Artificial Intelligence (AI) has been at the helm of most of the technological transformations that the world has seen in recent years – so much so that it is now a keyword for industries that are future-driven. The usage of intelligent algorithms, classification of data along with the use of smart predictive analysis AI has found application across a multitude of sectors. Coming to a more specific intersection, which is the combination of AI and GIS to create a relation-based approach called geospatial AI or GeoAI – it can be termed as a new-age machine learning, which is based on geographical components. Presently, GeoAI is one of the most interesting emergent technologies that has found equal application in government and private sector enterprises for data analysis.
Geospatial is a huge dataset that can be utilised by deep learning and machine learning in making their findings much more accurate. 80% of the datasets that we generate today are geospatial, which means that GeoAI naturally creates solutions for other basic sectors. Be it agriculture, autonomous vehicles, climate or defence, etc. GeoAI finds varied applications in all these sectors since the data collected in them is primarily geospatial in nature.
Given today’s technologically dependent, connected world, there is a continuous process of data integration, which places geospatial solutions at the core. If one looks at the business world, there is a constant expansion in the capabilities of geospatial technology companies, moving from data, hardware/software marketing silos to providing 360 decision-making solutions, via GeoAI. This has led to the collaboration and convergence of standalone systems such as e-Governance, business and knowledge process engineering etc. to create end-to-end solutions.
Geospatial technologies like LiDAR, Satellite imagery, drone mapping, surveying terrestrial cameras produces accurate data with 3D information. Taking into consideration factors responsible for effective decision making such as high data volume and other parameters, it becomes virtually impossible for the human mind to arrive at most conclusive or lowest possible risk situation. AI embedded in spatial technologies can do this end, assist in analysing multiple factors, which can be then perceived by the AI in real-time with an almost-human perspective via accurate 3D data. One can take the example of GeoAI in self-driving cars which can solve complex problems in heavy traffic conditions without human intervention and is error-free.
The rise of GeoAI has brought about tremendous opportunities for the public and private sector to be able to incorporate timely, informed meaures, for example, it could be helping in increasing crop yield with the help of precision agriculture or assisting in fighting crime by the deployment of predictive policing models or even predicting when the next big storm or flood will hit. Machine learning and Deep learning application in GIS and remote sensing has also been demonstrated in various applications like identifying slums in cities, building extraction and object detection. GeoAI can thus take its learning further in the spatial domain to mimic human brain and aid in decision making.
GeoAI holds within it the potential to become a major driver for economic growth as well as social progress, creating massive opportunities that were not possible before. For this to happen, industry, civil society, government as well as the public need to work together in order to support the shift in technology and give thoughtful attention to its full potential.
