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386 Research products

  • Digital Humanities and Cultural Heritage
  • 2023-2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Richard-Trémeau Emma; Betts John Charles; Brogan Catriona;

    This collection of photographs was compiled as part of the MaltaPot project at the University of Malta. This project aims to enhance the understanding of pottery technology and provenancein Neolithic Malta, mainly dating to the Għar Dalam, Skorba (Early Neolithic), and Zebbuġ (Late Neolithic) phases. This collection presents sherds from the Għar Dalam phase, photographs, and microphotographs and lists their archaeological contexts and form. This collection was prepared thanks to the information from the National Museum of Archaeology (NMA), Malta, and the FRAGSUS project. The project used multiple techniques to characterise the pottery sherds, such as microscopy, polarised light microscope, X-Ray Fluorescence or X-Ray Diffraction. This collection presents the sherds which were not analysed using these destructive techniques, although they had a section ground flat for microphotography. Data collection was carried out between 2018-2020 by Dr Brogan. The document was compiled by 2023 Ms Richard-Trémeau. Photographs can be used if credited. This upload contains a PDF document and two zip files with the macroscopic photograph (Exterior surface) and the microphotographs.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Fiser Jaromir;

    This deliverable reports on the work carried out in Task 6.2 of WP6 (actions for preventive protection - subtask to Task 6.2), which concerns preliminary actions for preventive protection, namely measurement devices and data loggers installation, in selected representative sites (museum building, storage hangar open or closed, possibly roofed).

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Source: The Visits of Elizabeth : ELTeC Edition

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    Source: The Professor: A Tale : ELTec edition : ELTeC Edition

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Balbo Andrea;

    This report describes the activities carried out within the WP4 - Development of protective coating for outdoor exposure

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Silva, Miguel Ribeiro Vilar Brás da;

    A história da humanidade e as suas evidências arqueológicas são inestimáveis e devem ser preservadas, respeitadas e valorizadas. No entanto, o trabalho tradicional de um arqueólogo é principalmente uma tarefa manual, lenta e requer conhecimento especializado, bem como considerável experiência, o que representa uma limitação significativa devido à disponibilidade limitada de arqueólogos. Além disso, preocupações com o aquecimento global, o aumento generalizado do nível do mar ou a destruição devido a atividades humanas, entre outras, contribuem para um crescente receio de perder alguns sítios arqueológicos, já que o método tradicional de identificação e preservação desses sítios não consegue acompanhar a velocidade de propagação de tais problemas. Decorrente destes factos, aliado a uma tendência generalizada e com sucesso no recurso a técnicas de Inteligência Artificial em outras especialidades, também na Arqueologia tem-se vindo a verificar uma adesão significativa. A adoção de técnicas de Inteligência Artificial tem permitido alguma ajuda aos arqueólogos em várias tarefas, com especial foco na identificação de sítios arqueológicos através do recurso a métodos de deteção remota. Atualmente, não existem aplicações ou ferramentas que possam executar este trabalho, no entanto, tem-se verificado um esforço crescente de estudo e desenvolvimento de trabalho nesse sentido, quer ao nível académico quer científico. Esta tese tem como objetivo implementar uma ferramenta que, através da leitura de dados LiDAR, coletados de uma determinada área geográfica, consiga efetuar uma deteção de objetos referentes a vestígios arqueológicos específicos (mamoas), recorrendo a uma variedade de modelos de machine learning, atribuindo uma classificação para determinar se identificou ou não com sucesso a presença de uma mamoa. O ponto de partida do trabalho realizado nesta tese inicia-se com o acesso e trabalho realizado sobre três técnicas de visualização aplicada sobre dados LiDAR, nomeadamente consiste no acesso a ficheiros como Digital Terrain Model (DTM), Local Relief Model (LRM) e Slope. Estes dados LiDAR e consequente conversão nas técnicas de visualização anteriormente citadas ocorreram a partir de voos de drones, equipados com sensores LiDAR que, sobrevoando a zona de Viana do Castelo, proporcionou a obtenção de tais dados. Adicionalmente aos três ficheiros de técnicas de visualização, foi também disponibilizado um ficheiro shape que fornece informação georreferenciada da localização de mamoas na área sobrevoada pelos drones. Com recurso ao software QGIS, foi possível identificar que, as localizações das mamoas encontravam-se relativamente concentradas numa parte específica das imagens. Desta forma, e considerando o tamanho dos ficheiros em questão, efetuou-se uma seleção nas imagens, cortando áreas que já apresentassem uma distância considerável da mamoa mais próxima, de forma a tornar mais ágil o processo de trabalho e treino dos modelos escolhidos. Posteriormente, e com as imagens em tamanho mais reduzido, efetuou-se uma operação de combinação entre as três tipologias de imagens, obtendo uma única imagem onde, incorporando as características destas, permitiu realçar determinados aspetos com intuito de, posteriormente, auxiliar nas tarefas de treino e teste dos modelos de aprendizagem profunda a que foram aplicados. Seguiu-se o processo de pré-processamento de dados tendo sido definido e trabalhado um programa que executasse a mesma tarefa, fornecendo como output um dataset em formato COCO, formato escolhido dada popularidade e sucesso verificado na aplicação a métodos de deteção de objetos. A construção deste dataset foi igualmente realizada de forma a criar estrutura de ficheiros que, respeitando na mesma o formato COCO, proporcionasse a aplicação da técnica de leave-one-out cross-validation, uma vez que, foi considerado a melhor opção dada existência de apenas 77 mamoas, de forma a evitar cenários de enviesamento de dados ou até overfitting. Para diversificar e enriquecer esta análise comparativa, foram criados dois datasets diferentes, cujas bounding boxes em volta das mamoas apresentavam tamanhos diferentes, nomeadamente 15x15 metros e 30x30 metros. Como o objetivo da tese é a realização de testes em algumas arquiteturas de deteção de objeto, foi utilizada um projeto que, está precisamente preparado e desenvolvido para a realização de análises de benchmark, de várias metodologias de classificação de imagem, nas quais estão incluídas as de deteção de objeto. Esta biblioteca permitiu a realização do estudo comparativo não só entre as arquiteturas analisadas e identificadas como as mais promissoras e populares na análise de estado de arte, como ainda permitiu a comparação com outras arquiteturas dada a variedade de oferta de modelos que a mesma proporcionava. Este estudo conseguiu realizar a comparação com um total de nove arquiteturas de aprendizagem profunda, testando quatro detetores two-stage e cinco detetores one-stage. Como era esperado, a maioria dos detetores two-stage superou os detetores one-stage em termos de precisão média de deteção de mamoas, com exceção do modelo Fully Convolutional One-Stage (FCOS), que alcançou a maior precisão média de todos os modelos testados, apresentando resultados entre 68,1% e 78,6% em ambos os datasets. Igualmente esperado foi a confirmação do modelo one-stage Single Shot Detector (SSD) como sendo o modelo com mais rápido tempo de processamento de treino, apesar de, entre os restantes modelos, a diferença de tempo já ser menos significativa e não se notar uma supremacia dos modelos one-stage como seria inicialmente esperado. 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.

