Improving contrast for the detection of archaeological vegetation marks using optical remote sensing techniques.

Airborne archaeological prospection in arable crops relies on detecting features using contrasts in the growth of the overlying crop as a proxy. This is possible because thecomposition of the soil in the features differs from the unmodified subsoil, and this exerts influence on the state of the crop. This influence is expressed as changes in crop canopydensity, structure, and in periods of resource constraint, variations in vegetation stressand vigour. These contrasts are dynamic, and vary temporally with local weather, andspatially with variations in drift geology and land use. This means that the archaeologicalfeatures have no unique spectral signature usable for classification. Rather, contrast isexpressed as relative, local variation in the crop. The extent to which the features are detectable using a particular technique is dependanton the strength of the contrast and the ability of the sensor to resolve it. Current practicerelies heavily on photography in the visible spectrum, but other sensors and processingtechniques have the potential to improve our ability to resolve subtle contrasts. This isimportant, as it affords the opportunity to extend the detection temporally and in soiltypes not normally considered conducive to detection. This work uses multi-temporal spectro-radiometry and ground-based survey to studycontrast at two sites in southern England. From these measurements leaf area index, vegetationindices, the red-edge position, chlorophyll fluorescence and continuum removalof foliar absorption features were derived and compared to evaluate contrast. The knowledgegained from the ground-based surveys was used to inform the analysis of the airbornesurveys. This included the application of vegetation indices to RGB cameras, theuse of multi-temporal and full-waveform LiDAR to detect biomass variations, and the useof various techniques with hyper-spectral imaging spectroscopy. These methods providea demonstrable improvement in contrast, particularly in methods sensitve to chlorophyllfluorescence, which afford the opportunity to record transient and short term contraststhat are not resolved by other sensors

Recent trends and long-standing problems in archaeological remote sensing

The variety and sophistication of data sources, sensors, and platforms employed in archaeological remote sensing have increased significantly over the past decade. Projects incorporating data from UAV surveys, regional and research-driven lidar surveys, the uptake of hyperspectral imaging, the launch of high-temporal revisit satellites, the advent of multi-sensor rigs for geophysical survey, and increased use of structure from motion mean that more archaeologists are engaging with remote sensing than ever. These technological advances continue to drive research in the specialist community and provide reasons for optimism about future applications, but many social and technical obstacles to the integration of remote sensing into archaeological research and heritage management remain. This article addresses the challenges of contemporary archaeological remote sensing by briefly reviewing trends and then focusing on providing a critical overview of the main structural problems. The discussion here concentrates on topics that have dominated the discourse in recent archaeological literature and featured prominently in ongoing fieldwork for the past decade across three broad segments of landscape archaeology: data collection in the field, the current state of data access and archives, and processing and interpretation

LIDAR based semi-automatic pattern recognition within an archaeological landscape

LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Überwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen Gelände oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archäologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwändige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergänzen, können computergestützte Verfahren einige neue Lösungsansätze bieten, die darüber hinaus sogar die Identifizierung von für das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archäologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-Geländemodelle mit archäologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archäologischer Muster zu Bodendenkmalen als digitale „LIDAR-Landschaft“ bewertet. Dabei wird auf möglichst einfache und freie verfügbare algorithmische Ansätze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur großflächigen Erkennung archäologischer Denkmäler zurückgegriffen. Die Dissertation gibt dabei einen umfassenden Überblick über die archäologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer Ansätze den Entwicklungsstand der semiautomatisierten Erkennung archäologischer Strukturen im Rahmen archäologischer Prospektion und Fernerkundungen. Darüber hinaus erläutert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die Qualität der Erkennung von Bodendenkmälern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld für weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren großflächige Untersuchungen zum kulturellen Erbe ermöglicht werden. Resümierend vergleicht sie die analoge und computergestützte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschließend das weitere Potential LIDAR-basierter Mustererkennung in archäologischen Kulturlandschaften

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

Feasibility studies of terrestrial laser scanning in Coastal Geomorphology, Agronomy, and Geoarchaeology

