Application of artificial intelligence in Geodesy – A review of theoretical foundations and practical examples

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Artificial Intelligence (AI) is one of the key technologies in many of today's novel applications. It is used to add knowledge and reasoning to systems. This paper illustrates a review of AI methods including examples of their practical application in Geodesy like data analysis, deformation analysis, navigation, network adjustment, and optimization of complex measurement procedures. We focus on three examples, namely, a geo-risk assessment system supported by a knowledge-base, an intelligent dead reckoning personal navigator, and evolutionary strategies for the determination of Earth gravity field parameters. Some of the authors are members of IAG Sub-Commission 4.2 – Working Group 4.2.3, which has the main goal to study and report on the application of AI in Engineering Geodesy

Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Height modernization program and subsidence study in northern Ohio

'Prepared in cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration."; "Report date: November 2013."; "FHWA/OH-2013/19"-- Technical rept. documentation p.; Executive summary report (4 p.) laid in.; Includes bibliographical references (p. 85-86).; Final report.; Sponsoring agency: Ohio Dept. of Transportation, Office of CADD and Mapping Services; SJN: 134698; Harvested from the web on 6/16/14This study is an initiative focused on establishing accurate, reliable heights using Global Navigation Satellite System (GNSS) technology in conjunction with traditional leveling, gravity, and modern remote sensing information. The traditional method for determining the elevation of these vertical benchmarks is differential leveling, but the advanced technology of Global Navigation Satellite System (GNSS) and other modern positioning technologies have begun to replace this classical technique of vertical measurement in many situations. The primary goal of this research was to contribute to the improvement of height estimation using GPS that supports the goals of the National Height Modernization project led by NGS. This was attained by investigating the required baseline length to de-correlate the tropospheric corrections at individual stations, as well as to determine the optimal network design. In order to perform these experiments, three different networks were formed: the single, the double, and the multiple base station approaches. The comparison of these three approaches concluded that the multiple base approach (combination of CORS and IGS stations) is the optimal network, which improved the estimation of the tropospheric corrections, the quality of the processing results, and the positioning accuracy, especially in the height component. This configuration reduced the possible errors associated with the base station, provides reliable tropospheric corrections and improved the accuracy of the ellipsoidal heights. These test cases also illustrated that a longer session provides higher accuracy and reliable ellipsoidal heights. Based on the results in this study, at least a two-hour data span should be used to determine the ellipsoidal heights accurately in OPUS-Projects. Additionally, a second independent observation should be used to increase the confidence in the processing results. In order to maximize independence of the observations, the second observation should be obtained on a different day and at a different time of day. This project is closely related to project 134692, Impact of Lakeside Subsidence on Benchmark Reliability

Performance of GEOID09 for height conversion in Ohio

Performing organization: The Ohio State University, Satellite Positioning and Inertial Navigation Lab, Dept. of Civil & Environmental Engineering & Geodetic Science.; "Report date: August 2010."; "FHWA/OH-2010/13"-- Technical rept. documentation p.; Includes bibliographical references (p. 40).; Sponsoring agency: Ohio Dept. of Transportation, Office of Aerial Engineering; SJN: 134326; Harvested from the web on 1/25/11This study evaluates Height Modernization issues related to NGS hybrid geoid performance (specifically GEOID09 and GEOID03) for height conversions between NAVD88 and NAD83 for the state of Ohio and quality of gravity and height data needed to produce a cm-accurate gravimetric geoid. The hybrid geoid is of particular significance in this study, because an accurate hybrid geoid would support both accurate height conversions as well as improve orthometric (MSL) height determination, facilitating the almost-total replacement (except in GPS-antagonistic environments) of traditional leveling exercises by GPS leveling, a much more cost- and labor-efficient heighting technique than its traditional counterpart

Probabilistic Use of LiDAR Data to Detect and Characterize Landslides

"Report date: March 2015"--Page 5.; Includes bibliographical references.; Final report;; Sponsored by Ohio Department of Transportation, Research Section; SJN: 134609; Harvested from the web on 4/13/15Introduction -- Research objectives -- General description of research -- Test area -- Landslide types -- LiDAR data -- Field surveys -- Point-based landslide detection -- Profile-based landslide detection -- Shape-based landslide detection -- Probabilistic change-based landslide detection -- Classification performance analysis -- Simulation analysis -- Conclusion, future recommendations -- References -- Appendix.Landslide hazard and its consequences in the transportation network are well-understood, yet current methods of identifying and assessing landslide conditions are inefficient, as they are mostly based on labor-intensive field surveys. This research was performed as a feasibility study, where the potential of airborne LiDAR data for landslide detection was investigated. The primary objective of this pilot study was to develop, implement and validate computer models for automatic detection and assessment of landslides using time-series of airborne LiDAR data. Models have been developed using LiDAR data obtained from SR 666 in Muskingum County (District 5) and independently tested on LiDAR data covering southern Ohio. In this research effort, two techniques, one using single and the other based on multi-temporal surface models, obtained by airborne LiDAR, were proposed, implemented and tested for landslide susceptibility and hazard mapping. Based on a single dataset, 84% of the landslides from the reference inventory map of SR 666 were correctly identified, while using two datasets acquired four years apart, the proposed technique was able to identify 66% of the mapped landslides that are experiencing temporal changes susceptible to slides

Airborne LiDAR : a new source of traffic flow data

At head of title: Project title.; "Report date: October 2005."; Executive summary report laid in.; Includes bibliographical references.; Final report.; Prepared by Ohio State University, Dept. of Civil and Environmental Engineering and Geodetic Science, Center for Mapping, sponsored by the Ohio Dept. of Transportation, Office of Aerial Engineering, and prepared in cooperation with the Ohio Dept. of Transportation and the Federal Highway Administration under; Harvested from the web on 2/7/06LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition to efficiently create DEM/DSM in the late 90's. With increasing point density, new systems are now able to support object extraction, such as extracting building and roads, from LiDAR data. The novel concept of this project was to use LiDAR data for traffic flow estimates. In a sense, extracting vehicles over transportation corridors represents the next step in complexity by adding the temporal component to the LiDAR data feature extraction process. The facts are that vehicles are moving at highway speeds and the scanning acquisition mode of the LiDAR certainly poses a serious challenge for the data extraction process. The OSU developed method and its implementation, the I FLOW program, have demonstrated that LiDAR data contain valuable information to support vehicle extraction, including vehicle grouping and localizations. The classification performance showed strong evidence that the major vehicle categories can be efficiently separated. The I FLOW program is ready for deployment.LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition to efficiently create DEM/DSM in the late 90's. With increasing point density, new systems are now able to support object extraction, such as extracting building and roads, from LiDAR data. The novel concept of this project was to use LiDAR data for traffic flow estimates. In a sense, extracting vehicles over transportation corridors represents the next step in complexity by adding the temporal component to the LiDAR data feature extraction process. The facts are that vehicles are moving at highway speeds and the scanning acquisition mode of the LiDAR certainly poses a serious challenge for the data extraction process. The OSU developed method and its implementation, the I FLOW program, have demonstrated that LiDAR data contain valuable information to support vehicle extraction, including vehicle grouping and localizations. The classification performance showed strong evidence that the major vehicle categories can be efficiently separated. The I FLOW program is ready for deployment