Improving point of entry database through data science: Deep learning-based identification of unmapped roads using remote sensing images

Abstract

This Dissertation proposes an end-to-end framework for map creation using a data science approach to address the data gap identified in the International Organization for Migration’s (IOM) COVID-19 Impact on Points of Entry program. Leveraging satellite imagery and deep learning techniques, the study aims to identify relevant nodes within complex networks of land roads and borders to augment the Points of Entry database, focusing on Angola as a proof-of-concept area. Initial tasks involve data collection, namely satellite imagery extraction from ArcGIS Pro and the manual creation of corresponding ground truth data. Subsequently, train and test datasets are prepared, with images divided into (64 × 64) pixel pieces. The test dataset exclusively comprises data from Angola’s border area, while the training dataset includes images from within the country’s boundaries. The Dissertation’s training phase encompasses two main sections: Custom-Built and Pre-Trained models. Six custom-built Convolutional Neural Network (CNN) architectures are designed, alongside experiments using pre-trained models (MobileNetV1 and ResNet50), pre-trained with the ImageNet dataset, with fine-tuning applied. The best-performing model is based on a CNN, consisting of two blocks with two convolutional and one pooling layers, correctly identifies 47 Points of Entry, enhancing the database from 7 to 47 Points of Entry. By integrating data science methods with satellite imagery, the study aims to provide automated mechanisms for identifying relevant nodes in complex networks, empowering humanitarian and governmental stakeholders to monitor, make informed decisions and respond efficiently to emerging challenges.

Recorrendo a imagens de sat´elite e t´ecnicas de aprendizagem profunda, esta Disserta¸c˜ao visa identificar interse¸c˜oes entre estradas e fronteiras terrestres, com vista `a automatiza ¸c˜ao do processo de atualiza¸c˜ao da base de dados de Pontos de Entrada, originalmente desenvolvida pelo programa “COVID-19 Impact on Points of Entry” da International Organization for Migration. Utilizando Angola como ´area estudo, a Disserta¸c˜ao prop˜oe uma abordagem baseada em classifica¸c˜ao de imagens. Para isso, inicialmente extra´ıram-se imagens de sat´elite do ArcGIS Pro, em Angola, e criaram-se manualmente as legendas correspondentes. Posteriormente, foram criados conjuntos de dados de treino e teste, com as imagens divididas em peda¸cos de (64×64) pixels. O conjunto de teste cont´em exclusivamente imagens da zona fronteiri¸ca de Angola, enquanto o conjunto de treino inclui imagens internas aos limites do pa´ıs. Criaram-se seis arquiteturas baseadas em Redes Neuronais Convolucionais (CNN) e utilizaram-se modelos pr´e-treinados com os dados da ImageNet (MobileNetV1 e ResNet50), com o prop´osito de investigar a melhor abordagem. V´arias experiˆencias foram desenvolvidas, recorrendo a cada arquitetura. O modelo que atingiu o melhor desempenho ´e baseado numa CNN personalizada, composta por dois blocos com duas camadas convolucionais e uma camada de pooling. Este identifica corretamente 47 Pontos de Entrada, melhorando a base de dados de 7 para 47 pontos. A integra¸c˜ao de m´etodos de ciˆencia de dados com imagens de sat´elite, visa fornecer uma proposta mais automatizada para identificar novos Pontos de Entrada terrestres, relevante para a capacita¸c˜ao de organiza¸c˜oes humanit´arias e governamentais na monitoriza¸c˜ao, tomada de decis˜oes e resposta a crises.