As more geospatial data is collected and analyzed, knowledge and understanding of geospatial data become crucial today. Many data scientists are expanding their work outside the GIS department and taking into other roles geospatial insights.
Geospatial skills and capabilities are growing in demand, and geospatial functions are evolving to become an integral part of data analytics, artificial intelligence, big data, and more. Competitive advantage will be given to companies that accept GIS professionals' skills and expertise extensively. GIS professionals will continue to grow beyond just the skill and tactics set of GIS solutions to collaborate with other departments throughout the organization to remain ahead of the game.
All about geospatial solutions
Predictions for the adoption of connected items vary widely, but the link points and volumes of data will increase rapidly. Therefore, the challenge is not to deploy smart things or to collect data from smart things. The key issue is the ability to combine and analyze data to gain insights. These insights may, in some cases, prompt decisions with global consequences, solving some of the most knotty problems of humanities. In other situations, the insights can lead to a small action that enhances an individual's life.
This is where geospatial solutions interfere. In particular, analytics and big data have important significant roles in interpreting the data generated in the connected world. But the province of geospatial solutions, an area that has much to contribute to the connected world, is to envision that data in a way that tells a story and uncovers insights. Unfortunately, in particular, in insurance, the old impressions of geographic information systems (GIS) linger. Many insurers provide GIS tools for geospatial research; however, they continue to be used for very specific applications by a handful of specialists.
Currently, 3D breakthroughs; animation; digital capture by drones, satellites, or LiDAR; and other technologies are offering new opportunities. Tools for spatiotemporal analysis (understanding of changes over time), crowdsourcing of real-time data and cloud-based map and device sharing networks have elevated the discipline and given the opportunity for government and industry to gain a deep understanding of the world to help address both new and old issues.
Geospatial solutions include GIS (geographic information systems), GPS (global positioning systems), and RS (remote sensing). It is a technology that provides a completely different way of producing and using maps that are important for public and industry management.
The categories of spatial technologies used in industrial applications include geographic information systems, remote sensing, and global positioning systems. Remote sensing is the satellite imagery and geospatial data gathered over and from satellite or aerial cameras. Some satellite business image can produce 30 centimeters of detail. Satellite imaging significantly enhances a GIS mapping project and functions as a source of information and data to support the evaluation and classification of geospatial analysis and modeling.
Factors which have increased the adoption of geospatial solutions?
1. Geospatial in the Digital Ecosystems
Spatial data and location with interconnected technologies – Big Data, Augmented Reality, Machine Learning, Artificial Intelligence, IoT, and 3D systems – shape the nature of traditional business practices in this data-driven manufacturing period, offering competitive advantages through analytics, data visualization, and digital insights. It signifies the ever-increasing need for real-time information.
Far and beyond archetypal industries such as urban planning, internal security, governance, and defense, the spatial framework remains an integral part. It is also a central resource for smart applications, workflow management, and a big platform for manufacturing, architecture, telecommunications, technology, smart transportation, and many other fields. Location information may have been required from a location to less than one meter of accuracy (and, in some instances, millimeter accuracy).
2. Artificial Intelligence and Big Data Analytics
Geospatial Artificial Intelligence (geoAI) is an emerging field of science that combines space science innovations, artificial intelligence techniques in data mining, machine learning (e.g., in-depth learning), and high-performance computing to gain understanding from large-scale spatial information. Exposure modeling is a commonly used tool of environmental epidemiology to conduct exposure assessment to evaluate exposure distribution in the research population.
GeoAI techniques provide substantial benefits for environmental epidemiology exposure modeling. This also includes the ability to integrate large quantities of spatial and temporal information in a variety of formats; computational efficiency; flexibility in algorithms and workflows. It helps to adapt suitable spatial (environmental) process features, such as spatial non-stationarity; and scalability.
3. Development of the GIS Industry Using 3D, AR, and VR
Far from being appealing, three-dimensional models have all sorts of uses, from planning and analyzing anything from urban development to knowing where first responders can be reached in an emergency. If 3D modeling can be paired with virtual reality and augmented reality, appreciating what they see is even simpler for some people. In the GIS industry, the next big thing is 3D, AR, and VR.
Take, for instance, a project headed by the Fraunhofer Institute that Esri Deutschland GmbH is involved in called Morgenstadt — City of the Future. The initiative utilizes 3D technology and VR to operate for tomorrow's towns to predict, develop and implement technologies. Esri technology is used to produce urban 3D models, a key urban planning instrument that can be used together with AR and VR to create the scheduling method more transparent.
To sum up
The geospatial solutions market is powered by a broad range of factors, including developments in geospatial technology with the proliferation of artificial intelligence and big data analytics. We now live in a data-driven and information-centric world where data users' expectations are constantly increasing. The geospatial industry has continued to remain prominent in the development of innovative solutions that provide citizens and communities with improved outcomes.
The big data landscape provides the custodians of authoritative geospatial data sets with a spectrum of challenges and opportunities for industries trying to embrace the new big data technologies.