Artificial Intelligence Geographic Information Systems-AI GIS

Document Type : Original Article

Author

Public Works , Civil Engineering , Thebes Academy , Cairo , Egypt

Abstract

— The integration of artificial intelligence (AI) and
geographic information systems (GIS) has opened up new
possibilities and advancements in the field of geoscience. AI
techniques, such as machine learning and deep learning, have
shown great potential in solving various GIS challenges and
improving the intelligence of GIS software. GeoAI, which
combines AI with GIS operations, encompasses geospatial
machine learning and geospatial deep learning. Geospatial
machine learning in ArcGIS allows users to address tasks like
geographical clustering, spatial classification, and spatial
regression. Geospatial deep learning algorithms, on the other
hand, enable advanced analysis of 3D data and images.
AI for GIS involves applying AI techniques to enhance the
capabilities of GIS software. This includes AI attribute
collection, AI survey and mapping, AI cartography, and AI
interaction. By leveraging AI, GIS software can become more
intelligent and efficient in handling data and performing various
tasks. GIS for AI refers to the utilization of GIS capabilities in
further processing and mining data obtained from AI
recognition. By incorporating geographical visualization and
spatial analytics, GIS can enhance AI findings and provide
decision makers with more intuitive information expression.
Examples of GIS for AI applications include traffic flow
monitoring, city component management, real-time geo-fence
alerts, and vehicle tracking. Prominent companies across
industries are strategically investing in AI, particularly machine
learning, and leveraging location data to gain competitive
advantages. Location analytics is being used for discovering
hidden trends, gaining critical insights, and making informed
decisions.

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