Onboard Database Management for Satellites Market Outlook

As per our latest research, the global onboard database management for satellites market size in 2024 stands at USD 1.42 billion, reflecting robust demand across commercial, government, and research sectors. The market is experiencing a healthy momentum with a CAGR of 9.1% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.12 billion. This growth is primarily driven by the increasing deployment of small and medium satellites, the rising complexity of satellite missions, and a growing emphasis on real-time data processing and management capabilities in orbit.

One of the primary growth factors for the onboard database management for satellites market is the exponential increase in satellite launches, particularly small and medium satellites, for applications such as earth observation, communication, and navigation. The proliferation of low Earth orbit (LEO) satellite constellations by both commercial and governmental players is generating unprecedented volumes of data that must be managed and processed efficiently. As satellites become more autonomous and missions more complex, onboard database management systems are evolving to support real-time data analytics, adaptive mission planning, and seamless integration with ground systems. The demand for robust, fault-tolerant, and scalable database solutions onboard satellites is thus growing rapidly, further catalyzed by technological advancements in satellite hardware and software.

Additionally, the integration of artificial intelligence (AI) and machine learning (ML) algorithms into onboard database management systems is transforming satellite operations. These technologies enable satellites to process and interpret data in real-time, optimize mission parameters autonomously, and reduce latency in decision-making. This is particularly valuable for earth observation and scientific research missions, where timely data analysis is critical. The increasing sophistication of onboard processing capabilities is also driving the adoption of advanced database management solutions that can handle high-volume, high-velocity data streams. Furthermore, the growing need for secure and resilient data management in the context of cyber threats and the harsh space environment is propelling investments in next-generation onboard database systems.

The market is also benefiting from the miniaturization of satellite components and the declining cost of satellite launches. These trends are making space more accessible to a wider range of stakeholders, including start-ups, universities, and emerging space agencies. As a result, there is a burgeoning demand for modular, scalable, and easy-to-integrate onboard database management solutions that can cater to diverse mission requirements. The convergence of cloud computing, edge computing, and satellite technologies is further enhancing the capabilities of onboard database systems, enabling seamless data sharing and synchronization between satellites and ground stations. This is fostering innovation and opening up new revenue streams for solution providers in the onboard database management for satellites market.

Regionally, North America continues to dominate the onboard database management for satellites market, driven by significant investments from government agencies such as NASA and the Department of Defense, as well as a thriving commercial space industry. Europe is also witnessing substantial growth, supported by initiatives from the European Space Agency (ESA) and increasing private sector participation. The Asia Pacific region is emerging as a high-growth market, fueled by ambitious space programs in countries like China, India, and Japan, and a rapidly expanding commercial satellite sector. Latin America and the Middle East & Africa are gradually gaining traction, with growing interest in satellite-based applications for communication, earth observation, and scientific research. The global landscape is characterized by intense competition, rapid technological innovation, and a strong focus on enhancing the reliability, security, and performance of onboard database management systems.

Onboard Database Management for Satellites Market Outlook

According to our latest research, the global onboard database management for satellites market size reached USD 1.42 billion in 2024, demonstrating robust expansion fueled by the increasing deployment of advanced satellite constellations and the escalating demand for real-time data processing in space. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching an estimated USD 4.16 billion by 2033. This impressive growth trajectory is primarily driven by advancements in satellite technology, the miniaturization of satellite hardware, and the rising adoption of AI-powered onboard data management systems.

A primary growth factor propelling the onboard database management for satellites market is the increasing complexity and volume of data generated by modern satellite missions. With the proliferation of small satellites and mega-constellations, satellites are now required to process, store, and manage vast quantities of data in real-time to support diverse applications such as Earth observation, navigation, and communications. This shift towards autonomous satellite operations is necessitating sophisticated onboard database management solutions capable of handling high-throughput data streams, ensuring data integrity, and enabling rapid decision-making without ground intervention. The integration of edge computing and AI-driven analytics into onboard systems further amplifies the market's expansion, as these technologies facilitate efficient data processing and reduce the reliance on ground-based infrastructure.

