (GIS). The coal-bearing formation names, coal basin or field names, geographic provinces, coal ranks, coal geologic ages, and estimated individual coalbed thicknesses (if known) of the coal-bearing formations were included. An additional point shapefile, coal_co2_projects.shp, contains the locations of pilot projects for carbon dioxide injection into coalbeds. This report is not a comprehensive study of deep coal in the United States. Some areas of deep coal were excluded based on geologic or data-quality criteria, while others may be absent from the literature and still others may have been overlooked by the authors.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or _location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason, C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

The storage assessment unit (SAU) is the fundamental unit used in the National Assessment of Geologic Carbon Dioxide Storage Resources project for the assessment of geologic CO2 storage resources. The SAU is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the assessment interval. Individual SAUs are defined on the basis of common geologic and hydrologic characteristics. The resource that is assessed is the mass of CO2 that can be stored in the technically accessible pore volume of a storage formation. The technically accessible storage resource is one that may be available using present-day geological and engineering knowledge and technology for CO2 injection into geologic formations and therefore is not a total in-place resource estimate. The SAU boundary is defined geologically as the limits of the geologic elements that define the SAU, such as limits of reservoir rock, geologic structures, depth, and seal lithologies. The only exceptions to this are SAUs that border the international, or Federal-State water boundary. In these cases, the international or Federal-State water boundary forms part of the SAU boundary. Drilling-density cell maps show the number of wells that have been drilled into the SAU. Each 1-square-mile cell has a count for the number of unique well boreholes drilled into the SAU. For a given sedimentary basin, the National Assessment of Geologic Carbon Dioxide Storage Resources project identifies SAUs containing the potential for storage and sequestration of carbon dioxide. Proprietary well header data from IHS ENERDEQ through 2010 were queried to determine which wells were drilled into specific SAUs. The coordinates of wells are proprietary and cannot be released; however, counts of the number of wells per square mile are presented in the well drilling density data layer.

Locating, summarizing and presenting large collections of Earth science data in a highly distributed and networked environment is critical in NASA's mission for Earth Sciences. Technologies supporting management, storage, search and retrieval of very large, distributed, geo-spatial earth science data volumes are urgently needed to cope with the impending data survivability crisis. For instance, the EOSDIS archive data growth rate is currently about 1 petabyte/year. NRC's Committee on Coping with Increasing Demands on Government Data Centers recently made a series of recommendations on which emerging technologies can help data centers meet user needs and build and maintain the long-term record of environmental change. In this proposal, we propose to design, develop and prototype Extrieva - a low cost scalable Archive Storage Management System innovation, which embraces several of the NRC technology recommendations. In particular, Extrieva is a disk-based solution as assessed by NRC to be now competitive with tape for long-term, archival-class storage. Moreover, with its self-management and automation features implemented over commodity Linux clusters using distributed indexing and load balancing algorithms, Extrieva offers a low cost scalable solution with unprecedented ease of management addressing the needs of EOSDIS' diverse global users base.

The storage assessment unit (SAU) is the fundamental unit used in the National Assessment of Geologic Carbon Dioxide Storage Resources project for the assessment of geologic CO2 storage resources. The SAU is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the assessment interval. Individual SAUs are defined on the basis of common geologic and hydrologic characteristics. The resource that is assessed is the mass of CO2 that can be stored in the technically accessible pore volume of a storage formation. The technically accessible storage resource is one that may be available using present-day geological and engineering knowledge and technology for CO2 injection into geologic formations and therefore is not a total in-place resource estimate. The SAU boundary is defined geologically as the limits of the geologic elements that define the SAU, such as limits of reservoir rock, geologic structures, depth, and seal lithologies. The only exceptions to this are SAUs that border an international, or Federal-State water boundary. In these cases, the international or Federal-State water boundary forms part of the SAU boundary. Drilling-density cell maps show the number of wells that have been drilled into the SAU. Each 1-square-mile cell has a count for the number of unique well boreholes drilled into the SAU. For a given sedimentary basin, the National Assessment of Geologic Carbon Dioxide Storage Resources project identifies SAUs containing the potential for storage and sequestration of carbon dioxide. Proprietary well header data from IHS ENERDEQ through 2010 were queried to determine which wells were drilled into specific SAUs. The coordinates of wells are proprietary and cannot be released; however, counts of the number of wells per square mile are presented in the well drilling density data layer.

TerraMetrics, Inc., proposes a Phase II R/R&D program to implement the TerraBlocks^TM Server architecture that provides geospatial data authoring, storage and delivery capabilities. TerraBlocks enables successful deployment, display and visual interaction of diverse, massive, multi-dimensional science datasets within popular web-based geospatial platforms like Google Earth and NASA World Wind.

