Poverty Detection using Deep Learning and Image Processing

Abstract

Abstract: Predicting poverty level from satellite imagery is a challenging task that has recently been tackled using deep neural networks. The goal of this task is to use satellite images of a specific area to predict the poverty level of the inhabitants living in that area.One approach to solving this task is to use DNN, which are a type of neural network that are well-suited for image classification tasks. The DNNs take in the satellite images as input and extract features from them using a series of convolutional layers. These layers are designed to learn the patterns and features present in the images, such as the presence of roads, buildings, light. Once the DNNs have extracted the relevant features from the images, they pass them through fully connected layers to make the final poverty level prediction. To train these neural networks, a large dataset of satellite images and their corresponding poverty level labels is required. This dataset is used to train the network to make predictions on new, unseen images. The performance of the network can be evaluated using metrics such as accuracy, precision, and recall. Overall, deep neural networks are a powerful tool for predictingpoverty level from satellite imagery, as they are able to automatically learn the relevant features from the images and make accurate predictions.However, the accuracy of the predictions can be limited by the quality andsize of the training dataset.