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    Authors: van der Heijden, T.;

    How can we document analogue media technologies, like historical film and sound recording technologies, and the tacit knowledge involved in their practices of use? What role can digital technologies – like 3D and 360-degree photography – play in preserving media heritage? Tim van der Heijden (Open University of the Netherlands) discusses these questions while reflecting on the CRAFTED-DEMA dataset recently published on Europeana.eu.

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  • Authors: Department for Digital, Culture, Media and Sport;

    Abstract copyright UK Data Service and data collection copyright owner.The Participation Survey is a continuous push-to-web survey of adults aged 16 and over in England. It serves as a successor to the Taking Part survey, which ran for 16 years as a continuous face to face survey. Paper surveys are available for those not digitally engaged. Fieldwork started in October 2021 and it is envisaged that the survey will be a key evidence source for Department for Digital, Culture, Media and Sport (DCMS) and its sectors by providing statistically representative national estimates of adult engagement with the DCMS sectors. The survey’s main objectives are to: Provide a central, reliable evidence source that can be used to analyse cultural, digital, and sporting engagement, providing a clear picture of why people do or do not engage. Provide data at a county level to meet user needs, including providing evidence for the levelling up agenda. Underpin further research on driving engagement and the value and benefits of engagement.Further information on the survey can be found on the gov.uk Participation Survey webpage. Three versions of the Participation Survey 2022-2023 are available:An open access version (SN 9126). This version is freely available to download and does not require UK Data Service registration. This safeguarded dataset (SN 9125), which includes some additional detail. It is only available to registered UKDS users who have agreed to abide by the conditions of the End User Licence. A Secure Access version containing all years (SN 9014), which contains further detailed information. Access to this version is very restricted and requires UKDS registration, completion of an extensive application form, approval from the depositor, and successful completion of a Safe Researcher Training course before access can be granted. Users are advised to first download the safeguarded version (SN 9125) to check whether it includes sufficient detail for their research, before considering making an application for the Secure Access version.Details of all variables available for the version concerned can be found in the UKDS Data Dictionary - see the Documentation section. Main Topics:The Participation Survey collects data on engagement in: the arts libraries heritage museums and galleries tourism major cultural events major sporting events sport gambling digital sectors The survey includes information on frequency of participation, reasons for participating, barriers to participation and attitudes to the sectors. Information is also gathered on demographics (e.g. age, education), and related areas including wellbeing, loneliness, and use of digital technology. Multi-stage stratified random sample Self-administered questionnaire: Web-based (CAWI) Self-administered questionnaire: Paper

