Deep Learning aplicado a imágenes satelitales como herramienta de detección de viviendas sin servicio de energía en el caserı́o Media Luna-Uribia-Guajira

Abstract

In this thesis project, a Deep Learning application is developed, specifically a tool for detection of homes without utility services on the satellite map of the village of Media Luna, located in the municipality of Uribia, north of La Guajira. Using a dataset composed of satellite images of homes in different rural areas of Colombia, obtained through Google Earth, two different prediction models are developed, a comparison of these models is made with the aim of minimizing the prediction error. Different technologies were used to solve the problem, including TensorFlow and Keras for the creation of neural networks, with their respective configurations. Convolutionary Neural Networks are proposed and a pre-trained Keras model called VGG16 with a ReLu activation function. The experiments carried out show that the use of Convolutional Networks and the algorithms presented have an acceptable and more efficient performance than the traditional methods applied for the VSS counting in rural areas, with reasonable processing times and speed in the delivery of the required information.