Geospatial Big Data Platform for Defense Market Outlook

According to our latest research, the global Geospatial Big Data Platform for Defense market size in 2024 reached USD 4.3 billion, driven by the increasing adoption of advanced data analytics and real-time situational awareness solutions across defense sectors worldwide. The market is expected to grow at a robust CAGR of 12.7% from 2025 to 2033, with a projected value of USD 12.7 billion by the end of 2033. This notable expansion is primarily attributed to the rising need for intelligent decision-making, enhanced surveillance capabilities, and the integration of AI-powered geospatial analytics in defense operations.

One of the primary growth factors fueling the Geospatial Big Data Platform for Defense market is the exponential increase in data generated from modern defense systems, including satellites, unmanned aerial vehicles (UAVs), and ground-based sensors. The ability to process, analyze, and visualize massive volumes of geospatial data in near real-time is critical for mission success, especially in intelligence, surveillance, and reconnaissance (ISR) activities. Defense organizations are increasingly investing in advanced platforms that can handle structured and unstructured data, enabling commanders to gain actionable insights and maintain a tactical advantage on the battlefield. The integration of AI and machine learning algorithms into geospatial platforms further enhances data processing speed and accuracy, making these solutions indispensable for modern defense strategies.

Another significant driver is the growing emphasis on interoperability and collaboration among allied forces. Modern military operations often involve joint missions that require seamless data sharing and situational awareness across different branches and nations. Geospatial big data platforms are designed to support standardized data formats, secure communication protocols, and multi-domain operations, facilitating effective coordination in complex scenarios. As defense budgets continue to prioritize digital transformation and network-centric warfare capabilities, the demand for interoperable geospatial solutions is expected to surge, creating substantial growth opportunities for platform providers and technology vendors.

Furthermore, the increasing prevalence of asymmetric warfare and evolving security threats necessitates rapid and informed decision-making. Geospatial big data platforms enable defense agencies to monitor potential threats, track troop movements, and assess environmental variables in real time. The adoption of cloud-based platforms further enhances accessibility and scalability, allowing defense personnel to access critical data from remote or contested environments. As governments worldwide recognize the strategic importance of geospatial intelligence, investments in next-generation big data platforms are projected to accelerate, reinforcing the market’s upward trajectory.

Regionally, North America maintains a dominant position in the Geospatial Big Data Platform for Defense market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, with its substantial defense budget and focus on technological innovation, leads the adoption of advanced geospatial solutions. Europe’s market growth is driven by modernization initiatives and collaborative defense projects among EU member states, while Asia Pacific is witnessing rapid expansion due to rising defense expenditures in China, India, and Southeast Asian countries. Latin America and the Middle East & Africa are gradually increasing their investments in geospatial intelligence, though their market shares remain comparatively smaller. As geopolitical tensions and security challenges persist, regional markets are expected to experience varying growth rates, shaped by local priorities and technological advancements.

Component Analysis

The Component segment of the Geospati

Total-Current-Assets Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

This dataset contains metadata (title, abstract, date of publication, field, etc) for around 1 million academic articles. Each record contains additional information on the country of study and whether the article makes use of data. Machine learning tools were used to classify the country of study and data use.

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the year 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that are included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample.

The empirical approach employed in this project utilizes text mining with Natural Language Processing (NLP). The goal of NLP is to extract structured information from raw, unstructured text. In this project, NLP is used to extract the country of study and whether the paper makes use of data. We will discuss each of these in turn.

To determine the country or countries of study in each academic article, two approaches are employed based on information found in the title, abstract, or topic fields. The first approach uses regular expression searches based on the presence of ISO3166 country names. A defined set of country names is compiled, and the presence of these names is checked in the relevant fields. This approach is transparent, widely used in social science research, and easily extended to other languages. However, there is a potential for exclusion errors if a country’s name is spelled non-standardly.

The second approach is based on Named Entity Recognition (NER), which uses machine learning to identify objects from text, utilizing the spaCy Python library. The Named Entity Recognition algorithm splits text into named entities, and NER is used in this project to identify countries of study in the academic articles. SpaCy supports multiple languages and has been trained on multiple spellings of countries, overcoming some of the limitations of the regular expression approach. If a country is identified by either the regular expression search or NER, it is linked to the article. Note that one article can be linked to more than one country.

The second task is to classify whether the paper uses data. A supervised machine learning approach is employed, where 3500 publications were first randomly selected and manually labeled by human raters using the Mechanical Turk service (Paszke et al. 2019).[1] To make sure the human raters had a similar and appropriate definition of data in mind, they were given the following instructions before seeing their first paper:

Each of these documents is an academic article. The goal of this study is to measure whether a specific academic article is using data and from which country the data came.