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  • Authors: Office for National Statistics;

    Abstract copyright UK Data Service and data collection copyright owner. The Annual Survey of Hours and Earnings, 2020: Synthetic Data Pilot is a synthetic version of the Annual Survey of Hours and Earnings (ASHE) study available via Trusted Research Environments (TREs). ASHE is one of the most extensive surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete. ASHE is available for research projects demonstrating public good to accredited or approved researchers via TREs such as the Office for National Statistics Secure Research Service (SRS) or the UK Data Service Secure Lab (at SN 6689). To access collections stored within TREs, researchers need to undergo an accreditation process. Gaining access to data in a secure environment can be time and resource intensive. This pilot has created a low fidelity, low disclosure risk synthetic version of ASHE data, which can be made available to researchers more quickly while they wait for access to the real data.The synthetic data were created using the Synthpop package in R. The sample method was used; this takes a simple random sample with replacement from the real values. The project was carried out in the period between 19th December 2022 and 3rd January 2023. Further information is available within the documentation. User feedback received through this pilot will help the ONS to maximise benefits of data access and further explore the feasibility of synthesising more data in future. Main Topics: The ASHE synthetic data contain the same variables as ASHE for each individual, relating to wages, hours of work, pension arrangements, and occupation and industrial classifications. There are also variables for age, gender and full/part-time status. Because ASHE data are collected by the employer, there are also variables relating to the organisation employing the individual. These include employment size and legal status (e.g. public company). Various geography variables are included in the data files. The year variable in this synthetic dataset is 2020. Simple random sample Compilation/Synthesis

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    Authors: Hansen, Lasse; Enevoldsen, Kenneth;

    TextDescriptives is a Python package for calculating a large variety of statistics from text. It is built on top of spaCy and can be easily integrated into existing workflows. The package has already been used for analysing the linguistic stability of clinical texts, creating features for predicting neuropsychiatric conditions, and analysing linguistic goals of primary school students. This paper describes the package and its features.

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    Authors: Richard-Trémeau Emma; Betts John Charles; Brogan Catriona;

    This collection of photographs was compiled as part of the MaltaPot project at the University of Malta. This project aims to enhance the understanding of pottery technology and provenancein Neolithic Malta, mainly dating to the Għar Dalam, Skorba (Early Neolithic), and Zebbuġ (Late Neolithic) phases. This collection presents sherds from the Għar Dalam phase, photographs, and microphotographs and lists their archaeological contexts and form. This collection was prepared thanks to the information from the National Museum of Archaeology (NMA), Malta, and the FRAGSUS project. The project used multiple techniques to characterise the pottery sherds, such as microscopy, polarised light microscope, X-Ray Fluorescence or X-Ray Diffraction. This collection presents the sherds which were not analysed using these destructive techniques, although they had a section ground flat for microphotography. Data collection was carried out between 2018-2020 by Dr Brogan. The document was compiled by 2023 Ms Richard-Trémeau. Photographs can be used if credited. This upload contains a PDF document and two zip files with the macroscopic photograph (Exterior surface) and the microphotographs.

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    Authors: Fiser Jaromir;

    This deliverable reports on the work carried out in Task 6.2 of WP6 (actions for preventive protection - subtask to Task 6.2), which concerns preliminary actions for preventive protection, namely measurement devices and data loggers installation, in selected representative sites (museum building, storage hangar open or closed, possibly roofed).

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    Source: The Visits of Elizabeth : ELTeC Edition

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    Source: The Professor: A Tale : ELTec edition : ELTeC Edition

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    Authors: Balbo Andrea;

    This report describes the activities carried out within the WP4 - Development of protective coating for outdoor exposure

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    Authors: Silva, Miguel Ribeiro Vilar Brás da;

