Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich in theory and concepts, this comprehensive book on least square estimation and data analysis provides examples that are designed to help students extend their knowledge to solving more practical problems. The sample problems are accompanied by suggested solutions, and are challenging, yet easy enough to manually work through using simple computing devices, and chapter objectives provide an overview of the material contained in each section. Understanding Least Squares Estimation and Geomatics Data Analysis begins with an explanation of survey observables, observations, and their stochastic properties. It reviews matrix structure and construction and explains the needs for adjustment. Next, it discusses analysis and error propagation of survey observations, including the application of heuristic rule for covariance propagation. Then, the important elements of statistical distributions commonly used in geomatics are discussed. Main topics of the book include: concepts of datum definitions; the formulation and linearization of parametric, conditional and general model equations involving typical geomatics observables; geomatics problems; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre-analysis; three-dimensional geodetic network adjustment; nuisance parameter elimination and the sequential least squares adjustment; post-adjustment data analysis and reliability; the problems of datum; mathematical filtering and prediction; an introduction to least squares collocation and the kriging methods; and more. Contains ample concepts/theory and content, as well as practical and workable examples Based on the author's manual, which he developed as a complete and comprehensive book for his Adjustment of Surveying Measurements and Special Topics in Adjustments courses Provides geomatics undergraduates and geomatics professionals with required foundational knowledge An excellent companion to Precision Surveying: The Principles and Geomatics Practice Understanding Least Squares Estimation and Geomatics Data Analysis is recommended for undergraduates studying geomatics, and will benefit many readers from a variety of geomatics backgrounds, including practicing surveyors/engineers who are interested in least squares estimation and data analysis, geomatics researchers, and software developers for geomatics.

Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich in theory and concepts, this comprehensive book on least square estimation and data analysis provides examples that are designed to help students extend their knowledge to solving more practical problems. The sample problems are accompanied by suggested solutions, and are challenging, yet easy enough to manually work through using simple computing devices, and chapter objectives provide an overview of the material contained in each section. Understanding Least Squares Estimation and Geomatics Data Analysis begins with an explanation of survey observables, observations, and their stochastic properties. It reviews matrix structure and construction and explains the needs for adjustment. Next, it discusses analysis and error propagation of survey observations, including the application of heuristic rule for covariance propagation. Then, the important elements of statistical distributions commonly used in geomatics are discussed. Main topics of the book include: concepts of datum definitions; the formulation and linearization of parametric, conditional and general model equations involving typical geomatics observables; geomatics problems; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre-analysis; three-dimensional geodetic network adjustment; nuisance parameter elimination and the sequential least squares adjustment; post-adjustment data analysis and reliability; the problems of datum; mathematical filtering and prediction; an introduction to least squares collocation and the kriging methods; and more. Contains ample concepts/theory and content, as well as practical and workable examples Based on the author's manual, which he developed as a complete and comprehensive book for his Adjustment of Surveying Measurements and Special Topics in Adjustments courses Provides geomatics undergraduates and geomatics professionals with required foundational knowledge An excellent companion to Precision Surveying: The Principles and Geomatics Practice Understanding Least Squares Estimation and Geomatics Data Analysis is recommended for undergraduates studying geomatics, and will benefit many readers from a variety of geomatics backgrounds, including practicing surveyors/engineers who are interested in least squares estimation and data analysis, geomatics researchers, and software developers for geomatics.

Develops the full power of the least-squares method Enables engineers and scientists to apply the method to their specific problem Deals with linear as well as with non-linear least-squares, parametric as well as non-parametric methods

This book is for students and professionals involved in Geospatial Computations and related areas such as Geomatics, Surveying Engineering, Geoinformatics, Geospatial Information Science and Technology (GIS&T), Geography, Geology, Agriculture, and Geointelligence. More emphasis is given to using scientific methods and tools materialized in algorithms and software to produce practical results. Specifically, algorithms such as error analysis of measurements and the least squares adjustment method to obtain ground coordinates of points with their reliability to construct the geometric framework of the geographical space necessary for various geospatial applications such as a Geographic Information System (GIS) are discussed. Other algorithms involve interpolation methods for DEM and spatial data analysis. Furthermore, such algorithms in the geospatial area are basic surveying methods using a total station, photogrammetry, digital terrain modeling, GNSS, augmented reality, coordinate transformations, map projections, and interpolation. Most algorithms are implemented into 27 educational computer programs and necessary data to understand GIS&T operations from the inside with a didactics approach targeting to become more intelligent than machines. The educational programs include general photogrammetric operations with aerial photography and drones, 3-D surveying network adjustment, GNSS navigation solutions, and many others. This approach helps to obtain high-quality scientific and technological bases, which in turn enhance the ability to exploit and use most tools and functions of existing GIS&T systems and, therefore, to be highly competitive as a professional in the market. This book has ten chapters such as Measurements and Errors Estimation and Accuracy Standards, Specialized Numerical Methods, Error Propagation & Least Squares Adjustment, Condition Method and Generalized Least Squares, Applications to Map Projections and Transformation of Coordinates, Applications to Surveying Networks, Applications of Computational Methods in Photogrammetry, Digital Elevation Models (DEM), Computer Programming – Scripting & AI.

Develops the full power of the least-squares method Enables engineers and scientists to apply the method to their specific problem Deals with linear as well as with non-linear least-squares, parametric as well as non-parametric methods

This book contains the best papers accepted for presentation at the 3rd Springer Conference of the Arabian Journal of Geosciences (CAJG-3). The book is divided into three parts to distinguish research studies from the fields of (1) geological and geotechnical engineering, (2) geomechanical studies based on numerical and analytical methods, and (3) geo-informatics and remote sensing. The content of these papers provides new scientific knowledge for further understanding on landslides, new stabilization techniques, importance of geophysics for engineering geology investigations as well as new empirical approaches for easily predicting some physical and hydrogeomechanical properties of geomaterials. It also sheds the light on usage of different remote sensing technologies, mapping techniques in a geographic information systems environment along with some advanced signal processing, and deep learning approaches. The book is of interest to all researchers, practitioners, and students in the fields of geological and mining engineering, geotechnical engineering, hydrogeomechanics, engineering geology, geotechnologies, groundwater and natural hazards, and analyses of some emerging earth processes and environmental variabilities.

This book examines every aspect of least square adjustment. It defines terms and introduces readers to the fundamentals of errors and describes methods for analyzing them. It also illustrates the application of least squares in adjusting a wide range of survey types and provides detailed coverage of applications of least squares to GPSs and GISs.

A lucid explanation of the intricacies of both simple and complex least squares methods. As one of the classical statistical regression techniques, and often the first to be taught to new students, least squares fitting can be a very effective tool in data analysis. Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of Least Squares Data Fitting with Applications is how to do this on a computer with efficient and robust computational methods for linear and nonlinear relationships. The presentation also establishes a link between the statistical setting and the computational issues. In a number of applications, the accuracy and efficiency of the least squares fit is central, and Per Christian Hansen, Víctor Pereyra, and Godela Scherer survey modern computational methods and illustrate them in fields ranging from engineering and environmental sciences to geophysics. Anyone working with problems of linear and nonlinear least squares fitting will find this book invaluable as a hands-on guide, with accessible text and carefully explained problems. Included are • an overview of computational methods together with their properties and advantages • topics from statistical regression analysis that help readers to understand and evaluate the computed solutions • many examples that illustrate the techniques and algorithms Least Squares Data Fitting with Applications can be used as a textbook for advanced undergraduate or graduate courses and professionals in the sciences and in engineering.