Automated selection of topographic base information for thematic maps

Modern GIS are capable of producing well designed maps but offer little assistance to users with little cartographc knowledge. Maps which are produced by such users may have a lot of cartographic errors and be of poor design. Thus, it is very necessary to build cartographic knowledge into GIS to help users to make effective use of such programs and produce basic maps conforming to basic principles of design. One possible way of improving map design in GIS is to build cartographic knowledge into the system. One particular area where such cartographic knowledge could be applied is in the selection of base (topographic) information for special topic maps. The selection will depend upon map topic, map purpose, map scale, and the amount of detail required for the particular map. A topographic database at 1:250 000 has been used to starting point for this study and the scale of output maps limited to the 1:250 000 to 1:1000 000 range. To build a knowledge base of map content, published maps have been examined, and two aspects have been considered: maps with the same topic at different scales; and maps at the same scale but with different topics. For further development to the knowledge base, a questionnaire has been sent to cartographers and expert map users to determine what they consider should be the map content for maps on a range of topics at several scales. An initial examination of the knowledge base produced from the survey of published mapping highlights some anomalies, but by using the knowledge of the cartographers and map users, the knowledge base is revised. To apply this knowledge, a formula for selecting appropriate base information is tested and the results show that the approach does produce satisfactory results. It is suggested this is implemented within a GIS to allow users to focus on the analysis data, with maps produced having appropriate base information depending on the topic, scale and the required level of detail automatically

Classification and Segmentation of Galactic Structuresin Large Multi-spectral Images

Extensive and exhaustive cataloguing of astronomical objects is imperative for studies seeking to understand mechanisms which drive the universe. Such cataloguing tasks can be tedious, time consuming and demand a high level of domain specific knowledge. Past astronomical imaging surveys have been catalogued through mostly manual effort. Immi-nent imaging surveys, however, will produce a magnitude of data that cannot be feasibly processed through manual cataloguing. Furthermore, these surveys will capture objects fainter than the night sky, termed low surface brightness objects, and at unprecedented spatial resolution owing to advancements in astronomical imaging. In this thesis, we in-vestigate the use of deep learning to automate cataloguing processes, such as detection, classification and segmentation of objects. A common theme throughout this work is the adaptation of machine learning methods to challenges specific to the domain of low surface brightness imaging.We begin with creating an annotated dataset of structures in low surface brightness images. To facilitate supervised learning in neural networks, a dataset comprised of input and corresponding ground truth target labels is required. An online tool is presented, allowing astronomers to classify and draw over objects in large multi-spectral images. A dataset produced using the tool is then detailed, containing 227 low surface brightness images from the MATLAS survey and labels made by four annotators. We then present a method for synthesising images of galactic cirrus which appear similar to MATLAS images, allowing pretraining of neural networks.A method for integrating sensitivity to orientation in convolutional neural networks is then presented. Objects in astronomical images can present in any given orientation, and thus the ability for neural networks to handle rotations is desirable. We modify con-volutional filters with sets of Gabor filters with different orientations. These orientations are learned alongside network parameters during backpropagation, allowing exact optimal orientations to be captured. The method is validated extensively on multiple datasets and use cases.We propose an attention based neural network architecture to process global contami-nants in large images. Performing analysis of low surface brightness images requires plenty of contextual information and local textual patterns. As a result, a network for processing low surface brightness images should ideally be able to accommodate large high resolu-tion images without compromising on either local or global features. We utilise attention to capture long range dependencies, and propose an efficient attention operator which significantly reduces computational cost, allowing the input of large images. We also use Gabor filters to build an attention mechanism to better capture long range orientational patterns. These techniques are validated on the task of cirrus segmentation in MAT-LAS images, and cloud segmentation on the SWIMSEG database, where state of the art performance is achieved.Following, cirrus segmentation in MATLAS images is further investigated, and a com-prehensive study is performed on the task. We discuss challenges associated with cirrus segmentation and low surface brightness images in general, and present several tech-niques to accommodate them. A novel loss function is proposed to facilitate training of the segmentation model on probabilistic targets. Results are presented on the annotated MATLAS images, with extensive ablation studies and a final benchmark to test the limits of the detailed segmentation pipeline.Finally, we develop a pipeline for multi-class segmentation of galactic structures and surrounding contaminants. Techniques of previous chapters are combined with a popu-lar instance segmentation architecture to create a neural network capable of segmenting localised objects and extended amorphous regions. The process of data preparation for training instance segmentation models is thoroughly detailed. The method is tested on segmentation of five object classes in MATLAS images. We find that unifying the tasks of galactic structure segmentation and contaminant segmentation improves model perfor-mance in comparison to isolating each task

The evaluation of Corona and Ikonos satellite imagery for archaeological applications in a semi-arid environment

Archaeologists have been aware of the potential of satellite imagery as a tool almost since the first Earth remote sensing satellite. Initially sensors such as Landsat had a ground resolution which was too coarse for thorough archaeological prospection although the imagery was used for geo-archaeological and enviro-archaeological analyses. In the intervening years the spatial and spectral resolution of these sensing devices has improved. In recent years two important occurrences enhanced the archaeological applicability of imagery from satellite platforms: The declassification of high resolution photography by the American and Russian governments and the deregulation of commercial remote sensing systems allowing the collection of sub metre resolution imagery. This thesis aims to evaluate the archaeological application of three potentially important resources; Corona space photography and Ikonos panchromatic and multispectral imager). These resources are evaluated in conjunction with Landsat Thematic Mapper (TM) imagery over a 600 square km study area in the semi-arid environment around Homs, Syria. The archaeological resource in this area is poorly understood, mapped and documented. The images are evaluated for their ability to create thematic layers and to locate archaeological residues in different environmental zones. Further consideration is given to the physical factors that allow archaeological residues to be identified and how satellite imagery and modern technology may impact on Cultural Resource Management. This research demonstrates that modern high resolution and historic satellite imagery can be important tools for archaeologists studying in semi-arid environments. The imagery has allowed a representative range of archaeological features and landscape themes to be identified. The research shows that the use of satellite imagery can have significant impact on the design of the archaeological survey in the middle-east and perhaps in other environments

