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Machine Learning and Artificial Intelligence: Rural Development Analysis Using Satellite Image Processing
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Author(s): Anupama Hoskoppa Sundaramurthy (BMS Institute of Technology and Management, India), Nitya Raviprakash (Rashtreeya Vidyalaya College of Engineering, India), Divija Devarla (Rashtreeya Vidyalaya College of Engineering, India)and Asmitha Rathis (Rashtreeya Vidyalaya College of Engineering, India)
Copyright: 2020
Pages: 11
Source title:
AI and Big Data’s Potential for Disruptive Innovation
Source Author(s)/Editor(s): Moses Strydom (Emeritus, France)and Sheryl Buckley (University of South Africa, South Africa)
DOI: 10.4018/978-1-5225-9687-5.ch004
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Abstract
This chapter proposes a cost-effective and scalable approach to obtain information on the current living standards and development in rural areas across India. The model utilizes a CNN to analyze satellite images of an area and predict its land type and level of development. A decision tree classifies a region as rural or urban based on the analysis. A summary describing the area is generated from inferences made on the recorded statistics. The CNN is able to predict the land and development distribution with an accuracy of 95.1%. The decision tree predicts rural areas with a precision of 99.6% and recall of 88.9%. The statistics obtained for a dataset of more than 1000 villages in India are cross-validated against the Census of India 2011 data. The proposed technique is in contrast to traditional door-to-door surveying methods as the information retrieved is relevant and obtained without human intervention. Hence, it can aid efforts in tracking poverty at a finer level and provide insight on improving the economic livelihood in rural areas.
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