Open Territory

Territory, as an incipient design setting, is progressively displacing conventional notions of site within design research and practice, and, with this, the design professions are increasingly exploring their agency as instruments of territorial intervention, formation and reformation; a disciplinary shift witnessed in recent discourses such as Landscape Urbanism, Ecological Urbanism, and Ecological Design. With this renewed contextual perspective, complexity is acknowledged as a base condition, accompanied by an operative shift toward geographical contexts, techniques, and representations which foreground systems-oriented perspectives with process-driven approaches. Similarly, a pivotal shift in focus from the essence of objects to the management of dynamic spatial systems is increasingly taking root. Yet, the specific methods, tools and techniques used to operate within this expanding field of practice are deserving of further exploration in their own right, and it is this point that serves as the primary motivation for this thesis. As such, the thesis proposes a methodological framework which operates at the intersection of territorial design research and computational thinking, proposing the use of methods, techniques and tools drawn from spatial data mining, machine learning, and computational modelling as mechanisms for dealing with complexity in territorial systems. The driving motivation in the development of this framework is to eliminate the gap between contextual analysis and the development of a design response, by exploring ways in which the data which is used to characterize a design context can be carried directly through to inform a design process. The framework, offered as a black-box system, is examined by way of a specific implementation, using historical data from the 2011 Japan Earthquake and Tsunami as the basis for a design experiment. After exploring each phase of the framework – Discovery, Modelling, Formation & Exploration – the challenges and limitations of appropriating extra-disciplinary devices, and the role of subjectivity in computational modelling are discussed. Lastly, looking forward, a recursive implementation of the proposed framework is proposed as an avenue for future research and development

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

New techniques for the automatic registration of microwave and optical remotely sensed images

Remote sensing is a remarkable tool for monitoring and mapping the land and ocean surfaces of the Earth. Recently, with the launch of many new Earth observation satellites, there has been an increase in the amount of data that is being acquired, and the potential for mapping is greater than ever before. Furthermore, sensors which are currently operational are acquiring data in many different parts of the electromagnetic spectrum. It has long been known that by combining images that have been acquired at different wavelengths, or at different times, the ability to detect and recognise features on the ground is greatly increased. This thesis investigates the possibilities for automatically combining radar and optical remotely sensed images. The process of combining images, known as data integration, is a two step procedure: geometric integration (image registration) and radiometric integration (data fusion). Data fusion is essentially an automatic procedure, but the problems associated with automatic registration of multisource images have not, in general, been resolved. This thesis proposes a method of automatic image registration based on the extraction and matching of common features which are visible in both images. The first stage of the registration procedure uses patches as the matching primitives in order to determine the approximate alignment of the images. The second stage refines the registration results by matching edge features. Throughout the development of the proposed registration algorithm, reliability, robustness and automation were always considered priorities. Tests with both small images (512x512 pixels) and full scene images showed that the algorithm could successfully register images to an acceptable level of accuracy

Earth Resources: A continuing bibliography with indexes, issue 15, October 1977

This bibliography lists 387 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Aeronautical engineering: A continuing bibliography with indexes (supplement 323)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports