New Computational Methods for Automated Large-Scale Archaeological Site Detection

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

Aplicación del análisis de componentes principales para la detección y estimación de la alternancia de la onda T en un sistema monoderivacional y multiderivacional

Las alternancias de la onda T se definen como una fluctuación en la amplitud, duración o morfología de la repolarización ventricular repetida cada dos latidos, y actualmente se consideran un posible marcador del riesgo de muerte súbita cardíaca. Se proponen dos sistemas, uno monoderivacional y otro multiderivacional para la detección y estimación de la alternancia de onda T mediante un análisis de componentes principales.Berganzo Besga, I. (2014). Aplicación del análisis de componentes principales para la detección y estimación de la alternancia de la onda T en un sistema monoderivacional y multiderivacional. http://hdl.handle.net/10251/45812.Archivo delegad

Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia

This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available

Potential of Multitemporal Lidar for the Detection of Subtle Archaeological Features under Perennial Dense Forest

This paper presents a method for the merging of lidar-derived point clouds of the same area taken at different moments, even when these are not co-registered. The workflow also incorporates the filtering of vegetation allowing the classification of unclassified point clouds using the ground points of reliable coverages. The objective is to produce a digital terrain model by joining all ground points to generate a higher resolution model than would have been possible using a single coverage. The workflow is supplemented by a multi-scale relief visualisation tool that allows for better detection of archaeological micro-reliefs of variable size even in areas of complex topography. The workflow is tested in six Iberian Iron Age sites, all of them located in mountain areas with dense Mediterranean perennial forests and shrub vegetation

Using LiDAR to detect architectural features in urban sites in the coast of Northern Iberia (6th – 3rd centuries BC). Preliminary results

We present here the first results of an ongoing research project aimed at improving our knowledge of the urban settlements of the north-eastern Iberian Peninsula during the Iron Age. In the 4th-3rd centuries BC, and probably as early as the 6th-5th centuries BC, we detect a strongly hierarchical settlement pattern in this area. It was composed of settlement types that were differentiated by their size and function. The urban sites at the top of the hierarchy are the least known, as their excavation and study present several difficulties, such as the large areas they cover (around 10 hectares) and the fact that most of them lie under dense forest cover that obscures the archaeological remains. This last factor makes it difficult to apply certain non-invasive methods, including geophysical prospection. They are, however, suitable for study by remote sensing techniques. In this paper we discuss the efficiency of those techniques, more specifically the use of lidar data as a method of detecting architectural features in these settlements.Presentem els primers resultats d’una investigació en curs que pretén millorar el coneixement dels assentaments urbans a l’edat de ferro del nord-est de la península Ibèrica. Almenys per als segles IV-III aC, i probablement ja des dels segles VI-V aC, s’ha detectat un patró d’assentament fortament jerarquitzat en aquesta àrea, integrat per diferents tipus de nuclis segons la seva grandària i funció. Els jaciments urbans, al capdamunt de la jerarquia, són precisament els menys coneguts, ja que la seva excavació i estudi presenten diverses dificultats, incloent-hi les grans superfícies que cobreixen (al voltant de 10 ha) i el fet que la majoria d’ells es troben sota una densa capa forestal que oculta les restes arqueològiques.Aquest últim factor fa que sigui difícil aplicar certs mètodes no invasius, com ara la prospecció geofísica.En canvi, són susceptibles de ser estudiats a través de tècniques de teledetecció. En aquest article analitzem l’eficàcia d’aquestes tècniques, més concretament l’ús de les dades lidar, com a mètode per detectar estructures arquitectòniques en aquests assentaments

Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan.

Acknowledgements: The Mapping Archaeological Heritage in South Asia (MAHSA) project is funded by Arcadia, a charitable fund of Lisbet Rausing and Peter Baldwin. This research was also partially supported by Grant PID2021-128945NB-I00, awarded by MCIN/AEI/10.13039/501100011033, and by “ERDF A way of making Europe”. The authors acknowledge the support of the Generalitat de Catalunya CERCA Program to CVC and ICAC. Finally, the authors would like to thank Junaid Abdul Jabbar, Mou Sarmah, Ushni Dasgupta, Azadeh Vafadari, Kuili Suganya Chittiraibalan, Arnau Garcia-Molsosa and Adam Green.Funder: Arcadia, a charitable fund of Lisbet Rausing and Peter BaldwinThis paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map