Predicting Socio-Economic Development Using Deep Learning
Aditya Singh , Devesh Pandey, Anuj Pandey , Snehal Latam
DOI : 10.46335/IJIES.2023.8.4.2
Abstract— For Uniform growth across the country there is a need to find socio-economic status and monitoring of remote areas. It is about the current state of development or the process state of socio-economy of that place. In our paper, we will predict the development in an location using satellite images provided by various sources using a model that we create which will perform classification and use various image preprocessing techniques. The top things considered during monitoring are the roof top of houses, agriculture, water bodies and constructed roads etc. Convolution neural networks are known for its inbuilt libraries such as OpenCV, NumPy etc. OpenCV is good library has it known for increasing speed of process that is executing and also classifying the image. CNN also provides better accuracy for deep learning processes. In this paper we have use basically three modules: preprocessing of image, CNN classification and predict the social economic status by using the four basic parameters agriculture land, water resources, roads, and structure.
