530 research outputs found

    Insights into temperature controls on rockfall occurrence and cliff erosion

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    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Optimization of neural networks for deep learning and applications to CT image segmentation

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    [eng] During the last few years, AI development in deep learning has been going so fast that even important researchers, politicians, and entrepreneurs are signing petitions to try to slow it down. The newest methods for natural language processing and image generation are achieving results so unbelievable that people are seriously starting to think they can be dangerous for society. In reality, they are not dangerous (at the moment) even if we have to admit we reached a point where we have no more control over the flux of data inside the deep networks. It is impossible to open a modern deep neural network and interpret how it processes the information and, in many cases, explain how or why it gives back that particular result. One of the goals of this doctoral work has been to study the behavior of weights in convolutional neural networks and in transformers. We hereby present a work that demonstrates how to invert 3x3 convolutions after training a neural network able to learn how to classify images, with the future aim of having precisely invertible convolutional neural networks. We demonstrate that a simple network can learn to classify images on an open-source dataset without loss in accuracy, with respect to a non-invertible one. All that with the ability to reconstruct the original image without detectable error (on 8-bit images) in up to 20 convolutions stacked in a row. We present a thorough comparison between our method and the standard. We tested the performances of the five most used transformers for image classification on an open- source dataset. Studying the embedded matrices, we have been able to provide two criteria that can help transformers learn with a training time reduction of up to 30% and with no impact on classification accuracy. The evolution of deep learning techniques is also touching the field of digital health. With tens of thousands of new start-ups and more than 1B $ of investments only in the last year, this field is growing rapidly and promising to revolutionize healthcare. In this thesis, we present several neural networks for the segmentation of lungs, lung nodules, and areas affected by pneumonia induced by COVID-19, in chest CT scans. The architecturesm we used are all residual convolutional neural networks inspired by UNet and Inception. We customized them with novel loss functions and layers studied to achieve high performances on these particular applications. The errors on the surface of nodule segmentation masks are not over 1mm in more than 99% of the cases. Our algorithm for COVID-19 lesion detection has a specificity of 100% and overall accuracy of 97.1%. In general, it surpasses the state-of-the-art in all the considered statistics, using UNet as a benchmark. Combining these with other algorithms able to detect and predict lung cancer, the whole work was presented in a European innovation program and judged of high interest by worldwide experts. With this work, we set the basis for the future development of better AI tools in healthcare and scientific investigation into the fundamentals of deep learning.[spa] Durante los últimos años, el desarrollo de la IA en el aprendizaje profundo ha ido tan rápido que Incluso importantes investigadores, políticos y empresarios están firmando peticiones para intentar para ralentizarlo. Los métodos más nuevos para el procesamiento y la generación de imágenes y lenguaje natural, están logrando resultados tan increíbles que la gente está empezando a preocuparse seriamente. Pienso que pueden ser peligrosos para la sociedad. En realidad, no son peligrosos (al menos de momento) incluso si tenemos que admitir que llegamos a un punto en el que ya no tenemos control sobre el flujo de datos dentro de las redes profundas. Es imposible abrir una moderna red neuronal profunda e interpretar cómo procesa la información y, en muchos casos, explique cómo o por qué devuelve ese resultado en particular, uno de los objetivos de este doctorado. El trabajo ha consistido en estudiar el comportamiento de los pesos en redes neuronales convolucionales y en transformadores. Por la presente presentamos un trabajo que demuestra cómo invertir 3x3 convoluciones después de entrenar una red neuronal capaz de aprender a clasificar imágenes, con el objetivo futuro de tener redes neuronales convolucionales precisamente invertibles. Nosotros queremos demostrar que una red simple puede aprender a clasificar imágenes en un código abierto conjunto de datos sin pérdida de precisión, con respecto a uno no invertible. Todo eso con la capacidad de reconstruir la imagen original sin errores detectables (en imágenes de 8 bits) en hasta 20 convoluciones apiladas en fila. Presentamos una exhaustiva comparación entre nuestro método y el estándar. Probamos las prestaciones de los cinco transformadores más utilizados para la clasificación de imágenes en abierto. conjunto de datos de origen. Al estudiar las matrices incrustadas, hemos sido capaz de proporcionar dos criterios que pueden ayudar a los transformadores a aprender con un tiempo de capacitación reducción de hasta el 30% y sin impacto en la precisión de la clasificación. La evolución de las técnicas de aprendizaje profundo también está afectando al campo de la salud digital. Con decenas de miles de nuevas empresas y más de mil millones de dólares en inversiones sólo en el año pasado, este campo está creciendo rápidamente y promete revolucionar la atención médica. En esta tesis, presentamos varias redes neuronales para la segmentación de pulmones, nódulos pulmonares, y zonas afectadas por neumonía inducida por COVID-19, en tomografías computarizadas de tórax. La arquitectura que utilizamos son todas redes neuronales convolucionales residuales inspiradas en UNet. Las personalizamos con nuevas funciones y capas de pérdida, estudiado para lograr altos rendimientos en estas aplicaciones particulares. Los errores en la superficie de las máscaras de segmentación de los nódulos no supera 1 mm en más del 99% de los casos. Nuestro algoritmo para la detección de lesiones de COVID-19 tiene una especificidad del 100% y en general precisión del 97,1%. En general supera el estado del arte en todos los aspectos considerados, estadísticas, utilizando UNet como punto de referencia. Combinando estos con otros algoritmos capaces de detectar y predecir el cáncer de pulmón, todo el trabajo se presentó en una innovación europea programa y considerado de gran interés por expertos de todo el mundo. Con este trabajo, sentamos las bases para el futuro desarrollo de mejores herramientas de IA en Investigación sanitaria y científica sobre los fundamentos del aprendizaje profundo

