Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools. This book is an attempt to identify and rationally organize the statistical-mathematical methods which are common in many fields where geomatics is applied, like geodesy, geophysics and, in particular, the field of inverse problems and image analysis as it enters into photogrammetry and remote sensing. These lecture notes aim at creating a bridge between people working in different disciplines and making them aware of a common methodological basis.
Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools. This book is an attempt to identify and rationally organize the statistical-mathematical methods which are common in many fields where geomatics is applied, like geodesy, geophysics and, in particular, the field of inverse problems and image analysis as it enters into photogrammetry and remote sensing. These lecture notes aim at creating a bridge between people working in different disciplines and making them aware of a common methodological basis.
Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools. This book is an attempt to identify and rationally organize the statistical-mathematical methods which are common in many fields where geomatics is applied, like geodesy, geophysics and, in particular, the field of inverse problems and image analysis as it enters into photogrammetry and remote sensing. These lecture notes aim at creating a bridge between people working in different disciplines and making them aware of a common methodological basis.
The applications of geomatics technology in its broader context have resulted in significant progress in the field of earth science. This book provides brief coverage on some trends in geomatics technology as it relates to earth scientists. The development in geomatics, whether GIS, remote sensing, GPS or photogrammetry, can be seen from trends in the applications of Big Data, Smart City, Internet of Things (IoT), the use of augmented reality and utilization of unmanned aerial vehicles (UAVs) and in the impact of machine learning and AI on geomatics.
Geomatics is a neologism, the use of which is becoming increasingly widespread, even if it is not still universally accepted. It includes several disciplines and te- niques for the study of the Earth’s surface and its environments, and computer science plays a decisive role. A more meaningful and appropriate expression is G- spatial Information or GeoInformation. Geo-spatial Information embeds topography in its more modern forms (measurements with electronic instrumentation, sophisticated techniques of data analysis and network compensation, global satellite positioning techniques, laser scanning, etc.), analytical and digital photogrammetry, satellite and airborne remote sensing, numerical cartography, geographical information systems, decision support systems, WebGIS, etc. These specialized elds are intimately interrelated in terms of both the basic science and the results pursued: rigid separation does not allow us to discover several common aspects and the fundamental importance assumed in a search for solutions in the complex survey context. The objective pursued by Mario A. Gomarasca, one that is only apparently modest, is to publish an integrated text on the surveying theme, containing simple and comprehensible concepts relevant to experts in Geo-spatial Information and/or speci cally in one of the disciplines that compose it. At the same time, the book is rigorous and synthetic, describing with precision the main instruments and methods connected to the multiple techniques available today.
Planetary health involves complex spatial–temporal interactions among agents, hosts, and earth environment. Due to rapid technical development of geomatics, including geographic information systems (GIS) and remote sensing (RS) in the era of big data analytics, therefore, earth data analytics has become one of the important approaches for monitoring earth surface process and measuring of the effects of environment changes on all humans and other living organisms on earth. Various methods in earth data analytics, including spatial–temporal statistics, spatial evolutionary algorithms, remote sensing image analysis, wireless geo-sensors, and location-based analytics, are an emerging discipline in understanding complex interactions in planetary health. This edited book provides a broad focus on methodological theories of earth data analytics and their applications to measuring the process of planetary health, with the goal to build scientific understanding on how geospatial analytics can provide valuable insights in measuring environmental risks in Southeast Asian regions. It is collection of selected papers covering both theoretical and empirical studies focusing on topics relevant to spatial perspectives on planetary health and environmental exposure studies. The book is written for senior undergraduates, graduate students, lecturers, and researchers in applications of geospatial technologies for public health and environmental studies.
A collection of articles on the use of analytical methods in studies concerning geospatial analysis and data integration for cultural heritage evaluation. This volume is presented as a collection of self-contained articles providing easy access to suit the requirements and interests of individual readers. In order to be self-contained, each article is prefaced by a general introduction which briefly provides the theoretical principles and the general background of the discussed methods or techniques. Contents: 1) Airborne Imaging Technology, DTM and MMS in Natural and Cultural Heritage; 2) Open Source Geospatial Analysis for Cultural Landscape Management; 3) GIS Resources for Ecclesiastic Cultural Heritage Documentation; 4) GIS Prototypes and Thermal Characterization for Regional Landscape Plans and Cultural Heritage Master Plan; 5) Mobile GIS for Colour Calibration Procedures; 6) Data Integration for Cultural Landscape and Resource Efficiency; 7) Non Destructive Testing and GIS for Digital Architectural Heritage; 8) Geo-referencing Techniques and Mapping in Cultural and Natural Heritage Analysis; 9) Data Integration for Survey and Conservation; 10) Geospatial and Hyperspectral MIVIS Data for Environmental Analysis; 11) Airborne Imaging Technology, DTM and MMS in Natural and Cultural Heritage; 12) Mobile Investigator and RS for Heritage Protection: the Fire Risk Assessment; 13) Land Use Analysis Achieved by Hyperspectral Evaluation and In Situ Analysis.
The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. New in this edition: Provides comprehensive background on the theory of deep learning and its application to remote sensing data. Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications. Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies. Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models. This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.
This book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.
Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.
