Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Impact of Data Selection on the Accuracy of Atmospheric Refractivity Inversions Performed over Marine Surfaces

Within the Earth’s atmosphere there is a planetary boundary layer that extends from the surface to roughly 1 km above the surface. Within this planetary boundary layer exists the marine atmospheric boundary layer, which is a complex turbulent surface layer that extends from the sea surface to roughly 100 m in altitude. The turbulent nature of this layer combined with the interactions across the air-sea interface cause ever changing environmental conditions within it, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of these EM systems requires in-situ knowledge of the refractivity. Efforts to inversely obtain refractivity from radar power returns have done so using both reflected sea clutter and bi-static radar approaches. These types of inversion methods are driven by radar measurements. This study applies a bi-static radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of radar propagation loss data quantity and source location on the accuracy of refractivity inversions. Genetic algorithms and the Variable Terrain Radio Parabolic Equation radar propagation model are used to perform the inversions for three refractivity parameters. Numerical experiments are performed to test various randomly distributed amounts of synthetic data from a 100 m altitude by 60 km range domain. To compare the impact of location of data on the inverse solutions, three domains were examined from which data was sourced, including the whole domain (0 m to 100 m altitude and 0 km to 60 km range), a lower domain (0 m to 60 m altitude and 0 km to 60 km range), and a long-range domain (0 m to 100 m altitude and 30 km to 60 km range). Comparisons of inversion performance across experiments involved evaluation of several metrics: fitness scores, fitness-distance-correlations, the root-mean-square-errors of refractivity profiles, and percent errors of each individual refractivity parameter. The results of the data quantity experiments show that propagation loss measurement coverage of approximately 1% of the prediction domain yields the most accurate refractivity estimates. It is concluded that this amount of data is needed to sufficiently eliminate non-unique solutions that were observed using smaller data quantities. The results of the regional study indicate that the long-range domain produced slightly more accurate results with less data compared to the other regions. From the results of these experiments and prior studies, four specific sampling patterns were developed that were hypothesized to generate accurate inversion results. It was shown that the pattern containing the most data cells with the widest spread over the domain generated inversion results with the highest parameter and refractivity accuracy; although, a second pattern that sourced data concentrated in a short range low altitude region performed similarly with significantly less data. The results from this study enable advancement of refractivity inversion techniques by providing insight into where and how many EM measurements are needed for successful refractivity inversions. Improvements in refractivity inversion techniques enable performance improvements of EM sensing and communication technologies

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications