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— 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.
Zakaria Ahmed (Sat,) studied this question.
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