Benchmark aplicado à Deteção de Objetos de Mamoas Arqueológicas a partir de dados LiDAR

• Autor(a) principal: Silva, Miguel Ribeiro Vilar Brás da
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/23988
• topic: Archaeology; mounds; LiDAR; object detection; Domínio/Área Científica::Engenharia e Tecnologia

Human history and its archaeological evidence are priceless and should be preserved, esteemed and respected. However, the traditional work of an archaeologist is mainly manual labour, sluggish and requires specialized knowledge as well as considerable experience, which represents quite a limitation due to the available community of archaeologists. Besides this fact, concerns about global warming, the generalized rise of sea levels or destruction due to human activities, among others, contribute to a growing fear of losing some archaeological sites as the traditional method of identification and preservation of these sites can’t keep up with the propagation speed of such problems. Because of this, a growing willingness to implement Artificial Intelligence techniques has been evidenced, which allows some help to the archaeologist in several tasks, with particular focus to archaeological sitting identification, through remote detection. Currently, there are no applications or tools that can execute such work, however, there has been a growing effort in studies and work on a scientific and academic level. This thesis aims to implement a tool that, through LiDAR data readings, gathered from some geographical area, can perform object detection to specific archaeological findings (such as mounds), testing a variety of machine learning models to, assigning a classification, determine if it’s in the presence of an archaeological mound. The input of the work done for this thesis consists of a Digital Terrain Model (DTM), a Local Relief Model (LRM) and a Slope obtained from drone flights over Viana do Castelo, with the use of LiDAR sensors. The combination of these three images was processed to achieve a single image with higher identification of certain features for future model training. For comparison reasons, two datasets were built with different margin sizes around each archaeological mound. The goal of the thesis is to perform tests on some object detection architectures, compare the efficiency of their evaluations and be able to determine which of the tested models performs a better prediction result on detecting the presence of an archaeological mound. This study was able to perform the comparison of a total of nine Deep Learning (DL) architectures, testing four two-stage detectors and five one-stage detectors. As expected, most of the two-stage detectors outperformed the one-stage detectors in terms of mean average precision for the detection of archaeological mounds, except for the one stage detector Fully Convolutional One-Stage (FCOS), which achieved the highest mean average precision from all, showing results between 68.1% to 78.6% for both size dataset.