Mapping heterogeneous buried archaeological features using multisensor data from unmanned aerial vehicles

There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs give

Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results

This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs given

Integración geoespacial para mapear asentamientos prehispánicos en los límites del imperio azteca

[EN] Mexico s vast archaeological research tradition has increased with the use of remote sensing technologies; however, this recent approach is still costly in emerging market economies. In addition, the scales of prospection, landscape, and violence affect the type of research that heritage-culture ministries and universities can conduct. In Central Mexico, researchers have studied the pre-Hispanic Settlement Pattern during the Mesoamerican Postclassic (900-1521 AD) within the scope of the Aztec Empire and its conquests. There are settlements indications before and during the rule of the central empire, but the evidence is difficult to identify, particularly in the southwest of the capital, in the transition between the Lerma and Balsas River basins and their political-geographical complexities. This research focuses on a Geographic Information System (GIS)-based processing of multiple source data, the potential prospection of archaeological sites based on spatial data integration from Sentinel-2 optical sensors, Unmanned Aerial Vehicle (UAV), Digital Terrain Model (DTM), Normalized Difference Vegetation Index (NDVI) and field validation. What is revealed is the relationship between terrain morphologies and anthropic modifications. A binary map expresses possible archaeological remnants as a percentage; NDVI pixels and the morphometry values were associated with anthropic features (meso-reliefs with a tendency to regular geometries: slope, orientation, and roughness index); they were then interpreted as probable archaeological evidence. Within archaeological fieldwork, with limited resources (time, funding and staff), this approach proposes a robust method that can be replicated in other mountainous landscapes that are densely covered by vegetation.[ES] México tiene una vasta tradición de investigación arqueológica que, en las últimas décadas, se ha incrementado con el uso de tecnologías de percepción remota; sin embargo, este enfoque sigue siendo costoso en el contexto de las economías emergentes. Además, las escalas de prospección, paisaje e inseguridad influyen en el tipo de investigación que realizan los ministerios de patrimonio cultural y las universidades. En el Centro de México, el Patrón de Asentamiento Prehispánico durante el Posclásico Mesoamericano (900-1521 d.C.), ha sido estudiado dentro del alcance del Imperio Azteca y sus conquistas. Hay indicios de asentamientos antes y durante el dominio del Imperio central, pero la evidencia es difícil de identificar; particularmente en el suroeste de la capital, en la transición entre las cuencas de los ríos Lerma y Balsas y sus complejidades político-geográficas. Esta investigación se centra en el procesamiento basado en GIS de datos de múltiples fuentes, la prospección de sitios arqueológicos apoyada en la integración de datos espaciales de los sensores ópticos Sentinel-2, el vehículo aéreo no tripulado (UAV), el modelo digital del terreno (MDT), el índice de vegetación de diferencia normalizada (NDVI) y la validación de campo, que revelan la relación entre las morfologías del terreno y las modificaciones antrópicas. Un mapa binario expresa los posibles remanentes arqueológicos como un porcentaje; los píxeles del NDVI y los valores de morfometría se asociaron a características antrópicas (mesorrelieves con tendencia a geometrías regulares: pendiente, orientación e índice de rugosidad), y se interpretaron como probable evidencia arqueológica. Dentro del trabajo de campo arqueológico, con recursos limitados (tiempo, finanzas y auxiliares), este enfoque sugiere un método robusto que puede ser replicado en otros paisajes montañosos que están densamente cubiertos por vegetación.Miranda-Gómez, R.; Cabadas-Báez, HV.; Antonio-Némiga, X.; Dávila-Hernández, N. (2022). Geospatial integration in mapping pre-Hispanic settlements within Aztec empire limits. Virtual Archaeology Review. 13(27):49-65. https://doi.org/10.4995/var.2022.161064965132

Big Earth Data for Cultural Heritage in the Copernicus Era

Digital data is stepping in its golden age characterized by an increasing growth of both classical and emerging big earth data along with trans- and multidisciplinary methodological approaches and services addressed to the study, preservation and sustainable exploitation of cultural heritage (CH). The availability of new digital technologies has opened new possibilities, unthinkable only a few years ago for cultural heritage. The currently available digital data, tools and services with particular reference to Copernicus initiatives make possible to characterize and understand the state of conservation of CH for preventive restoration and opened up a frontier of possibilities for the discovery of archaeological sites from above and also for supporting their excavation, monitoring and preservation. The different areas of intervention require the availability and integration of rigorous information from different sources for improving knowledge and interpretation, risk assessment and management in order to make more successful all the actions oriented to the preservation of cultural properties. One of the biggest challenges is to fully involve the citizen also from an emotional point of view connecting “pixels with people” and “bridging” remote sensing and social sensing