Another significant driver is the rapid evolution of satellite hardware and software architectures, which has opened new frontiers for onboard database management. The trend towards modular satellite designs and standardized software interfaces allows for seamless upgrades and integration of advanced database management systems. This flexibility is especially critical for commercial operators and government agencies seeking to extend the operational life of satellites and adapt to changing mission requirements. Furthermore, the emergence of cloud-based and hybrid deployment models is transforming how satellite data is managed, enabling dynamic allocation of processing resources between onboard and ground systems. These advancements are not only enhancing operational efficiency but are also reducing mission costs, thereby making satellite-based services more accessible to a broader range of end-users.

The growing emphasis on space situational awareness and cybersecurity is also fueling the demand for robust onboard database management solutions. As the number of satellites in orbit continues to rise, the risk of data breaches, signal interference, and unauthorized access becomes a critical concern. Modern onboard database management platforms are now incorporating advanced encryption, authentication, and anomaly detection features to safeguard sensitive mission data and ensure the integrity of satellite operations. This heightened focus on security is particularly evident in government and defense sectors, where the protection of classified information and mission-critical assets is paramount. As a result, vendors are investing heavily in research and development to deliver secure, resilient, and scalable database management solutions tailored to the unique challenges of space environments.

From a regional perspective, North America currently dominates the onboard database management for satellites market, accounting for the largest revenue share in 2024. This leadership position is underpinned by the presence of major satellite manufacturers, strong government support for space exploration initiatives, and a thriving commercial space sector. However, the Asia Pacific region is emerging as a key growth engine, driven by significant investments in satellite infrastructure by countries such as China, India, and Japan. Europe also maintains a strong foothold, supported by collaborative space programs and robust research and development activities. As the global space economy continues to expand, these regions are expected to witness sustained growth in demand for advanced onboard database management solutions, further intensifying competition and fostering innovation across the industry.

Component Analysis

The onboard database management for satellites market is segmented by component into software, hardware, and services, each playing a pi

As of June 2024, almost a ******* percent of the licenses for spatial database management systems (DBMSs) were open-source licenses. Over the years, open source DBMSs have become more and more popular. As of the evaluated period, open source DBMSs have become as popular as commercial ones.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

Spatial Database Market Outlook

According to our latest research, the global spatial database market size reached USD 2.94 billion in 2024, driven by the exponential growth in geospatial data generation and the increasing adoption of location-based services across industries. The market is projected to grow at a robust CAGR of 12.1% from 2025 to 2033, reaching a forecasted value of USD 8.23 billion by 2033. This impressive growth trajectory is primarily fueled by advancements in spatial analytics, the proliferation of IoT devices, and the rising demand for real-time geographic information systems (GIS) in both public and private sectors.

One of the primary growth factors for the spatial database market is the surging demand for advanced geospatial analytics in urban planning and smart city initiatives. As cities across the globe embrace digital transformation, there is an increasing need for sophisticated spatial databases capable of handling complex, multi-dimensional datasets. These databases enable city planners and government agencies to analyze spatial relationships, optimize resource allocation, and improve decision-making processes. The integration of spatial databases with AI and machine learning algorithms further enhances their analytical capabilities, allowing for predictive modeling and real-time visualization of urban dynamics. This has accelerated the adoption of spatial database solutions in both developed and emerging economies, positioning the market for sustained growth over the next decade.

Another significant driver is the rapid expansion of IoT and connected devices, which generate vast volumes of location-based data requiring efficient management and analysis. Industries such as transportation, logistics, and utilities are leveraging spatial databases to track assets, optimize routes, and monitor infrastructure in real time. The ability to process and analyze geospatial data streams from sensors, vehicles, and mobile devices is critical for operational efficiency and risk mitigation. Moreover, the increasing use of spatial databases in environmental monitoring—such as tracking climate change, natural disasters, and resource management—underscores their importance in supporting sustainability initiatives. This trend is further amplified by the growing emphasis on data-driven decision-making across sectors, fueling the demand for scalable and high-performance spatial database solutions.

The adoption of cloud-based spatial database solutions is another pivotal factor contributing to market growth. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to access and manage spatial data without significant upfront investments in infrastructure. The shift towards cloud-native architectures also facilitates seamless integration with other enterprise applications and data sources, enhancing interoperability and data sharing. This has led to a surge in demand for spatial database-as-a-service (DBaaS) offerings, particularly among small and medium enterprises (SMEs) and organizations with distributed operations. The ongoing advancements in cloud security and data privacy are further encouraging the migration of critical geospatial workloads to the cloud, accelerating the overall expansion of the spatial database market.

