The role of artificial neural network and machine learning in utilizing spatial information The role of artificial neural network and machine learning in utilizing spatial information

• Published in: Spatial information research (Online) Vol. 31; no. 3; pp. 275 - 285
• Main Authors: Goel, Akash, Goel, Amit Kumar, Kumar, Adesh
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2023; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Deep learning; Spatial information; Multimedia applications; Satellite communication; Machine learning; Artificial neural networks
• DOI: 10.1007/s41324-022-00494-x
• ISSN: 2366-3286

In this age of the fourth industrial revolution 4.0, the digital world has a plethora of data, including the internet of things, mobile, cybersecurity, social media, forecasts, health data, and so on. The expertise of machine learning and artificial intelligence (AI) is required to soundly evaluate the data and develop related smart and automated applications, These fields use a variety of machine learning techniques including supervised, unsupervised, and reinforcement learning. The objective of the study is to present the role of artificial neural networks and machine learning in utilizing spatial information. Machine learning and AI play an increasingly important role in disaster risk reduction from hazard mapping and forecasting severe occurrences to real-time event detection, situational awareness, and decision assistance. Some of the applications employed in the study to analyze the various ANN domains included weather forecasting, medical diagnosis, aerospace, facial recognition, stock market, social media, signature verification, forensics, robotics, electronics hardware, defense, and seismic data gathering. Machine learning determines the many prediction models for problems involving classification, regression, and clustering using known variables and locations from the training dataset, spatial data that is based on tabular data creates different observations that are geographically related to one another for unknown factors and places. The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information processing, healthcare, and weather forecasting with greater than 90% accuracy.