Landscape classification using GIS and national digital databases

This study considers whether visual landscape character can be classified using GIS. Landscape classification is needed to give landscape researchers and planners a frame of reference for communicating and comparing their research. Such classification is difficult because of the complex nature of landscapes and because it must be explicit. Classification needs to be based on theory, but there is a distinct lack of landscape theory. It is argued that to effectively develop landscape theory a classification is required and that a classification evolves with theory. GIS provides a suitable platform to facilitate this evolution. A set of criteria is established to which a landscape classification should adhere. To be useful for evaluative and cognitive research, a landscape classification needs to distinguish the important characteristics that affect landscape. These characteristics are identified from what little landscape theory exists: a landscape classification needs to incorporate landform, vegetation, naturalness, and water; the classes should be based on the public's perception; the classes should be general and involve compositions; and the classes should incorporate movement and exploration. Besides these criteria, more general criteria that have been used on other land based classifications also apply, particularly the need for a classification to be repeatable. GIS and national digital databases can incorporate these criteria in a landscape classification and this is demonstrated on a transect of the South Island of New Zealand, using mainly a 1:250,000 topographical database and a vegetation database. Difficulties associated with these databases are discussed. A three-phase landscape classification process is developed: 1) Selection of attributes, 2) Definition and classification of the attributes to six levels of generalisation, and 3) Creation of landscape classes from compositions of the attributes. The sensitivity of the process to different operational definitions is considered, and it was significant in some cases. An important analysis function that enables GIS to classify landscapes is the focal neighbourhood function. This in effect analyses the study area from many different points. Once a landscape classification is developed, it can be used with GIS for description, mapping, and inventory purposes. Uniqueness and variety of landscapes can also be determined. A range of observer perspectives can be recognized in the classification by using an application of fuzzy set theory that incorporates entropy. Automating landscape classification requires developing appropriate operational definitions that balance the human concept model of landscapes, the characteristics of national digital databases, and GIS capabilities. Operational definitions can be formulated using four abstractions: classification, generalisation, association, and aggregation, and then represented using GIS analysis techniques. Classifying landscapes automatically is an exercise in generalisation, as there is a considerable amount of information to consider. The challenge is to produce a meaningful generalised classification, rather than a very detailed classification. Expressing association is also important because landscapes are a composition of different landscape components. Focal neighbourhood functions enable the spatial influence of different components to be expressed and from this landscape compositions can be identified. The national digital databases used in this study do not contain conceptualised information on morphological landforms. Height contour databases are available from which it is possible to classify landforms and a substantial part of this study investigates this. Hammond's manual landform classification was automated and applied to the study area. Some problems were identified and a modified process was subsequently developed

What does the honeybee see? And how do we know?

This book is the only account of what the bee, as an example of an insect, actually detects with its eyes. Bees detect some visual features such as edges and colours, but there is no sign that they reconstruct patterns or put together features to form objects. Bees detect motion but have no perception of what it is that moves, and certainly they do not recognize “things” by their shapes. Yet they clearly see well enough to fly and find food with a minute brain. Bee vision is therefore relevant to the construction of simple artificial visual systems, for example for mobile robots. The surprising conclusion is that bee vision is adapted to the recognition of places, not things. In this volume, Adrian Horridge also sets out the curious and contentious history of how bee vision came to be understood, with an account of a century of neglect of old experimental results, errors of interpretation, sharp disagreements, and failures of the scientific method. The design of the experiments and the methods of making inferences from observations are also critically examined, with the conclusion that scientists are often hesitant, imperfect and misleading, ignore the work of others, and fail to consider alternative explanations. The erratic path to understanding makes interesting reading for anyone with an analytical mind who thinks about the methods of science or the engineering of seeing machines

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

The applications of aerial photography, photogrammetry and photo-interpretation in the planning process

To date aerial photography and associated photogrammetric and photo-interpretation techniques have played but a limited role in the planning process. In this study their dual role (i) as a base medium and (ii) as a source of data is investigated bearing in mind the requirements of planning data and certain inherent defects of conventional maps in the planning process. Having considered certain pertinent technical aspects of aerial photography and associated techniques, especially modern developments such as orthophotos, use of multi-emulsion photography, automated data extraction and automated data processing techniques, the application of these techniques is discussed in greater detail in respect of the dual role mentioned earlier. Aerial photographs are shown to be of considerable value to the planner as an analytic tool and a powerful source of data when dealing with such topics as feasibility studies, land use, resource surveys, urban and regional research and analysis, urban history, urban and rural administration, site evaluation, transportation and other. branches of engineering, urban sociology and economics, as well as urban aesthetics. Aerial photographic data adequately meets the data requirements of the planning process and furthermore lends itself to modern automatic data processing methods. The modern improved forms of photography, i.e. photomaps, orthophotos, etc. have definite advantages over conventional maps insofar as a base medium in planning is concerned, and the wider use of aerial photographs and products is anticipated when planners become more aware of their universal application and versatility