From a regional perspective, North America continues to dominate the spatial database market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to the presence of major technology players, a mature IT infrastructure, and significant investments in smart city and defense projects. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, government-led digitalization initiatives, and the increasing adoption of advanced GIS technologies in countries such as China, India, and Japan. The region's robust economic growth and expanding industrial base are expected to create substantial opportunities for spatial database vendors, making it a key focus area for future market expansion.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.0 https://doi.org/10.5066/P955KPLE. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release (PAD-US 4.1 Vector Analysis and Summary Statistics) presents results from statistical summaries of the PAD-US 4.1 protection status (by GAP Status Code) and public access status for various land unit boundaries. Summary statistics are also available to explore and download from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary ...

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

In order to improve the capacity of storage, exploration and processing of sensor data, a spatial DBMS was used and the Aquopts system was implemented.

In field surveys using different sensors on the aquatic environment, the existence of spatial attributes in the dataset is common, motivating the adoption of PostgreSQL and its spatial extension PostGIS. To enable the insertion of new data sets as well as new devices and sensing equipment, the database was modeled to support updates and provide structures for storing all the data collected in the field campaigns in conjunction with other possible future data sources. The database model provides resources to manage spatial and temporal data and allows flexibility to select and filter the dataset.

The data model ensures the storage integrity of the information related to the samplings performed during the field survey in an architecture that benefits the organization and management of the data. However, in addition to the storage specified on the data model, there are several procedures that need to be applied to the data to prepare it for analysis. Some validations are important to identify spurious data that may represent important sources of information about data quality. Other corrections are essential to tweak the data and eliminate undesirable effects. Some equations can be used to produce other factors that can be obtained from the combination of attributes. In general, the processing steps comprise a cycle of important operations that are directly related to the characteristics of the data set. Considering the data of the sensors stored in the database, an interactive prototype system, named Aquopts, was developed to perform the necessary standardization and basic corrections and produce useful data for analysis, according to the correction methods known in the literature.

The system provides resources for the analyst to automate the process of reading, inserting, integrating, interpolating, correcting, and other calculations that are always repeated after exporting field campaign data and producing new data sets. All operations and processing required for data integration and correction have been implemented from the PHP and Python language and are available from a Web interface, which can be accessed from any computer connected to the internet. The data access cab be access online (http://sertie.fct.unesp.br/aquopts), but the resources are restricted by registration and permissions for each user. After their identification, the system evaluates the access permissions and makes available the options of insertion of new datasets.

The source-code of the entire Aquopts system are available at: https://github.com/carmoafc/aquopts

The system and additional results were described on the official paper (under review)

The data, codes and queries to accompany the paper "Multipurpose temporal GIS model for cadastral data management". Full details of the designs and use of queries are explained in the paper

PaleoRiada is the first national geographic database that compiles data on palaeoflood records published in scientific journals, book chapters, conference presentations, and publicly accessible scientific-technical reports. This database has been implemented through a Database Management System (Microsoft Access).

Funding:

Grants 2022-2023 and 2023-2026, signed between the Spanish General Directorate for Water (DGA-MITERD) and the Spanish Research Council (CSIC-MCIU), which include actions 20223TE003 and 20233TE012 (Tarquín project in IGME-CSIC).

Community of Madrid (Predoctoral research grant PIPF-2022/ECO-24879)