TerraBlocks is a wavelet-encoded data storage technology and server architecture for NASA science data deployment into widely available web-based geospatial applications. The TerraBlocks approach provides dynamic geospatial data services with an emphasis on 1) server and data storage efficiency, 2) maintaining server-to-client science data integrity and 3) offering client-specific delivery of large Earth science geospatial datasets. The TerraBlocks approach bridges the gap between inflexible, but fast, pre-computed tile delivery approaches and highly flexible, but slower, map services approaches.

The pursued technology exploits the use of a network-friendly, wavelet-compressed data format and server architecture that extracts and delivers appropriately-sized blocks of multi-resolution geospatial data to geospatial client applications on demand and in interactive real time.

The Phase II project objective is to provide a complete and fully-functional prototype TerraBlocks data authoring and server software package delivery to NASA and simultaneously set the stage for commercial availability. The Phase III objective is to commercially deploy the TerraBlocks technology, with the collaboration of our commercial and government partners, to provide the enabling basis for widely available third-party data authoring and web-based geospatial application data services.

This is an export of the data archived from the 2024 National Incident Feature Service (NIFS).Sensitive fields and features have been removed.Each edit to a feature is captured in the Archive. The GDB_FROM and GDB_TO fields show the date range that the feature existed in the National Incident Feature Service.The National Incident Feature Service is based on the National Wildfire Coordinating Group (NWCG) data standard for Wildland Fire Event. The Wildland Fire Event data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident based data management tools, minimum standards for data exchange, and to assist users in meeting the NWCG Standards for Geospatial Operations (PMS 936).

This online map displays California’s active Underground Gas Storage (UGS) projects and wells associated to UGS projects. Project data and well data are provided by CalGEM’s Well Statewide Tracking and Reporting System (WellSTAR). Wells are displayed by well type and the association to a UGS project.CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).WellSTAR homepageUpdate Frequency: As Needed

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

This data release provides shapefiles that represent storage assessment units (SAUs) and drilling-density cells in the Anadarko and Southern Oklahoma Basins of Colorado, Kansas, Oklahoma, and Texas in the United States. The SAU is the fundamental unit used in the National Assessment of Geologic Carbon Dioxide Storage Resources project for the assessment of geologic CO2 storage resources. The SAU is a geographic polygon interpreted, defined, and mapped by the geologist responsible for the assessment interval. Individual SAUs are defined on the basis of common geologic and hydrologic characteristics. The resource that is assessed is the mass of CO2 that can be stored in the technically accessible pore volume of a storage formation. The technically accessible storage resource is one that may be available using present-day geological and engineering knowledge and technology for CO2 injection into geologic formations and therefore is not a total in-place resource estimate. The SAU polygon is defined geologically as the limits of the geologic elements that define the SAU, such as limits of reservoir rock, geologic structures, depth, and seal lithologies. The only exceptions to this are SAUs that border the international, or Federal-State water boundary. In these cases, the international or Federal-State water boundary forms part of the SAU boundary. Drilling-density cell maps show the number of wells that have been drilled into the SAU. Each 1-square-mile cell has a count for the number of unique well boreholes drilled into the SAU. For a given sedimentary basin, the National Assessment of Geologic Carbon Dioxide Storage Resources project identifies SAUs containing the potential for storage and sequestration of carbon dioxide. Proprietary well header data from IHS ENERDEQ through 2010 were queried to determine which wells were drilled into specific SAUs. The coordinates of wells are proprietary and cannot be released; however, counts of the number of wells per square mile are presented in the well drilling density data layer.

The 10-hour, closed-loop PSH geospatial data include paired reservoir volume (gigaliter), capacity (megawatts), distance between reservoirs (kilometer), head height (meter), transmission spurline distance (kilometer), transmission spurline costs ($), and total cost ($/kilowatt).

“Wet storage” means the temporary storage of shellstock from shellfish growing areas in the approved classification or in the open status of the conditionally approved classification. Shellstock can be stored in containers or floats in natural bodies of water or in tanks containing natural or synthetic seawater.For more information, see Chapter 15 of DMR rule.

TerraMetrics, Inc., proposes an SBIR Phase I R/R&D program to investigate and develop a key web services architecture that provides data processing, storage and delivery capabilities and enables successful deployment, display and visual interaction of diverse, massive, multi-dimensional science datasets within popular web-based geospatial platforms like Google Earth, Google Maps, NASA's World Wind and others. The proposed innovation exploits the use of a wired and wireless, network-friendly, wavelet-compressed data format and server architecture that extracts and delivers appropriately-sized blocks of multi-resolution geospatial data to client applications on demand and in real time. The resulting format and architecture intelligently delivers client-required data from a server, or multiple distributed servers, to a wide range of networked client applications that can build a composite, user-interactive 3D visualization of fused, disparate, geospatial datasets. The proposed innovation provides a highly scalable approach to data storage and management while offering geospatial data services to client science applications and a wide range of client and connection types from broadband-connected desktop computers to wireless cell phones. TerraMetrics offers to research the feasibility of the proposed innovation and demonstrate it within the context of a live, server-supported, Google Earth-compatible client application with high-density, multi-dimensional NASA science data.