Terrestrial laser scanning (TLS) is a newer, active method of remote sensing for the automatic detection of 3D coordinate points. This method has been developed particularly during the last 20 years, in addition to airborne and mobile laser scanning methods. All these methods use laser light and additional angle measurements for the detection of distances and directions. Thus, several thousands to hundreds of thousands of polar coordinates per second can be measured directly by an automatic deflection of laser beams. For TLS measurements, the coordinates and orientation of the origin of the laser beam can be determined to register different scan positions in a common coordinate system. These measurements are usually conducted by Global Navigation Satellite Systems or total station surveying, but also identical points can be used and data driven methods are possible. Typically, accuracies and point densities of a few centimetres to a few millimetres are achieved depending on the method. The derived 3D point clouds contain millions of points, which can be evaluated in post-processing stages by symbolic or data-driven methods. Besides the creation of digital surface and terrain models, laser scanning is used in many areas for the determination of 3D objects, distances, dimensions, and volumes. In addition, changes can be determined by multi-temporal surveys. The terrestrial laser scanner Riegl LMS Z-420i was used in this work in combination with the Differential Global Positioning System system Topcon Hiper Pro, based on Real Time Kinematic (RTK-DGPS). In addition to the direct position determination of the laser scanner, the position of a self-developed reflector on a ranging pole was measured by the RTK-DGPS system to accurately derive the orientation of each measured point cloud. Moreover, the scanner is equipped with an additional, mounted camera Nikon D200 to capture oriented pictures. These pictures allow colouring the point cloud in true colours and thus allow a better orientation. Furthermore, the pictures can be used for the extraction of detailed 3D information and for texturing the 3D objects. In one of the post-processing steps, the direct georeferencing by RTK-DGPS data was refined using the Multi Station Adjustment, which employs the Iterative Closest Point algorithm. According to the specific objectives, the point clouds were then filtered, clipped, and processed to establish 3D objects for further usage. In this dissertation, the feasibility of the method has been analysed by investigating the applicability of the system, the accuracy, and the post-processing methods by means of case studies from the research areas of coastal geomorphology, agronomy, and geoarchaeology. In general, the measurement system has been proven to be robust and suitable for field surveys in all cases. The surveys themselves, including the selected georeferencing approach, were conducted quickly and reliably. With the refinement of the Multi Station Adjustment a relative accuracy of about 1 cm has been achieved. The absolute accuracy is about 1.5 m, limited by the RTK-DGPS system, which can be enhanced through advanced techniques. Specific post-processing steps have been conducted to solve the specific goals of each research area. The method was applied for coastal geomorphological research in western Greece. This part of the study deals with 3D reconstructed volumes and corresponding masses of boulders, which have been dislocated by high energy events. The boulder masses and other parameters, such as the height and distance to the current sea level, have been used in wave transport equations for the calculation of minimum wave heights and velocities of storm and tsunami scenarios and were compared to each other. A significant increase in accuracy of 30% on average compared with the conventional method of simply measuring the axes was detected. For comparison, annual measurements at seven locations in western Greece were performed over three years (2009-2011) and changes in the sediment budget were successfully detected. The base points of the RTK-DGPS system were marked and used every year. Difficulties arose in areas with high surface roughness and slight changes in the annual position of the laser scanner led to an uneven point density and generated non-existing changes. For this reason, all results were additionally checked by pictures of the mounted camera and a direct point cloud comparison. Similarly, agricultural plants were surveyed by a multi-temporal approach on a field over two years using the stated method. Plant heights and their variability within a field were successfully determined using Crop Surface Models, which represent the top canopy. The spatial variability of plant development was compared with topographic parameters as well as soil properties and significant correlations were found. Furthermore, the method was carried out with four different types of sugar-beet at a higher resolution, which was achieved by increasing the height of the measurement position. The differences between the crop varieties and their growth behaviour under drought stress were represented by the derived plant heights and a relation to biomass and the Leaf Area Index was successfully established. With regard to geoarchaeological investigations in Jordan, Spain, and Egypt, the method was used in order to document respective sites and specific issues, such as proportions and volumes derived from the generated 3D models were solved. However, a full coverage of complexly structured sites, like caves or early settlements is partially prevented by the oversized scanner, slow measurement rates, and the necessary minimum measurement distance. The 3D data can be combined with other data for further research by the common georeference. The selected method has been found suitable to create accurate 3D point clouds and corresponding 3D models that can be used in accordance with the respective research problem. The feasibility of the TLS method for various issues of the case studies was proven, but limitations of the used system have also been detected and are described in the respective chapters. Further methods or other, newer TLS systems may be better suited for specific cases

Airborne laser scanning raster data visualization

This guide provides an insight into a range of visualization techniques for high-resolution digital elevation models (DEMs). It is provided in the context of investigation and interpretation of various types of historical and modern, cultural and natural small-scale relief features and landscape structures. It also provides concise guidance for selecting the best techniques when looking at a specific type of landscape and/or looking for particular kinds of forms.The three main sections – descriptions of visualization techniques, guidance for selection of the techniques, and visualization tools – accompany examples of visualizations, exemplar archaeological and geomorphological case studies, a glossary of terms, and a list of references and recommendations for further reading. The structure facilitates people of different academic background and level of expertise to understand different visualizations, how to read them, how to manipulate the settings in a calculation, and choose the best suited for the purpose of the intended investigation.A smaller amount of books is also available in hardcover (ISBN 978-961-05-0011-7, 24 EUR).Monografija nudi vpogled v nabor tehnik prikaza visokoločljivih modelov višin. Napisana je v kontekstu preučevanja in interpretacije različnih tipov zgodovinskih in modernih, kulturnih in naravnih majhnih reliefnih oblik. Daje jedrnate napotke za izbiro najboljših tehnik prikaza določenih tipov pokrajine in izrazitih oblik.Tri glavna poglavja – opis tehnik prikazovanja digitalnih modelov višin, napotki za njihovo izbiro in orodja za izračun prikazov –, spremljajo izbrani primeri tipičnih arheoloških in geomorfoloških študij, slovarček pojmov ter seznam literature in priporočenega branja. Posameznikom z različnih znanstvenih področij in z različnim predznanjem o tematiki je struktura v pomoč pri razumevanju različnih tehnik prikazov, kako jih brati, kako izbrati prave nastavitve pri njihovem izračunu in kako prepoznati najbolj primerne za namen zasnovane raziskave

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application