The Capacity Management Database (CMD) is designed to track computer resource usage of the computer hardware running Veterans Health Information Systems and Technology Architecture (VistA). Information derived from CMD is used to assess the impact of planned new software products, identify the ramifications of archiving and purging of existing data, recognize emerging problems through database analysis, and validate sizing model expectations. Data for CMD is captured from all VistA systems in real time. Accumulated data is processed at each site and the results are transmitted to CMD using MailMan messages. Data can be added to CMD daily, along with the monthly updates. On a monthly basis, reports are sent to the Chief Information Officers (CIOs) of the Veterans Integrated Service Networks (VISNs) and Veterans Affairs Medical Center (VAMC) CIOs to allow review of the system's performance and identify future problems (e.g., lack of available disk space). IT personnel can access the database through the VA's intranet (http://vaww.oed.portal.va.gov/engineering/testing/CP/default.aspx) for comparison of their system with other sites. The users of CMD include the VA Office of Information & Technology, VISN CIOs, and facility CIO staff.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 3.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (Protected Areas Database of the United States 3.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download (Comma-separated Table [CSV], Microsoft Excel Workbook (.xlsx), Portable Document Format [.pdf] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster GIS analysis files are also available for combination with other raster data (Protected Areas Database of the United States (PAD-US) 3.0 Raster Analysis). The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full inventory, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class (Protected Areas Database of the United States (PAD-US) 3.0, https://doi.org/10.5066/P9Q9LQ4B), was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

On-Board Data Handling System Market Outlook

According to our latest research, the global On-Board Data Handling System market size reached USD 2.47 billion in 2024. The market is experiencing a robust expansion, registering a CAGR of 8.8% during the forecast period. By 2033, the market is projected to attain a value of USD 5.36 billion. This impressive growth trajectory is primarily driven by the rising deployment of advanced satellites and spacecraft, coupled with the increasing demand for real-time data processing and management in both commercial and defense applications.

The primary growth factor for the On-Board Data Handling System market is the exponential increase in satellite launches for diverse applications such as earth observation, communication, and navigation. The proliferation of low Earth orbit (LEO) satellite constellations, especially for broadband internet services and global connectivity, is pushing the demand for sophisticated on-board data handling capabilities. These systems are essential for managing the vast amounts of data generated by modern payloads, ensuring efficient data acquisition, storage, processing, and transmission to ground stations. Additionally, the integration of artificial intelligence (AI) and machine learning algorithms into on-board systems is enabling autonomous data handling, further enhancing system performance and reliability in space missions.

Another significant driver is the evolution of miniaturized and cost-effective hardware components, which has revolutionized the design and deployment of on-board data handling systems. The trend towards small satellites and CubeSats has necessitated the development of compact, lightweight, and energy-efficient data handling solutions. This shift not only reduces launch costs but also opens up new opportunities for commercial players and research organizations to participate in space missions. Furthermore, advancements in software-defined architectures and modular systems have facilitated rapid customization and scalability, catering to diverse mission requirements and operational environments.

The growing emphasis on data security and resilience in space missions is also fueling the adoption of advanced on-board data handling systems. With the increasing complexity of space missions and the critical nature of transmitted data, there is a heightened focus on implementing robust cybersecurity measures and fault-tolerant architectures. This is particularly important for government and defense applications, where secure and reliable data handling is paramount. The integration of redundant systems, error correction protocols, and real-time monitoring capabilities are becoming standard features, ensuring mission success even in the face of harsh space conditions and potential cyber threats.

Onboard Database Management for Satellites is becoming increasingly critical as the complexity and volume of data generated by modern space missions continue to grow. With the advent of more sophisticated payloads and the need for real-time data processing, onboard database management systems are essential for ensuring that data is efficiently stored, accessed, and processed directly on the satellite. This capability not only enhances the satellite's autonomy but also reduces the dependency on ground stations for data management tasks. By enabling satellites to handle data locally, these systems improve the overall efficiency and reliability of space missions, allowing for quicker decision-making and response times in dynamic environments.

Regionally, North America continues to dominate the On-Board Data Handling System market, driven by substantial investments in space exploration, defense modernization, and commercial satellite programs. The presence of leading aerospace companies, advanced research institutions, and supportive government initiatives have positioned the region at the forefront of technological innovation. Europe and Asia Pacific are also witnessing significant growth, propelled by collaborative space missions, increasing R&D activities, and the emergence of new space-faring nations. These regions are expected to contribute substantially to the overall market expansion, supported by favorable regulatory frameworks and growing public-private partnerships.

