Geospatial AI also known as Geospatial Analytics or Geospatial Intelligence (GEOINT) is one of the emerging technological trends in the geospatial industry. It is the convergence of geospatial data and artificial intelligence. This technological innovation is anticipated to be crucial for businesses and government bodies such as weather centers, national labs, defense agencies, healthcare, agriculture, insurance, and transportation, among others. Currently, various commercial companies and government organizations are deploying deep learning techniques, combined with geospatial data from satellites to drive the potential usage of geospatial AI. For instance, at a Canadian Geoignite Conference in 2019, Canada Mortgage and Housing Corporation explained the use of deep learning and satellite geospatial data to track housing starts, particularly for remote and indigenous areas. In another instance in 2017, Capgemini utilized Synthetic Aperture Radar (SAR) data and machine learning to evaluate forest replenishing in the U.K., augmenting a costly labor-intensive manual process.
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Geospatial imagery analytics provide video and image data of the Earth. The geo-referenced images are presented as raster and vector images. Raster images, also known as bitmaps, are individual pixels of color, whereas vector images are graphics comprised of mathematical formulas and can be infinitely scaled. Geospatial imagery analytics are used for various applications such as agriculture, defense & security, mining and manufacturing, healthcare, and insurance among others.
The geospatial imagery analytics market is expected to have a significant growth during the forecast period 2020-2025. The factors attributing to the growth of the market include the rise in the adoption of emerging technologies, increasing demand for small satellites, and the overall growth of the geospatial industry. Continuous technological advancements have resulted into the integration of geospatial technologies into systems of national security, scientific, and commercially operated satellites supplemented by enhanced enterprise GIS and Unmanned Aerial Vehicles (UAVs). Geospatial technologies such as GPS technology have assisted in managing agricultural and natural resources by providing satellite images.
The geospatial imagery analytics has numerous industrial applications with its various technologies augmenting the numerous industry verticals. Geospatial has become a key component for organized workflow, smart applications, and a key enabler of automation in manufacturing, telecom, architecture, engineering, intelligent and transportation among others. Based on application, the geospatial imagery analytics is segmented into agriculture, mining & manufacturing, defense & security, energy, utility, and natural resources, government, healthcare, insurance, and others. Geospatial imagery analytics require supportive technologies that aid in the collection and processing of data. Geospatial technologies help in a seamless collection of earth’s data and analyze it for modelling, simulations and visualization, helping in making informed decisions. These geospatial technologies have been transforming since the earliest maps were drawn in prehistoric times. However, continuous technological advancements have resulted the integration of geospatial technologies into the system of national security, scientific, and commercially operated satellites supplemented by enhanced enterprise GIS and Unmanned Aerial Vehicles (UAVs).
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GIS technology are used for environmental management such as natural calamities and disasters, global climate change, natural resources, wildlife, land surfaces, and many other applications. Another geospatial technology, which is remote sensing, is used for applications such as identifying and mapping of the topography of the ocean floor such as large mountain ranges, deep canyons on the ocean floor. However, the deployment of UAVs for collection geospatial information has increased in the past years and is expected to grow during the forecast period due to the potential capabilities of the UAVs.
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