This book covers fundamental aspects of spatial data modelling specifically on the aspect of three-dimensional (3D) modelling and structuring. Realisation of "true" 3D GIS spatial system needs a lot of effort, and the process is taking place in various research centres and universities in some countries. The development of spatial data modelling for 3D objects is the focus of this book.

Urban spaces are being called upon to develop a capacity for resilience and sustainability in order to meet the major challenges they face. To achieve such a goal, a practical development framework must be implemented in order to take advantage of the technological innovations that characterize the field of construction and urban engineering. Today, multi-scale BIM is bringing about significant changes that are redefining the paradigms of urban management. It facilitates simulations of the sustainability of urban spaces with respect to several criteria; most notably relating to energy, the economy and the environment. Building Information Modeling for a Smart and Sustainable Urban Space proposes a theoretical and practical framework for implementing BIM models for the creation of sustainable and intelligent urban spaces. It addresses the issues of acquisition, modeling, interoperability, and BIM and GIS integration for the production of BIM models. Case studies are presented, providing a practical dimension that demonstrates the production process of the urban model and its contribution to multiscale simulations, particularly in real estate evaluation and urban renewal.

Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. - Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography - Provides an overview, methods and case studies for each application - Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Building information modelling (BIM) uses a combination of technologies and resources to capture, manage, analyse, and display a digital representation of the physical and functional characteristics of a facility. A Geographic Information System (GIS) offers tools for visualising and analysing built and natural environments and their impacts on infrastructure systems, such as land use, transportation, etc. This book offers a framework for how the geospatial and surveying industry can create and integrate BIM with GIS. Through practical implementation methods, readers will learn to merge GIS data with design and BIM data to enable digital twins. This book presents the following features: Integrates BIM and GIS from a geospatial and surveying point of view for the first time; Addresses the creation of BIM from existing constructed buildings instead of typical pre-construction scenarios; Explains how to deliver BIM-suitable surveys to surveyors and geospatial practitioners; Provides surveying and geospatial industry expertise in the collecting, locating, managing, and communicating of BIM; Introduces new knowledge on the validation and integration of BIM and GIS within the spatial industry. This is an excellent book for professionals working with 3D data for built-environment digital twins, such as city planners, land surveyors, and geospatial practitioners. It is also an insightful resource for those working and studying in the fields of GIS, surveying, and geospatial engineering, providing the most current tools and resources for dealing with BIM.

This book presents a model for the integration of spatial information for 3D Geo-Information Systems (3D-GISs). Former Geo-Information Systems are restricted to 2D space. They execute the integration of spatial information by conversion of vector and raster representations. This, however, leads to conceptual difficulties because of the totally different paradigms. After an introduction to the history and architecture of Geo-Information Systems this book examines spatial representations in 2D and 3D space regarding their suitability in 3D-GISs. A three-level notion of space serves as a basis of a model for the integration of spatial information. It likewise takes into account the geometry, metrics and the topology of geo-objects.

Large-Scale 3D Data Integration: Challenges and Opportunities examines the fundamental aspects of 3D geo-information, focusing on the latest developments in 3D GIS (geographic information) and AEC (architecture, engineering, construction) systems. This book addresses policy makers, designers and engineers, and individuals that need to overco

During the last decade developments in 3D Geoinformation have made substantial progress. We are about to have a more complete spatial model and understanding of our planet in different scales. Hence, various communities and cities offer 3D landscape and city models as valuable source and instrument for sustainable management of rural and urban resources. Also municipal utilities, real estate companies etc. benefit from recent developments related to 3D applications. To meet the challenges due to the newest changes academics and practitioners met at the 5th International Workshop on 3D Geoinformation in order to present recent developments and to discuss future trends. This book comprises a selection of evaluated, high quality papers that were presented at this workshop in November 2010. The topics focus explicitly on the last achievements (methods, algorithms, models, systems) with respect to 3D geo-information requirements. The book is aimed at decision makers and experts as well at students interested in the 3D component of geographical information science including GI engineers, computer scientists, photogrammetrists, land surveyors, urban planners, and mapping specialists.

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.