JSG T.29

JSG T.29: Machine learning in geodesy

Chairs: Benedikt Soja (USA), chair
Affiliation: Commissions 2, 3 and 4

Introduction

Due to the exponential increase in computing power over the last decades, machine learning has grown in importance for several applications. In particular, deep learning, i.e., machine learning based on deep neural networks, typically performed on extensive data sets (“big data”), has become very successful in tackling various challenges, for example, image interpretation, language recognition, autonomous decision making or stock market predictions. Several scientific disciplines have embraced the capability of modern machine learning algorithms, including astronomy and many fields of geosciences.

The field of geodesy has seen a significant increase in observational data in recent years, in particular from Global Navigation Satellite Systems (GNSS) with tens of thousands of high-quality permanent stations, multiple constellations, and increasing data rates. With the upcoming NISAR mission, the InSAR community needs to prepare for handling daily products exceeding 50 GB. In the future, the next-generation Very Long Baseline Interferometry (VLBI) Global Observing System (VGOS) will deliver unprecedented amounts of data compared to legacy VLBI operations. Traditional data processing and analysis techniques that rely largely on human input are not well suited to harvest such rich data sets to their full potential. Still, machine learning techniques are not yet adopted in geodesy.

Machine learning in geodesy has the potential to facilitate the automation of data processing, detection of anomalies in time series and image data, their classification into different categories and prediction of parameters into the future. Machine learning and, in recent years, deep learning methods can successfully model complex spatio-temporal data through the creation of powerful representations at hierarchical levels of abstraction. Furthermore, machine learning techniques provide promising results in addressing the challenges that arise when handling multi-resolution, multi-temporal, multi-sensor, multi-modal data. The information contained in GNSS station position time series is essential as it can help derive important conclusions related to hydrology, earthquakes, or volcanism using machine learning. Other important applications are tropospheric and ionospheric parameters derived from GNSS where automated detection and prediction could be beneficial for improved severe weather forecasting and space weather monitoring, respectively. InSAR data will benefit in particular from efficient image processing algorithms based on machine learning, facilitating the detection of regions of interest. In several of these cases, the development of scalable deep learning schemes can contribute to more effectively handling and processing of large-scale spatio-temporal data.

Traditional machine learning techniques for geodetic tasks include convolutional neural networks for image data and recurrent neural networks for time series data. Typically, these networks are trained by supervised learning approaches, but certain applications related to autonomous processing will benefit from reinforcement learning.

The field of machine learning has expanded rapidly in recent years and algorithms are constantly evolving. It is the aim of this JSG to identify best practices, methods, and algorithms when applying machine learning to geodetic tasks. In particular, due to the “black box” nature of many machine learning techniques, it is very important to focus on appropriate ways to assess the accuracy and precision of the results, as well as to correctly interpret them.

Objectives

• Identify geodetic applications that could benefit from machine learning techniques, both in terms of which data sets to use and which issues to investigate.
• Create an inventory of suitable machine learning algorithms to address these problems, highlighting their strengths and weaknesses.
• Perform comparisons between machine learning methods and traditional data analysis approaches, e.g., for time series analysis and prediction.
• Focus on error assessment of results produced by machine learning algorithms.
• Identify open problems that come with the automation of data processing and generation of geodetic products, including issues of reliability.
• Develop best practices when applying machine learning methods in geodesy and establishing standardized terminology.

Program of activities

• Create a web page about machine learning in geodesy to provide information and raise awareness about this topic. The page will include:
  • inventory of algorithms, see above,
  • benchmark datasets to test the performance of these algorithms,
  • comprehensive record of previous activities/publications related to machine learning in geodesy,
  • description of activities by the JSG members.
• Work toward a state-of-the-art review paper about machine learning in geodesy co-authored by the JSG members.
• Promote sessions and presentation of the research results at international scientific assemblies (IAG/IUGG, EGU, AGU) and technique-specific meetings (IGS, IVS, ...).

Members

Benedikt Soja (USA), chair
Kyriakos Balidakis (Germany)
Clayton Brengman (USA)
Jingyi Chen (USA)
Maria Kaselimi (Greece)
Ryan McGranaghan (USA)
Randa Natras (Germany)
Simone Scardapane (Italy)