Machine learning and geodesy: A survey
-
Jemil Butt
,
Andreas Wieser
Abstract
The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise.
Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy.
Acknowledgment
The authors acknowledge the work of the two anonymous reviewers who contributed to this paper by providing suggestions on content and formatting of the paper thereby improving its readability and correctness.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Empirical analyses of robustness of the square Msplit estimation
- 1996–2017 GPS position time series, velocities and quality measures for the CORS Network
- Machine learning and geodesy: A survey
- Review
- A new Approach for surveying the axis of cylindrical pipes using a reflectorless total station
- Research Articles
- The uncertainty of CRUST1.0
- Identification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust
Articles in the same Issue
- Frontmatter
- Research Articles
- Empirical analyses of robustness of the square Msplit estimation
- 1996–2017 GPS position time series, velocities and quality measures for the CORS Network
- Machine learning and geodesy: A survey
- Review
- A new Approach for surveying the axis of cylindrical pipes using a reflectorless total station
- Research Articles
- The uncertainty of CRUST1.0
- Identification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust
