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Program Earth
Sensors are everywhere. Small, flexible, economical, and computationally powerful, they operate ubiquitously in environments. They compile massive amounts of data, including information about air, water, and climate. Never before has such a volume of environmental data been so broadly collected or so widely available.
Grappling with the consequences of wiring our world,Program Earthexamines how sensor technologies are programming our environments. As Jennifer Gabrys points out, sensors do not merely record information about an environment. Rather, they generate new environments and environmental relations. At the same time, they give a voice to the entities they monitor: to animals, plants, people, and inanimate objects. This book looks at the ways in which sensors converge with environments to map ecological processes, to track the migration of animals, to check pollutants, to facilitate citizen participation, and to program infrastructure. Through discussing particular instances where sensors are deployed for environmental study and citizen engagement across three areas of environmental sensing, from wild sensing to pollution sensing and urban sensing,Program Earthasks how sensor technologies specifically contribute to new environmental conditions. What are the implications for wiring up environments? How do sensor applications not only program environments, but also program the sorts of citizens and collectives we might become?
Program Earthsuggests that the sensor-based monitoring of Earth offers the prospect of making new environments not simply as an extension of the human but rather as new \"technogeographies\" that connect technology, nature, and people.
eBook
Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology
This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province, and efficient data coverage of the whole Hainan Province was realized using the grid data method, which significantly reduced the amount of remote sensing data and processing time required. This shows that the method has successfully improving data coverage capacity and utilization efficiency. The results of this study not only demonstrate the effective application of data-like cubes in remote sensing data processing and analysis, but also provide new perspectives and methods for future complex spatial data analysis and large-scale remote sensing data processing.
Journal Article
Computer processing of remotely-sensed images : an introduction
This fourth and full colour edition updates and expands a widely-used textbook aimed at advanced undergraduate and postgraduate students taking courses in remote sensing and GIS in Geography, Geology and Earth/Environmental Science departments. Existing material has been brought up to date and new material has been added. In particular, a new chapter, exploring the two-way links between remote sensing and environmental GIS, has been added. New and updated material includes: A website at www.wiley.com/go/mather4 that provides access to an updated and expanded version of the MIPS image processing software for Microsoft Windows, PowerPoint slideshows of the figures from each chapter, and case studies, including full data sets, Includes new chapter on Remote Sensing and Environmental GIS that provides insights into the ways in which remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, New section on image processing from a computer science perspective presented in a non-technical way, including some remarks on statistics, New material on image transforms, including the analysis of temporal change and data fusion techniques, New material on image classification including decision trees, support vector machines and independent components analysis, and Now in full colour throughout. This book provides the material required for a single semester course in Environmental Remote Sensing plus additional, more advanced, reading for students specialising in some aspect of the subject. It is written largely in non-technical language yet it provides insights into more advanced topics that some may consider too difficult for a non-mathematician to understand. The case studies available from the website are fully-documented research projects complete with original data sets. For readers who do not have access to commercial image processing software, MIPS provides a licence-free, intuitive and comprehensive alternative.
eBook
Open cities, open data : collaborative cities in the information era
\"Today the world's largest economies and corporations trade in data and its products to generate value in new disruptive markets. Within these markets vast streams of data are often inaccessible or untapped and controlled by powerful monopolies. Counter to this exclusive use of data is a promising world-wide \"open-data\" movement, promoting freely accessible information to share, reuse and redistribute. The provision and application of open data has enormous potential to transform exclusive, technocratic \"smart cities\" into inclusive and responsive \"open-cities\". This book argues that those who contribute urban data should benefit from its production. Like the city itself, the information landscape is a public asset produced through collective effort, attention, and resources. People produce data through their engagement with the city, creating digital footprints through social medial, mobility applications, and city sensors. By opening up data there is potential to generate greater value by supporting unforeseen collaborations, spontaneous urban innovations and solutions, and improved decision-making insights. Yet achieving more open cities is made challenging by conflicting desires for urban anonymity, sociability, privacy and transparency. This book engages with these issues through a variety of critical perspectives, and presents strategies, tools and case studies that enable this transformation.\"--Publisher's description.
Book
Geographical information systems : trends and technologies
\"Preface Geographical Information Systems (GIS) since its inception in the late 1960s have seen an increasing rate of theoretical, technological and organizational development. Developments in each decade of the last 50 years highlight particular innovations in this fi eld. The mid 1960s witnessed the initial development of GIS in combining spatially referenced data, spatial data models and data visualization. The early 1970s witnessed the ability of computer mapping in automatic map drafting and using data format. In the 1980s, computer mapping capabilities have been merged with traditional database management systems capabilities to generate spatial database management systems. Accordingly, the ability to select, sort, extract, classify and display geographic data on the basis of complex topological and statistical criteria was available to users. The 1990s saw map analysis and modeling advances in GIS, and these systems became real management information tools as computing power increased. During this decade, the Open GIS Consortium, aimed at developing publicly available geoprocessing specifi cations, was founded. Since 2000, with the advent of Web 2.0, mobile, and wireless technologies, GIS have been moving towards an era in which the power of such systems is continuously increasing in multiple facets consisting of computing, visualizing, mining, reasoning data. The latest changes in technologies and trends have brought new challenges and opportunities in GIS domain. Specifi cally, mobile and internet devices, Cloud computing, NoSQL databases, Semantic Web, Web services offer new ways of accessing, analyzing, and elaborating geospatial information in both real-world and virtual spaces\"-- Provided by publisher.
Book
ScienceEarth: A Big Data Platform for Remote Sensing Data Processing
Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.
Journal Article