Images and information from satellites might soon be able to help the government and policymakers frame need-specific solutions across sectors such as sanitation, employment, agriculture, education and health.
The government is currently working on a project that will use real-time data to help determine economic and development indicators.
At present, majority of the policymaking is based on census data that is collated over a decade. The problem with this method is that there is large room for error and a possibility of lag in data.
A high-end project will soon be developed by NITI Aayog in collaboration with IIT-Delhi. This project will use machine-learning tools to analyse images from satellites up to the district level.
Economic Times reported a senior government official stating that IIT-Delhi has completed a lot of work towards this project and the government may settle on a form of collaboration with them in the near future. The idea of this project is to use satellite data to help predict the main socioeconomic indicators.
Though there is no formal collaboration with NITI Aayog yet, a professor from IIT-Delhi was reported saying that they are in discussions to take the project further for the purpose of research as well as application. The professor stated that the technology can be applied to a variety of areas such as which areas require better investment in education, or certain areas that require better sanitation.
The satellite images may also be able to determine outbreaks of waterborne diseases such as dengue. This data can help flag high-risk areas that have poor sanitation. The technology may also be able to predict areas that need to improve their pre and post-natal care.
This technology has been used before by researchers at Standford University. They used daytime and night-time data to estimate quite accurately the level of household consumption and assets. The same method is being implemented by IIT-Delhi to determine the socioeconomic conditions of 2 lakh villages in Haryana, Punjab, Bihar, Uttar Pradesh, Jharkhand and West Bengal.
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