Why Geospatial Analytics Relies on AI for Extracting Structured Imagery Data?
Geospatial technology is gradually emerging as a great aid to recognize geospatial data as a provider of essential and relevant information for various industries such as agriculture, defense, insurance, etc.
Over the past few years, remote sensing technology in geospatial analytics has seen significant growth due to the increase in natural catastrophic events leading to changes in physical and chemical characteristics of the Earth’s environment.
Geospatial analytics collects, analyzes, and displays geographic information system (GIS) data and imagery, including global positioning system (GPS).
Advanced street maps, satellite imagery, and route planners are being widely adopted by the global geospatial customer base to gain location intelligence and identify the exact area of choice. Such intelligence merges the capabilities of geospatial technologies, data analytics, and artificial intelligence (AI) to visualize geographical patterns, routes, and spatial trends to plan, map, and optimize the positional dimension of geospatial data.
Here we will learn about the role and application of AI in geospatial imagery analytics.
Why is geospatial imagery analytics gaining wide recognition?
Geospatial imagery analytics provides actionable insights to diverse end users across applications. The geospatial-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 comprising mathematical formulas and can be infinitely scaled.
Geospatial imagery data analytics plays an important role in the adoption of digital innovation, contributing significantly to the country’s gross domestic product (GDP) as well as optimizing efficiency and cost-effectiveness of the economic sectors such as infrastructure, energy, agriculture, financial services, retail and logistics, and climate and environment.
The evolution of integrated domain awareness solutions and growing sources of satellite and drone imagery products are driving the growth of the geospatial imagery analytics market across sectors.
According to a report by BIS Research, global geospatial imagery analytics market is expected to reach $32.78 billion in 2032 from $24.25 billion in 2021, at a CAGR of 2.90% during the forecast period 2022–2032.
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Geospatial imagery analytics companies have witnessed an increasing demand for applications such as agriculture, disaster management, cartography, and natural resources survey.
Why is geospatial analysis merging with AI?
Geospatial imagery analytics are used in various industries such as agriculture, defense and security, mining and manufacturing, healthcare, and insurance.
Hence, the capability to serve diverse industries, enhance operations, and have data-driven decision-making, has led to the large-scale adoption of geospatial analytics to merge with numerous technologies such as artificial intelligence.
AI can make extensive data handling easy and quick from different sources such as satellites, aerial vehicles, and ground technologies.
Additionally, the rise of urbanization and government projects are constituting the rapid integration of AI. Also, through automation, AI can extract information and provide real-time insights from big spatial data.
Some other significant advantages of AI in geospatial imagery data analysis are:
- AI is a savior of time and manual effort when analyzing GIS data. AI holds the ability to minimize turnaround time by automating thousands of repetitive tasks like classification or clustering.
- AI algorithms generate suitable outputs in new geographic data with the data patterns received, which concludes the results without clustering data.
- AI is a scalable technology that can run 24×7, providing precise AI-powered solutions and eliminating human error.
What are the growth-driving factors of geospatial AI?
With data-driven AI geospatial analytics, companies can achieve advance warnings of the changes which they can bring in the future event and increase the efficiency of their offerings. These developments make geospatial AI technology robust and ensure accelerated growth in the coming years.
Due to the volume of imagery involved in satellite data, geospatial AI generally requires high computing power.
Further growth driving factors of geospatial AI are:
- Availability of Data- The quality of geospatial analytics is measured by 5Vs, i.e., volume, velocity, veracity, variety, and value in the extraction of structured imagery data.
- Cloud Storage- With cloud computing, geospatial AI algorithms run faster and more efficiently on today’s computers by satisfying the requirement enormous amount of storage space.
- Better Algorithms- With the increased computing capacity, more efficient and complex AI algorithms can be easily run on massive Geospatial datasets.
Conclusion
Geospatial analytics has various applications, such as weather monitoring, surveillance, urban planning, and disaster management. Geospatial imagery analytics have depended more on imagery from multiple sources, such as satellites, aerial vehicles, and aircraft.
While geospatial imagery is used in various industries, AI enables structured, efficient, and accurate data for professionals in various fields, which helps in responding to issues more quickly while saving money.
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