The work presented here has been carefully selected, edited, and peer reviewed in order to advance research and development, as well as to encourage innovative applications of Geomatics technologies in global change studies. The book will appeal not only to academicians, but also to professionals, politicians and decision makers who wish to learn from the very latest and most innovative, quality research in this area of global change and natural disaster management.
/divContributions are drawn from revised submissions based on state-of-the-art papers from the 7th GiT4NDM - 5th EOGC, 2015 event.
Global Change studies are increasingly considered a vital source of information to understand the Earth Environment, especially in the framework of human-induced, climate change and land use transformation. Satellite Earth Observing systems and geomatics technologies provide a unique tool to monitor and model the changes, respectively. While the range of applications and innovative techniques are always increasing, this book provides a summary of key study cases where satellite data offers critical information to understand the usefulness of the geomatics technologies and global environmental issues. Geomatics technologies provide powerful tools to model and analyze the effects of those global environmental changes towards minimizing their adverse impacts on human health and the environment.
The book covers a broad range of topics including photogrammetric mapping and LiDAR remote sensing for generating high quality topographic products, global digital elevation models, current methods for shoreline mapping, and the identification and classification of residential buildings. Contributors also showcase cutting-edge developments for environmental and ecological mapping, including assessment of urbanization patterns, mapping vegetation cover, monitoring invasive species, and mapping marine oil spills—crucial for monitoring this significant environmental hazard.
The authors exemplify the information presented in this text with case studies from around the world. Examples include:
Envisat/ERS-2 images used to generate digital elevation models over northern Alaska In situ radiometric observations and MERIS images employed to retrieve chlorophyll a concentration in inland waters in Australia ERS-1/2 SAR images utilized to map spatiotemporal deformation in the southwestern United States
Aerospace sensors and related information extraction techniques that support various mapping applications have recently garnered more attention due to the advances in remote sensing theories and technologies. This book brings together top researchers in the field, providing a state-of-the-art review of some of the latest advancements in remote sensing and mapping technologies.
The aim of this volume is to share technological advances that allow wider, faster and more effective utilization of geospatial information in emergency response situations. The volume describes current accomplishments and challenges in providing geospatial information with these attributes, and is organized in six parts:
- Practice and legislation, with a focus on the utilization of geospatial information in recent disaster events, as well as resulting legislative attempts to share and access data.
- Data collection and data products.
- Data management and routing in 3D.
- Emerging technologies, including positioning, virtual reality and simulation models.
- Integration of heterogeneous data.
- Applications and solutions.
This volume is aimed at researchers, practitioners and students who work in the variety of disciplines related to geospatial information technology for emergency response, and represents the very best of current thinking from a number of pioneering studies over the past four years.
The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.
Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.
Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:A thorough introduction to OpenCVGetting input from camerasTransforming imagesSegmenting images and shape matchingPattern recognition, including face detectionTracking and motion in 2 and 3 dimensions3D reconstruction from stereo visionMachine learning algorithms
Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.
This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.What You Will LearnInstall and adopt TensorFlow in your Python environment to solve mathematical problemsGet to know the basic machine and deep learning conceptsTrain and test neural networks to fit your data modelMake predictions using regression algorithmsAnalyze your data with a clustering procedureDevelop algorithms for clustering and data classificationUse GPU computing to analyze big dataIn Detail
Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.
This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.
By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.Style and approach
Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.
Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.Learn techniques used in robot navigation, medical image analysis, and other computer vision applicationsWork with image mappings and transforms, such as texture warping and panorama creationCompute 3D reconstructions from several images of the same sceneOrganize images based on similarity or content, using clustering methodsBuild efficient image retrieval techniques to search for images based on visual contentUse algorithms to classify image content and recognize objectsAccess the popular OpenCV library through a Python interface
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.
This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.Learn OpenCV data types, array types, and array operationsCapture and store still and video images with HighGUITransform images to stretch, shrink, warp, remap, and repairExplore pattern recognition, including face detectionTrack objects and motion through the visual fieldReconstruct 3D images from stereo visionDiscover basic and advanced machine learning techniques in OpenCV
It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.
This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth EditionSolved examples in Matlab, including real-life data sets in imaging and audio recognitionAvailable separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning.
3D Printing with Delta Printers contains detailed descriptions of the innovative delta design including unique hardware, software, and maintenance requirements. The book also covers tips for building your own delta printer as well as examples of common enhancements.
This book will enable you to build, configure, and enhance your delta printer. The topics covered will reveal the often-mysterious nuances of the delta design that will enable your printer to compete with the best of what your 3D printer friends can build.