Habelos, hainos. Detección remota de indicios arqueolóxicos mediante fotografía aérea e Lidar en castros de Galicia

Several reasons explain why so-called “aerial archaeology” has rarely been developed in Galicia in the past. Nowadays, the increasing availability of open access datasets (aerial orthoimages, satellite imagery, Lidar data) is beginning to change this outlook. This paper reviews these reasons before presenting various traces of potential archaeological features recently documented around a large group of castros (Iron Age hillforts) in the provinces of A Coruña and Lugo. More than one thousand elements have been identified, including buried features around the sites (ditches, possible pathways, and field boundaries...), traces of levelled defensive elements and even twenty-five previously unknown full sites. These findings are serendipitous in nature and carried out only with the help of public, general-purpose datasets, and they provide a good argument to justify the development of future projects in this direction. The use of bespoke platforms, sensors, and imaging might yield very profitable results in the near future.Hay varios factores que explican que la llamada “arqueología aérea” haya tenido un desarrollo muy escaso en Galicia. Hoy en día, la creciente disponibilidad de conjuntos de datos de acceso público (ortofotografías, imágenes de satélite, Lidar) está cambiando este panorama. En este artículo repasamos esos factores y presentamos múltiples indicios de nuevos elementos arqueológicos que han sido identificados en el entorno de un amplio conjunto de castros en las provincias de A Coruña y Lugo. Entre los más de mil elementos identificados hay posibles estructuras enterradas en el entorno de esos castros (fosos, posibles caminos, restos de parcelas...), trazas de los sistemas defensivos e incluso un par de docenas de posibles nuevos castros. Todos estos hallazgos, que se produjeron de forma inicialmente casual y usando solo datos públicos de propósito general, justifican la potencialidad de desarrollar proyectos en esta dirección mediante el uso de plataformas, sensores y planificaciones de toma de imágenes específicamente diseñados con la finalidad de documentar elementos arqueológicos. [gl] Varios factores explican que a chamada “arqueoloxía aérea” teña tido moi pouco desenvolvemento en Galicia. Porén, a crecente dispoñibilidade de fontes de información de acceso público (ortofotografías, imaxes de satélite, Lidar) está a mudar este panorama. Neste artigo repasamos eses factores e presentamos múltiples indicios de novos elementos arqueolóxicos que foron identificados na contorna dun amplo conxunto de castros nas provincias de A Coruña e Lugo. Entre os máis de mil elementos identificados hai posibles estruturas soterradas na contorna dos castros (foxos, posibles camiños, restos de parcelas...), trazas de partes dos sistemas defensivos hoxe desaparecidas e mesmo un par de ducias de posibles novos castros. Todos estes achados, que foron feitos de forma inicialmente casual e usando só datos públicos de propósito xeral, xustifican a potencialidade de desenvolver proxectos nesta dirección mediante o uso de plataformas, sensores e planificacións de toma de imaxes especificamente deseñados coa finalidade de documentar elementos arqueolóxicos

UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research

Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring - A Review

Built cultural heritage is under constant threat due to environmental pressures, anthropogenic damages, and interventions. Understanding the preservation state of monuments and historical structures, and the factors that alter their architectural and structural characteristics through time, is crucial for ensuring their protection. Therefore, inspection and monitoring techniques are essential for heritage preservation, as they enable knowledge about the altering factors that put built cultural heritage at risk, by recording their immediate effects on monuments and historic structures. Nondestructive evaluations with close-range sensing techniques play a crucial role in monitoring. However, data recorded by different sensors are frequently processed separately, which hinders integrated use, visualization, and interpretation. This article’s aim is twofold: i) to present an overview of close-range sensing techniques frequently applied to evaluate built heritage conditions, and ii) to review the progress made regarding the fusion of multi-sensor data recorded by them. Particular emphasis is given to the integration of data from metric surveying and from recording techniques that are traditionally non-metric. The article attempts to shed light on the problems of the individual and integrated use of image-based modeling, laser scanning, thermography, multispectral imaging, ground penetrating radar, and ultrasonic testing, giving heritage practitioners a point of reference for the successful implementation of multidisciplinary approaches for built cultural heritage scientific investigations