There are two classification tasks in this exercise:

1. identifying whether an academic article is using data from any country

2. Identifying from which country that data came.

For task 1, we are looking specifically at the use of data. Data is any information that has been collected, observed, generated or created to produce research findings. As an example, a study that reports findings or analysis using a survey data, uses data. Some clues to indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.

After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.[2]

For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper.

We expect between 10 and 35 percent of all articles to use data.

The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labelled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. 900 articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

The model was able to predict whether an article made use of data with 87% accuracy evaluated on the set of articles held out of the model training. The correlation between the number of articles written about each country using data estimated under the two approaches is given in the figure below. The number of articles represents an aggregate total of

Net-Interest-Income Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this i...

Geological storage of carbon dioxide is receiving more and more attention as one of the efficient carbon reduction technologies, as China’s carbon-neutral strategic plan moves forward. There is an increasing demand for more effective and thorough methodologies to assess the potential of CO2 storage in deep saline aquifers. This study proposes a method for evaluating the geological storage potential of CO2 in deep saline aquifers and constructs an automatic evaluation system for the comprehensive potential of CO2 geological storage using ArcGIS Model Builder visual modeling technology. The automatic evaluation system consists of four functional parts: information collating and database constructing, data pre-processing, model building evaluation and result validation evaluation. First, structured and unstructured data including underlying geology, tectonic geology, oil and gas geology, and drilling data are collated and established in a geodatabase. Second, pre-processing models of the deep saline reservoir-caprock data are established based on the analysis of the geological evolution history of the study area to determine the effective storage thickness, effective porosity, and the influence range of faults; kriging methods are then used to realize the spatial interpolation of the evaluation parameters. Third, the volume coefficient method is adopted to construct the underground storage space model and to establish the density distribution model of the supercritical CO2 with nonlinear function while taking into account four evaluation factors (i.e. area, effective porosity, effective thickness, effective coefficient) and two limiting factors (i.e. fault, burial depth). Finally, the geological storage potential of CO2 in the study area is evaluated with the classification of the potential level and compared with the numerical simulation results to verify the model’s accuracy. The model is first applied in this paper using a suitable target in China as a case study. The results show that this target area’s anticipated storage potential value reaches 52.557 Mt. The total precision error, according to a comparison of the numerical simulation results, is 8.20%. Based on the results obtained, it can be concluded that the automatic GIS-based modeling approach is suitable for a comparable study of potential evaluation of CO2 geological storage in deep saline aquifers.

Accounts-Payable Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

📖 Dataset Summary

Floods are among the most frequent and devastating disasters worldwide, yet data describing them is often scattered across unstructured reports, geospatial sources, and satellite imagery.This dataset unifies those heterogeneous data sources into a structured, ontology-aligned Knowledge Graph (KG) format.
Each event is represented with:

Metadata: disaster type, location, date, country.
Textual Descriptions: humanitarian situation reports from ReliefWeb.… See the full description on the dataset page: https://huggingface.co/datasets/teoaivalis/extreme-floods-kg.

Retained-Earnings Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Short-Term-Debt Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Change-In-Working-Capital Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Diluted-Average-Shares Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Net-Income-Including-Non-Controlling-Interests Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Operating-Profit-Margin Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Return-On-Total-Capital Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.

Cash-Flow-Per-Share Time Series for Elastic NV. Elastic N.V., a search artificial intelligence (AI) company, provides software platforms to run in hybrid, public or private clouds, and multi-cloud environments in the United States and internationally. It primarily offers Elastic's Search AI Platform, a set of software products that ingest and store data from various sources and formats, as well as performs search, analysis, and visualization on that data. The company also provides Elastic search product a distributed, real-time vector database and analytics engine and data store for all types of data, including textual, numerical, geospatial, structured, and unstructured; Kibana, a user interface, management, and configuration interface for the platforms; Elasticsearch search platform, a platform with retrieval algorithms and the ability to integrate with large language models; and elastic security, a security solution that provides unified protection to prevent, detect, and respond to threats. In addition, it offers Elastic Observability, a solution that enables unified analysis, including Logs analytics to search and analyze petabytes of structured and unstructured logs; infrastructure monitoring to gain visibility across cloud, on-premises, Kubernetes, serverless, and hosts; Application Performance Monitoring to stream native production-grade; digital experience monitoring; and large language models. The company was incorporated in 2012 and is based in Amsterdam, the Netherlands.