This book introduces key game production concepts in an artist-friendly way, and rapidly teaches the basic scripting skills you'll need with Unity. It goes on to show how you, as an independent game artist, can create interactive games, ideal in scope for today's casual and mobile markets, while also giving you a firm foundation in game logic and design.
The first part of the book explains the logic involved in game interaction, and soon has you creating game assets through simple examples that you can build upon and gradually expand. In the second part, you'll build the foundations of a point-and-click style first-person adventure game—including reusable state management scripts, dialogue trees for character interaction, load/save functionality, a robust inventory system, and a bonus feature: a dynamically configured maze and mini-map. With the help of the provided 2D and 3D content, you'll learn to evaluate and deal with challenges in bite-sized pieces as the project progresses, gaining valuable problem-solving skills in interactive design. By the end of the book, you will be able to actively use the Unity 3D game engine, having learned the necessary workflows to utilize your own assets. You will also have an assortment of reusable scripts and art assets with which to build future games.
· Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
· Many more diagrams included--now in two color--to provide greater insight through visual presentation
· Matlab code of the most common methods are given at the end of each chapter.
· More Matlab code is available, together with an accompanying manual, via this site
· Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms.
· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.
See What’s New in the Fourth Edition:
Up-to-date information on GNSS and GPS modernization Changes in hardware, software, and procedures Comprehensive treatment of novel signals on new blocks of satellites (L5 and L2C)
The book minimizes your reliance on mathematical explanations and maximizes use of illustrations and examples that allow you to visualize and grasp key concepts. The author explains the progression of ideas at the foundation of satellite positioning and delves into some of the particulars. He keeps presentation practical, providing a guide to techniques used in GPS, from their design through observation, processings, real-time kinematic (RTK), and real-time networks. These features and more make it easier for you to meet the challenge of keeping up in this field.
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
New features of the 2nd Edition:Contains more than 1000 new terms, notably an increased focus on image processing and machine vision terms; Includes the addition of reference links across the majority of terms pointing readers to further information about the concept under discussion so that they can continue to expand their understanding; Now available as an eBook with enhanced content: approximately 50 videos to further illustrate specific terms; active cross-linking between terms so that readers can easily navigate from one related term to another and build up a full picture of the topic in question; and hyperlinked references to fully embed the text in the current literature.
Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text.
The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
If you want to do computational photography and computer vision on Apple's mobile devices, then this book is for you. No previous experience with app development or OpenCV is required. However, basic knowledge of C++ or Objective-C is recommended.What You Will LearnUse Xcode and Interface Builder to develop iOS appsObtain OpenCV's standard modules and build extra modules from sourceControl all the parameters of the iOS device's cameraCapture, save, and share photos and videosAnalyze colors, shapes, and textures in ordinary and specialized photographsBlend and compare images to create special photographic effects and augmented reality toolsDetect faces and morph facial featuresClassify coins and other objectsIn Detail
iOS Application Development with OpenCV 3 enables you to turn your smartphone camera into an advanced tool for photography and computer vision. Using the highly optimized OpenCV library, you will process high-resolution images in real time. You will locate and classify objects, and create models of their geometry. As you develop photo and augmented reality apps, you will gain a general understanding of iOS frameworks and developer tools, plus a deeper understanding of the camera and image APIs.
After completing the book's four projects, you will be a well-rounded iOS developer with valuable experience in OpenCV.Style and approach
The book is practical, creative, and precise. It shows you the steps to create and customize five projects that solve important problems for beginners in mobile app development and computer vision. Complete source code and numerous visual aids are included in each chapter. Experimentation is an important part of the book. You will use computer vision to explore the real world, and then you will refine the projects based on your findings.
Plant Image Analysis: Fundamentals and Applicationsintroduces the basic concepts of image analysis and discusses various techniques in plant imaging, their applications, and future potential. Several types of imaging techniques are discussed including RGB, hyperspectral, thermal, PRI, chlorophyll fluorescence, ROS, and chromosome imaging. The book also covers the use of these techniques in assessing plant growth, early detection of disease and stress, fruit crop yield, plant chromosome analysis, plant phenotyping, and nutrient status both in vivo and in vitro.
The book is an authoritative guide for researchers and those teaching in the fields of stress physiology, precision agriculture, agricultural biotechnology, and cell and developmental biology. Graduate students and professionals using machine vision in plant science will also benefit from this comprehensive resource.
Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges:Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions.
This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.
The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects.