Geospatial Analytics Market Outlook

In 2023, the global geospatial analytics market size was valued at approximately USD 55 billion and is projected to reach around USD 165 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The market is driven by technological advancements and the increasing need for geospatial data across various industries.

One of the key growth factors of the geospatial analytics market is the rapid advancement in geospatial technologies such as Geographic Information Systems (GIS), remote sensing, and global positioning systems (GPS). These technologies have significantly enhanced the accuracy and efficiency of data collection, analysis, and interpretation. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) algorithms with geospatial analytics has further augmented its capabilities, making it an indispensable tool for decision-making across diverse sectors.

Another significant driver of the geospatial analytics market is the growing adoption of location-based services and real-time data analysis. With the proliferation of smartphones and IoT devices, there is an increasing demand for applications that provide real-time location data. This has led to a surge in the use of geospatial analytics in urban planning, transportation and logistics, and disaster management. Companies and governments are leveraging geospatial data to optimize routes, manage resources efficiently, and respond swiftly to emergencies.

Furthermore, the rising awareness about climate change and environmental sustainability has propelled the use of geospatial analytics in climate change adaptation and environmental monitoring. Governments and organizations are increasingly relying on geospatial data to understand environmental changes, assess risks, and devise strategies for climate resilience. This trend is particularly significant in regions prone to natural disasters, where timely and accurate geospatial data can save lives and minimize damages.

From a regional perspective, North America holds a significant share of the geospatial analytics market, driven by the presence of major technology companies and extensive government initiatives focused on smart city development and environmental conservation. Europe follows closely, with substantial investments in geospatial technologies for urban planning and infrastructure development. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, industrialization, and government initiatives to enhance geospatial infrastructure.

Component Analysis

The geospatial analytics market is segmented into three main components: software, hardware, and services. Each of these components plays a pivotal role in the functioning and advancement of geospatial analytics. Starting with software, which encompasses a wide array of applications such as Geographic Information Systems (GIS), remote sensing software, and enterprise geospatial solutions. GIS software, in particular, is integral to the collection, storage, analysis, and visualization of geospatial data, enabling organizations to make informed decisions based on spatial patterns and relationships.

Hardware components in the geospatial analytics market include devices and equipment used for data collection and processing, such as GPS devices, drones, LiDAR sensors, and remote sensing satellites. These hardware components are essential for acquiring high-resolution geospatial data from various sources, providing a comprehensive view of geographical areas. The evolution of drone technology and advancements in satellite imaging have significantly enhanced the capability to capture accurate and detailed geospatial information, driving the demand for advanced hardware solutions.

Services in the geospatial analytics market encompass a range of offerings, including consulting, integration, maintenance, and support services. These services are crucial for the successful implementation and operation of geospatial analytics solutions. Consulting services help organizations identify the most suitable geospatial technologies and strategies to meet their specific needs. Integration services ensure seamless deployment of geospatial solutions within existing IT infrastructures, while maintenance and support services provide ongoing technical assistance and updates to keep the systems running smoothly.

The interplay between software, hardware, and services is critical for the effective utilization

Geospatial data about Round Rock, Texas Water Storage Tanks. Export to CAD, GIS, PDF, CSV and access via API.

Petroleum Storage Tanks (PST) and Underground Storage Tanks (UST) regulated by the TCEQ

The Geospatial Data Fusion market is experiencing robust growth, driven by increasing demand for precise location intelligence across diverse sectors. The market, valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The proliferation of Earth observation technologies, including satellite imagery and sensor data, provides a massive influx of raw data, necessitating sophisticated fusion techniques for actionable insights. Simultaneously, advancements in artificial intelligence (AI), particularly in computer vision and machine learning, are enhancing the accuracy and speed of data processing and analysis. The military and security sectors are significant contributors to market growth, utilizing geospatial data fusion for strategic planning, threat assessment, and real-time situational awareness. Furthermore, the rising adoption of cloud-based solutions (SaaS and PaaS) is streamlining data access, storage, and processing, further boosting market adoption. The market is segmented by application (Earth Observation and Space Applications, Computer Vision, Military, Security, Other) and deployment type (SaaS, PaaS), with SaaS currently dominating due to its accessibility and scalability. However, the market also faces some challenges. The high cost of data acquisition and processing can be a barrier to entry for smaller organizations. Data integration complexities, varying data formats, and ensuring data security are also crucial considerations. Despite these constraints, the market’s growth trajectory is expected to remain positive, propelled by continuous technological advancements, the increasing availability of geospatial data, and the growing need for precise location-based insights across various industries, ranging from urban planning and environmental monitoring to precision agriculture and disaster response. The competitive landscape features established players like Esri and emerging innovative companies like Geo Owl and Magellium, fostering healthy competition and driving innovation within the market.