    A história da humanidade e as suas evidências arqueológicas são inestimáveis e devem ser preservadas, respeitadas e valorizadas. No entanto, o trabalho tradicional de um arqueólogo é principalmente uma tarefa manual, lenta e requer conhecimento especializado, bem como considerável experiência, o que representa uma limitação significativa devido à disponibilidade limitada de arqueólogos. Além disso, preocupações com o aquecimento global, o aumento generalizado do nível do mar ou a destruição devido a atividades humanas, entre outras, contribuem para um crescente receio de perder alguns sítios arqueológicos, já que o método tradicional de identificação e preservação desses sítios não consegue acompanhar a velocidade de propagação de tais problemas. Decorrente destes factos, aliado a uma tendência generalizada e com sucesso no recurso a técnicas de Inteligência Artificial em outras especialidades, também na Arqueologia tem-se vindo a verificar uma adesão significativa. A adoção de técnicas de Inteligência Artificial tem permitido alguma ajuda aos arqueólogos em várias tarefas, com especial foco na identificação de sítios arqueológicos através do recurso a métodos de deteção remota. Atualmente, não existem aplicações ou ferramentas que possam executar este trabalho, no entanto, tem-se verificado um esforço crescente de estudo e desenvolvimento de trabalho nesse sentido, quer ao nível académico quer científico. Esta tese tem como objetivo implementar uma ferramenta que, através da leitura de dados LiDAR, coletados de uma determinada área geográfica, consiga efetuar uma deteção de objetos referentes a vestígios arqueológicos específicos (mamoas), recorrendo a uma variedade de modelos de machine learning, atribuindo uma classificação para determinar se identificou ou não com sucesso a presença de uma mamoa. O ponto de partida do trabalho realizado nesta tese inicia-se com o acesso e trabalho realizado sobre três técnicas de visualização aplicada sobre dados LiDAR, nomeadamente consiste no acesso a ficheiros como Digital Terrain Model (DTM), Local Relief Model (LRM) e Slope. Estes dados LiDAR e consequente conversão nas técnicas de visualização anteriormente citadas ocorreram a partir de voos de drones, equipados com sensores LiDAR que, sobrevoando a zona de Viana do Castelo, proporcionou a obtenção de tais dados. Adicionalmente aos três ficheiros de técnicas de visualização, foi também disponibilizado um ficheiro shape que fornece informação georreferenciada da localização de mamoas na área sobrevoada pelos drones. Com recurso ao software QGIS, foi possível identificar que, as localizações das mamoas encontravam-se relativamente concentradas numa parte específica das imagens. Desta forma, e considerando o tamanho dos ficheiros em questão, efetuou-se uma seleção nas imagens, cortando áreas que já apresentassem uma distância considerável da mamoa mais próxima, de forma a tornar mais ágil o processo de trabalho e treino dos modelos escolhidos. Posteriormente, e com as imagens em tamanho mais reduzido, efetuou-se uma operação de combinação entre as três tipologias de imagens, obtendo uma única imagem onde, incorporando as características destas, permitiu realçar determinados aspetos com intuito de, posteriormente, auxiliar nas tarefas de treino e teste dos modelos de aprendizagem profunda a que foram aplicados. Seguiu-se o processo de pré-processamento de dados tendo sido definido e trabalhado um programa que executasse a mesma tarefa, fornecendo como output um dataset em formato COCO, formato escolhido dada popularidade e sucesso verificado na aplicação a métodos de deteção de objetos. A construção deste dataset foi igualmente realizada de forma a criar estrutura de ficheiros que, respeitando na mesma o formato COCO, proporcionasse a aplicação da técnica de leave-one-out cross-validation, uma vez que, foi considerado a melhor opção dada existência de apenas 77 mamoas, de forma a evitar cenários de enviesamento de dados ou até overfitting. Para diversificar e enriquecer esta análise comparativa, foram criados dois datasets diferentes, cujas bounding boxes em volta das mamoas apresentavam tamanhos diferentes, nomeadamente 15x15 metros e 30x30 metros. Como o objetivo da tese é a realização de testes em algumas arquiteturas de deteção de objeto, foi utilizada um projeto que, está precisamente preparado e desenvolvido para a realização de análises de benchmark, de várias metodologias de classificação de imagem, nas quais estão incluídas as de deteção de objeto. Esta biblioteca permitiu a realização do estudo comparativo não só entre as arquiteturas analisadas e identificadas como as mais promissoras e populares na análise de estado de arte, como ainda permitiu a comparação com outras arquiteturas dada a variedade de oferta de modelos que a mesma proporcionava. Este estudo conseguiu realizar a comparação com um total de nove arquiteturas de aprendizagem profunda, testando quatro detetores two-stage e cinco detetores one-stage. Como era esperado, a maioria dos detetores two-stage superou os detetores one-stage em termos de precisão média de deteção de mamoas, com exceção do modelo Fully Convolutional One-Stage (FCOS), que alcançou a maior precisão média de todos os modelos testados, apresentando resultados entre 68,1% e 78,6% em ambos os datasets. Igualmente esperado foi a confirmação do modelo one-stage Single Shot Detector (SSD) como sendo o modelo com mais rápido tempo de processamento de treino, apesar de, entre os restantes modelos, a diferença de tempo já ser menos significativa e não se notar uma supremacia dos modelos one-stage como seria inicialmente esperado. 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.

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