Название: Adjustment Computations: Spatial Data Analysis, 7th Edition
Автор: Charles D. Ghilani, Dimitrios Bolkas, Michael J. Olsen
Издательство: Wiley
Год: 2026
Страниц: 795
Язык: английский
Формат: pdf (true), epub
Размер: 54.3 MB

Provides Comprehensive, Up-to-Date Guidance on Spatial Data Accuracy.

As modern surveying, mapping, and geospatial technologies continue to evolve, the need for precise data adjustment and error analysis has never been greater. Adjustment Computations: Spatial Data Analysis, Seventh Edition, remains the definitive guide to understanding, applying, and mastering least squares and related techniques that ensure the accuracy of spatial datasets.

This updated edition integrates advances in spatial technologies, with new chapters covering laser scanning, point cloud processing, and parametric model estimation. Updated discussions of ASPRS standards, NSRS updates, and current computational tools reflect the latest professional practices and technologies. The text maintains its clear, pedagogical structure with concise chapters, worked examples, and practical software applications. Providing the analytical tools to deliver high-quality spatial data for diverse applications, this book:

Includes coverage of laser scanning and its errors, point cloud shape fitting, GNSS networks, coordinate transformations, and parametric modeling
Incorporates updated standards from ASPRS and the National Spatial Reference System (NSRS)
Provides access to companion software, MathCAD worksheets, and video lessons for hands-on problem solving
Balances rigorous mathematics with accessible explanations and real-world surveying applications
Features real-world case studies of infrastructure documentation, construction, and environmental monitoring

In years past, the least‐squares method was seldom used for adjusting surveying data because the time required to set up and solve the necessary equations was too great for hand methods. Readily available computing capabilities have eliminated this disadvantage. Besides advances in computer technology, some other recent developments have also led to the increased use of least squares. Prominent among these are the global navigation satellite systems (GNSS) such as the global positioning system (GPS) and geographic information systems (GISs). These systems rely heavily on rigorous error propagation methods, adjustment of data, and statistical analysis of the results. But perhaps the most compelling of all reasons for the continually increasing interest in least‐squares adjustments is that modern accuracy standards depend on quantities obtained from least‐squares adjustments. Thus, surveyors of the future will not be able to test their observations for compliance with these standards unless they adjust their data using least squares.

The advent of newer technologies such as laser scanning and Uncrewed Aircraft Systems (UASs) has marked a new era for geospatial professions. Today, professionals can map natural surfaces and model objects and structures with unprecedented detail using hundreds of millions to billions of points. However, detailed point clouds are not free from error, and professionals still need to demonstrate compliance with accuracy requirements and survey standards. This can be achieved through error propagation, allowing professionals to understand the quality of point clouds, which can considerably vary spatially, making error propagation processes more important than ever. Furthermore, geospatial professionals, having to manage massive datasets of points, are faced with new problems such as three‐dimensional modeling of man‐made objects and natural surfaces. This can be achieved through robust methods such as least‐squares fitting.

The seventh edition addresses the evolving knowledge requirements with two new chapters. Chapter 26 covers basic laser scanner measurement principles, major error sources, and error propagation of uncertainty to point cloud coordinates. Chapter 27 covers the least‐squares method to fit planes, spheres, and cylinders to point clouds. Additionally, this chapter covers polynomial fitting and interpolation for digital elevation model (DEM) estimation. Chapter 19 of the new edition provides updated information related to the new ASPRS positional accuracy standards. Chapter 24 provides updated information about transformations in the modernized National Spatial Reference System (NSRS). Lastly, Chapter 24 covers the use of total station pseudo‐observations to adjust data for a combined GNSS and total station survey.

Fortunately, because eigenvectors and eigenvalues are used in many mathematical problems in diverse domains, there are many tools available to obtain them. Thus, robust computer routines to determine the eigenvalues and associated eigenvectors of a matrix are readily available in programs such as MathCad and Matlab or as accessible libraries for programming languages such as Python and C++.

Ideal for undergraduate and graduate courses in surveying, geomatics, and geospatial analysis, Adjustment Computations: Spatial Data Analysis, Seventh Edition, supports curricula in civil engineering, geosciences, and GIS programs. It is also a useful reference for professional surveyors and geospatial analysts seeking to enhance data accuracy and reliability.