1,721,007 research outputs found
Machine learning for the detection of archaeological sites from remote sensor data
No full textDeep learning for automated detection of archaeological sites (objects) on remote sensing data is a highly novel field. The key challenge of this field is in the inherent nature of the objects; they occur in small numbers, are sparsely located and feature a unique pattern on the different remote sensing data modalities. To this extent we identify three main contributions, (1) to include multi-sensor data, (2) to optimise Convolutional Neural Networks (CNNs) for small datasets and, (3) to optimise detection of the sparsely located objects. Our results demonstrate that deep learning can be successfully applied to detect archaeological sites on each of the individual remote sensing images, that our efforts to optimise CNNs for small datasets are successful, and that we have discovered new sites that were missed in a manual data analysis and field survey. We have optimised a workflow for the detection of new archaeological sites. We also share the first large-scale publicly available dataset archaeological image classification and object detection along with benchmarks of the most promising models that we applied in this thesis
A future perspective for automation in large mapping projects with feature learning
No full textEven though the discussion on automation has balanced towards the believers, the available methods that have been published over the years are yet to be generally adopted in research and national mapping projects. In this paper a future perspective will be proposed on how we could improve the applicability of models and make these independent of particular objects, landscapes and type of remote sensing data. Especially with increasing project size in both variety of objects and input of remote sensing data more flexibility in models are required. These models would choose feature learning over feature engineering where the characteristics of the objects are no longer designed by the creator but learned from data. This approach will be exemplified with the design of a model for a national mapping agency which will not be optimised for one region but could instead be used across different landscape and object types. The eventual model should be able to adapt and include new site locations or remote sensing data to further improve its accuracy. In the end this future perspective is meant to inspire big projects to aim for long term solutions and thereby preferably include a feature learning approach
A future perspective on automation in remote sensing
No full textIn the UK there is a longstanding tradition for the use of remote sensing to detect archaeological sites. As research in the UK has been mostly pioneering in the field it is surprising that the upcoming automation practises have not been adopted in research nor practise. Automation has been mostly criticised for the lack in accuracy but recent successes in computer science could overturn this. The key factor for this change is deep learning which has already been overwhelmingly successful in other domains such as self-driving cars and medical imagery. This paper will present how the archaeological field could benefit from these techniques in especially large mapping projects.Examples will be drawn from the ImageLearn project developed by the Ordnance Survey (OS) and the Electronics and Computer Science Department at the University of Southampton. In this project the high resolution aerial imagery and extensive set of labelled data from the OS was successfully used to automatically generate land cover classification. For the next phase of this project archaeological object detection will be studied as it provides a unique opportunity to test this model on some of most ‘overwritten’ signatures within our landscape
Tackling the small data problem in deep learning with multi-sensor approaches
No full textWithin data science, many problems are solved using machine learning. Recently, with the introduction of deep learning, we see this trend spread out across industries of which archaeological object detection on remote sensor data is a case in point. From the known case studies, we have identified the main issues and developed improvements accordingly. The main issue of archaeological datasets is that there are only a limited number of known sites which makes the networks prone to overfit. Overfitting happens when a network is trained on too few examples and learns patterns that do not generalize well to new data. To an extent, data augmentation can be used to prevent overfitting, however, the training images would still be highly correlated. Therefore, it is argued that the most effect can be gained by limiting storage of irrelevant features in networks. This can be done by optimising network architectures and additionally by using transfer learning in which pre-trained network are used to initialise training. Regardless of pre-training on datasets without archaeological sites, its trained network can still be useful for the low-level features (including lines and edges). A downside of pre-trained networks is that they can only work with data in the same format as they had been trained with. Our main contribution is the research into including multi-sensor data. We will present approaches to train networks using images with stacks of data, apply fusion networks and by generating pre-trained networks for the available data of different sensors
A future perspective for automated detection of archaeology using deep learning with remote sensor data
No full textAn essential aspect of archaeology is the protection of sites from looters, extensive agriculture and erosion. Under this constant threat of destruction, it is of utmost importance that sites are located so that they can be monitored and protected. This is mostly done on the ground or by using remote sensing data such as aerial images or LiDAR derived elevation models. This task is time consuming and requires highly specialised and experienced people and would thus immensely benefit from automation. Within this novel research, the potential of deep learning for the detection of archaeological sites is being assessed
Automated detection of archaeology in the New Forest using deep learning with remote sensor data
No full textAs a result of the New Forest Knowledge project, many new sites were discovered. This was partly due to the undertaken LiDAR survey which was followed by an intensive manual process to interpret the results. The research presented in this paper looks at methods to automate this process especially for round barrow detection using deep learning.Traditionally, automated methods require manual feature engineering to extract the visual appearance of a site on remote sensing data. Whereas this approach is difficult, expensive and bound to detect a single type of site, recent developments have moved towards automated feature learning of which deep learning is the most notable. In our approach, we use known site locations together with LiDAR data and aerial images to train Convolutional Neural Networks (CNNs). This network is typically constructed of many layers with each representing a different filter (e.g. to detect lines or edges). When this network is trained, each new site location that is fed to the network will update the weights of features to better represent the appearance of sites in the remote sensing data. For this learning process, an accurate dataset is required with a lot of examples and therefore the New Forest is a very suitable case study, especially thanks to the extensive research of the New Forest Knowledge project.In this paper, our latest results will be presented together with a future perspective on how we can scale our approach to a country wide detection method when computing power becomes even more efficient
Automation on steroids: an exploration of why deep learning is dominating automation
No full textTraditionally, research initiatives into automated detection of archaeological objects were focussed on feature engineering to detect individual object types. These methods have been criticised for their lack in accuracy which is mostly caused by their inability to capture the variability within an object type and the objects’ appearance across different land cover types. Recently, rather than further optimizing features, research has shifted towards feature learning which offers more flexibility. This shift was triggered by the overwhelming successes of deep learning (shown for e.g. self-driving cars and medical imagery). A deep convolutional neural network is build-up out of many layers and learns features from images of known objects which are fed to the network. In the early layers of a network only basic abstractions such as lines and edges are learned and as the deeper layers are reached the features get more refined and are able to extract the key characteristics of the object type. This process is very similar to how a human learns although there are some important advantages to the structure of deep networks. For example, they can be designed to incorporate different types of remote sensor data and can hence internally compare this variety of data. In his manner a network will quickly identify obvious false positives and adapt the weights of the layers accordingly. Another important point is that a network can fully appreciate the small variation of pixel values without any image enhancements. For LiDAR data this effect can be demonstrated with a network that identifies a slope in the first layers of the network and later on learns that the slope direction and local relief are important features for a specific object type. The above listed approaches just scratch the surface of the wide range of possible methods to using deep learning for aerial archaeology. In the end, the shift in research is mainly driven by the far-future concept of a national model which automatically retrains with newly acquired remote sensing data to allow for new discoveries that can further improve the networks
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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