    Tools for efficient Deep Learning

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    In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

    Contributions to improve the technologies supporting unmanned aircraft operations

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    Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

    V2VFormer++++: multi-modal vehicle-to-vehicle cooperative perception via global-local transformer

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    Multi-vehicle cooperative perception has recently emerged for facilitating long-range and large-scale perception ability of connected automated vehicles (CAVs). Nonetheless, enormous efforts formulate collaborative perception as LiDAR-only 3D detection paradigm, neglecting the significance and complementary of dense image. In this work, we construct the first multi-modal vehicle-to-vehicle cooperative perception framework dubbed as V2VFormer ++ , where individual camera-LiDAR representation is incorporated with dynamic channel fusion (DCF) at bird’s-eye-view (BEV) space and ego-centric BEV maps from adjacent vehicles are aggregated by global-local transformer module. Specifically, channel-token mixer (CTM) with MLP design is developed to capture global response among neighboring CAVs, and position-aware fusion (PAF) further investigate the spatial correlation between each ego-networked map in a local perspective. In this manner, we could strategically determine which CAVs are desirable for collaboration and how to aggregate the foremost information from them. Quantitative and qualitative experiments are conducted on both publicly-available OPV2V and V2X-Sim 2.0 benchmarks, and our proposed V2VFormer ++ reports the state-of-the-art cooperative perception performance, demonstrating its effectiveness and advancement. Moreover, ablation study and visualization analysis further suggest the strong robustness against diverse disturbances from real-world scenarios

    Topology Reasoning for Driving Scenes

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    Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code would be released soon

    Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

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    Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space

    New Computational Methods for Automated Large-Scale Archaeological Site Detection

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    Aquesta tesi doctoral presenta una sèrie d'enfocaments, fluxos de treball i models innovadors en el camp de l'arqueologia computacional per a la detecció automatitzada a gran escala de jaciments arqueològics. S'introdueixen nous conceptes, enfocaments i estratègies, com ara lidar multitemporal, aprenentatge automàtic híbrid, refinament, curriculum learning i blob analysis; així com diferents mètodes d'augment de dades aplicats per primera vegada en el camp de l'arqueologia. S'utilitzen múltiples fonts, com ara imatges de satèl·lits multiespectrals, fotografies RGB de plataformes VANT, mapes històrics i diverses combinacions de sensors, dades i fonts. Els mètodes creats durant el desenvolupament d'aquest doctorat s'han avaluat en projectes en curs: Urbanització a Hispània i la Gàl·lia Mediterrània en el primer mil·lenni aC, detecció de monticles funeraris utilitzant algorismes d'aprenentatge automàtic al nord-oest de la Península Ibèrica, prospecció arqueològica intel·ligent basada en drons (DIASur), i cartografiat del patrimoni arqueològic al sud d'Àsia (MAHSA), per a la qual s'han dissenyat fluxos de treball adaptats als reptes específics del projecte. Aquests nous mètodes han aconseguit proporcionar solucions als problemes comuns d'estudis arqueològics presents en estudis similars, com la baixa precisió en detecció i les poques dades d'entrenament. Els mètodes validats i presentats com a part de la tesi doctoral s'han publicat en accés obert amb el codi disponible perquè puguin implementar-se en altres estudis arqueològics.Esta tesis doctoral presenta una serie de enfoques, flujos de trabajo y modelos innovadores en el campo de la arqueología computacional para la detección automatizada a gran escala de yacimientos arqueológicos. Se introducen nuevos conceptos, enfoques y estrategias, como lidar multitemporal, aprendizaje automático híbrido, refinamiento, curriculum learning y blob analysis; así como diferentes métodos de aumento de datos aplicados por primera vez en el campo de la arqueología. Se utilizan múltiples fuentes, como lidar, imágenes satelitales multiespectrales, fotografías RGB de plataformas VANT, mapas históricos y varias combinaciones de sensores, datos y fuentes. Los métodos creados durante el desarrollo de este doctorado han sido evaluados en proyectos en curso: Urbanización en Iberia y la Galia Mediterránea en el Primer Milenio a. C., Detección de túmulos mediante algoritmos de aprendizaje automático en el Noroeste de la Península Ibérica, Prospección Arqueológica Inteligente basada en Drones (DIASur), y cartografiado del Patrimonio del Sur de Asia (MAHSA), para los que se han diseñado flujos de trabajo adaptados a los retos específicos del proyecto. Estos nuevos métodos han logrado proporcionar soluciones a problemas comunes de la prospección arqueológica presentes en estudios similares, como la baja precisión en detección y los pocos datos de entrenamiento. Los métodos validados y presentados como parte de la tesis doctoral se han publicado en acceso abierto con su código disponible para que puedan implementarse en otros estudios arqueológicos.This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology. Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’ s specific challenges have been designed. These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision in previous detection studies and how to handle problems with few training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies
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