Evaluation of segmentation parameters in OBIA for classification of land covers from UAV images

[EN] Unmanned Aerial Vehicles (UAVs) have given a new boost to remote sensing and image classification techniques due to the high level of detail among other factors. Object-based image analysis (OBIA) could improve classification accuracy unlike to pixel-based, especially in high-resolution images. OBIA application for image classification consists of three stages i.e., segmentation, class definition and training polygons, and classification. However, defining the parameters: spatial radius (SR), range radius (RR) and minimum region size (MR) is necessary during the segmentation stage. Despite their relevance, they are usually visually adjusted, which leads to a subjective interpretation. Therefore, it is of utmost importance to generate knowledge focused on evaluating combinations of these parameters. This study describes the use of the mean-shift segmentation algorithm followed by Random Forest classifier using Orfeo Toolbox software. It was considered a multispectral orthomosaic derived from UAV to generate a suburban map land cover in town of El Pueblito, Durango, Mexico. The main aim was to evaluate efficiency and segmentation quality of nine parameter combinations previously reported in scientific studies.This in terms of number generated polygons, processing time, discrepancy measures for segmentation and classification accuracy metrics. Results evidenced the importance of calibrating the input parameters in the segmentation algorithms. Best combination was RE=5, RR=7 and TMR=250, with a Kappa index of 0.90 and shortest processing time. On the other hand, RR showed a strong and inversely proportional degree of association regarding the classification accuracy metrics.[ES] Los Vehículos Aéreos No Tripulados (VANT) han otorgado un nuevo auge a la teledetección y a las técnicas d clasificación de imágenes debido al alto nivel de detalle entre otros factores. El análisis de imágenes basado en objetos (OBIA) puede mejorar la precisión en la clasificación a diferencia de la basada en píxeles, especialmente en imágenes de alta resolución. La aplicación de OBIA para la clasificación de imágenes consta de tres etapas i.e., segmentación, definición de clases y polígonos de entrenamiento y clasificación. No obstante, en la etapa de segmentación es necesario definir los parámetros: radio espacial (RE), radio de rango (RR) y tamaño mínimo de la región (TMR). Los cuales, pese a su relevancia, suelen ser ajustados de manera visual, lo que conlleva a una interpretación subjetiva. Por lo anterior, es de suma importancia generar conocimiento enfocado a evaluar las combinaciones de estos parámetros. Este estudio describe el uso del algoritmo de segmentación de desplazamiento medio, seguido del clasificador Random Forest mediante el software Orfeo Toolbox. Se consideró un ortomosaico multiespectral derivado de VANT para generar un mapa de cobertura de suelo sub-urbano en la localidad El Pueblito, Durango, México. El objetivo principal fue evaluar la eficiencia y calidad de segmentación de nueve combinaciones de parámetros anteriormente reportadas en estudios científicos. Ello en términos de número de polígonos generados, tiempo de procesamiento, medidas de discrepancia de segmentación y métricas de precisión de la clasificación. Los resultados obtenidos lograron evidenciar la importancia de ajustar los parámetros de entrada en los algoritmos de segmentación. La mejor combinación fue RE=5, RR=7 y TMR=250, con un índice de Kappa de 0,90 y el menor tiempo de procesamiento. Por otro lado, el RR presentó  un grado de asociación fuerte e inversamente proporcional con las métricas de precisión de clasificación.Se agradece al Consejo Nacional de Ciencia y Tecnología (Conacyt) por el financiamiento otorgado a la primera autora para la realización de sus estudios de maestría, así como al programa de Maestría en Geomática Aplicada a Recursos Forestales y Ambientales de la Facultad de Ciencias Forestales de la UJED.Hinojosa-Espinoza, SI.; Gallardo-Salazar, JL.; Hinojosa-Espinoza, FJC.; Meléndez-Soto, A. (2021). Evaluación de parámetros de segmentación en OBIA para la clasificación de coberturas del suelo a partir de imágenes VANT. Revista de Teledetección. 0(58):89-103. https://doi.org/10.4995/raet.2021.14782OJS89103058Abburu, S., Golla, S.B. 2015. Satellite image classification methods and techniques: A review. International Journal of Computer Applications, 119(8), 20-25. https://doi.org/10.5120/21088-3779Adelabu, S., Mutanga, O., Adam, E. 2015. 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