Managed PostGIS Services Market Outlook

According to our latest research, the global managed PostGIS services market size reached USD 1.12 billion in 2024, reflecting robust demand across various industries for spatial database management solutions. The market is witnessing a strong growth trajectory, registering a CAGR of 13.4% from 2025 to 2033. By the end of 2033, the managed PostGIS services market is forecasted to achieve a valuation of USD 3.57 billion. This growth is primarily driven by the increasing adoption of geospatial data analytics, the proliferation of location-based services, and a rising emphasis on digital transformation across multiple sectors.

One of the foremost growth factors for the managed PostGIS services market is the surging demand for advanced geospatial analytics and spatial database management. Organizations across industries such as IT & telecom, government, BFSI, and healthcare are increasingly leveraging spatial data to drive business intelligence, optimize operations, and enhance customer engagement. The integration of PostGIS with PostgreSQL offers robust spatial and geographic object support, making it an attractive choice for enterprises seeking scalable and cost-effective solutions. Furthermore, as businesses continue to generate massive volumes of location-based data from IoT devices and mobile applications, the need for managed services that provide seamless database hosting, administration, and security is more pronounced than ever. This trend is expected to fuel sustained market growth over the forecast period.

Another significant driver is the shift towards cloud-based deployment models, which offer flexibility, scalability, and cost efficiencies. Managed PostGIS services delivered via the cloud enable organizations to reduce their IT overhead, streamline database management, and ensure high availability and disaster recovery. Cloud-based solutions also facilitate rapid deployment and integration with other cloud-native applications, supporting the growing trend of digital transformation. Moreover, the rise of hybrid work environments and remote operations has further accelerated the adoption of cloud-managed spatial databases, enabling organizations to manage and analyze geospatial data from anywhere, thereby enhancing operational agility and decision-making capabilities.

The managed PostGIS services market is also benefiting from the increasing focus on data security and regulatory compliance. With the proliferation of sensitive geospatial data, organizations are under pressure to ensure robust security protocols and adherence to data protection regulations. Managed service providers are responding by offering advanced security and compliance solutions, including encryption, access control, and automated backup and recovery. These services not only help organizations mitigate risks but also enable them to focus on their core business objectives without the burden of managing complex database environments. As regulatory requirements continue to evolve, the demand for managed PostGIS services that offer comprehensive security and compliance features is expected to rise.

From a regional perspective, North America currently dominates the managed PostGIS services market, driven by the presence of leading technology companies, high adoption of cloud-based solutions, and a strong focus on innovation. Europe and the Asia Pacific region are also witnessing significant growth, supported by increasing investments in smart city projects, digital infrastructure, and the expansion of the IT & telecom sector. Emerging markets in Latin America and the Middle East & Africa are gradually embracing managed PostGIS services as organizations in these regions recognize the value of spatial data analytics for business growth and operational efficiency. Overall, the global managed PostGIS services market is poised for substantial expansion, with diverse regional dynamics shaping its trajectory.

Service Type Analysis

The service type segment in the managed PostGIS services market is broadly cat

Satellite design encompasses a multitude of steps from concept to flight. Mission specification to flight can take several years, depending on the scope, requirements and budget of the mission. The process also requires a wide range of design and management tools, with limited consistency data interchange capability, and a lack of coherency. Detailing the relationships between the satellite configuration, inventory control systems, life cycle management, design, analysis and test data is difficult at best. No tool exists that meets these needs for the general satellite design, system engineering and integration process. Sci_Zone is proposing our innovative Satellite Design Automation architecture SatBuilder Designer, in conjunction with the OpenSAT open database architecture to meet this need. OpenSAT seamlessly integrates existing detail design tools with SatBuilder Designer, as well as databases tracking requirements, components and inventory, with the final configuration of the satellite. SatBuilder Designer, an AI based toolset, provides for rapid design via design wizards and integration to existing design tools; it provides coherency between a range of applications and data sets. OpenSAT stores and distributes supporting satellite design, configuration, mission and test data from a centralized database server and can distribute the data across multiple platforms and via the internet.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists (i.e. green grass) are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localised and dispersed among data owners and databases. Here, we synthesised data from several research groups and conservation NGOs and present the Landscape Dynamics (landDX) spatial-temporal database. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions; to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management; and for local and governmental spatial planning.

Summary data for coverage of legally mandated MPAs in the Coral Triangle countries (June 2013). (DOCX)

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.