What’s new in the Second Edition:
Additional material on geographically-weighted statistics and local regression approaches A better overview of local models with reference to recent critical reviews about the subject area Expanded coverage of individual methods and connections between them Chapters have been restructured to clarify the distinction between global and local methods A new section in each chapter references key studies or other accounts that support the book Selected resources provided online to support learning
An introduction to the methods and their underlying concepts, the book uses worked examples and case studies to demonstrate how the algorithms work their practical utility and range of application. It provides an overview of a range of different approaches that have been developed and employed within Geographical Information Science (GIScience). Starting with first principles, the author introduces users of GISystems to the principles and application of some widely used local models for the analysis of spatial data, including methods being developed and employed in geography and cognate disciplines. He discusses the relevant software packages that can aid their implementation and provides a summary list in Appendix A.
Presenting examples from a variety of disciplines, the book demonstrates the importance of local models for all who make use of spatial data. Taking a problem driven approach, it provides extensive guidance on the selection and application of local models.
Written by an internationally renowned expert in the field, An Introduction to Contemporary Remote Sensing introduces the latest developments in remote sensing and imaging science. This book covers basic principles and discusses essential technologies, such as aerial photography, radar, Lidar (light detection and ranging), photogrammetry, satellites, thermal radiation, and much more. Full-color images illustrate the concepts presented, and review questions at the end of each chapter help reinforce learning. This detailed resource provides the solid foundation in remote sensing required for developing diverse geospatial applications.
Coverage includes:Geographic information system (GIS) Global positioning system (GPS), mobile mapping, and Google Earth Electromagnetic radiation principles Characteristics of remotely sensed data Spatial, spectral, radiometric, and temporal resolution Aerial photo interpretation Photogrammetry Remote sensors Earth observation satellites Digital image analysis Thermal remote sensing Active remote sensing
Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a new skill set, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating material. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life.
In A Mind for Numbers, Dr. Oakley lets us in on the secrets to learning effectively—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. The learning strategies in this book apply not only to math and science, but to any subject in which we struggle. We all have what it takes to excel in areas that don't seem to come naturally to us at first, and learning them does not have to be as painful as we might think!
From the Trade Paperback edition.
Understanding Surveillance Technologies is profusely illustrated with extensive references and indexes, laying the groundwork for more specialized texts on specific technologies. It covers each of the major sensing devices and explains how they are used in civilian, criminal justice, scientific, national security, and military applications. It also includes recent changes in legislation and the organization of the intelligence community, and discusses how equal access to high-tech sensing devices and encryption schemes has changed society.
Understanding Surveillance Technologies is modular, so the chapters can be read in any order, and is written in an accessible, somewhat narrative style, to suit the needs of journalists/newscasters, privacy organizations, educators, civic planners, and technology centers. It is appropriate as an adjunct reference for criminal justice/law enforcement/military, and forensic trainees, and as a textbook for courses in Surveillance Studies, Sociology, Communications, and Political Science. Now in its second edition, with 1,000 pages and more than 700 diagrams, it is still the only text that comprehensively conveys the breadth of the field.
1) Historic Air and Spaceborne Imagery
2) Multispectral and Hyperspectral Imagery
3) Synthetic Aperture Radar
5) Archaeological Site Detection and Modeling
Each of these five sections includes two or more case study applications that have enriched understanding of archaeological landscapes in regions including the Near East, East Asia, Europe, Meso- and North America. Targeted to the needs of researchers and heritage managers as well as graduate and advanced undergraduate students, this volume conveys a basic technological sense of what is currently possible and, it is hoped, will inspire new pioneering applications.
Particular attention is paid to the tandem goals of research (understanding) and archaeological heritage management (preserving) the ancient past. The technologies and applications presented can be used to characterize environments, detect archaeological sites, model sites and settlement patterns and, more generally, reveal the dialectic landscape-scale dynamics among ancient peoples and their social and environmental surroundings. In light of contemporary economic development and resultant damage to and destruction of archaeological sites and landscapes, applications of air and spaceborne technologies in archaeology are of wide utility and promoting understanding of them is a particularly appropriate goal at the 40th anniversary of the World Heritage Convention.
The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.
Whether you want to conduct scientific experiments, run engineering tests, or present an orbital art project, you’ll select basic components such as an antenna, radio transmitter, solar cells, battery, power bus, processor, sensors, and an extremely small picosatellite chassis. This entertaining series takes you through the entire process—from planning to launch.Prototype and fabricate printed circuit boards to handle your payloadChoose a prefab satellite kit, complete with solar cells, power system, and on-board computerCalculate your power budget—how much you need vs. what the solar cells collectSelect between the Arduino or BasicX-24 onboard processors, and determine how to use the radio transmitter and sensorsLearn your launch options, including the providers and cost requiredUse milestones to keep your project schedule in motion