Classification of Roof Construction Materials using Satellite Images with Convolutional Neural Network

Roof Classification is an approach currently utilized by Southeast Asian countries as a tool in assisting decision makers during the design process of urban policies and development which aims to ease congestion and proper allocation of land properties. However, the Philippines is among the countries who have yet to implement this into its urbanization programs. Data augmentation and SMOTE is performed to address the potential low accuracy issue that limits the existing CNN model for roof classification. A series of experimentation to determine the ideal number of epochs and batch size were performed to acquire the optimal accuracy for the roof classification. Consequently, the model achieved a promising result in terms of its classification performance and accuracy. The CNN model exhibits strong metrics across precision, recall, and F1-score with an average accuracy of 97%.