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A platform for research: civil engineering, architecture and urbanism
Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition
Elevation information and its derivation, such as grade, are very important in analyses of traffic operation, safety performance, and energy consumption on highways. Google Earth (GE) is considered a reliable source of elevation information of ground surface and highway elevation. Data were extracted from GE. However, the authors found that raw GE elevation data on highways contains various anomalies and noises. The primary objective of this study was to evaluate the use of the ensemble empirical mode decomposition (EEMD) method for anomaly detection and cleaning of highway elevation data extracted from GE. Three interstate highways’ segments were studied, and typical anomalies that existed in raw GE elevation data were identified. The EEMD method was then applied to decompose elevation data into different compositions with different details of original data, which were determined into those containing true information or white noise. The modeling results showed that the EEMD method was effective in excluding noises and obtaining accurate elevation data. Findings of this study can help transport authorities to create an accurate elevation data set for all highways or other road classes.
Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition
Elevation information and its derivation, such as grade, are very important in analyses of traffic operation, safety performance, and energy consumption on highways. Google Earth (GE) is considered a reliable source of elevation information of ground surface and highway elevation. Data were extracted from GE. However, the authors found that raw GE elevation data on highways contains various anomalies and noises. The primary objective of this study was to evaluate the use of the ensemble empirical mode decomposition (EEMD) method for anomaly detection and cleaning of highway elevation data extracted from GE. Three interstate highways’ segments were studied, and typical anomalies that existed in raw GE elevation data were identified. The EEMD method was then applied to decompose elevation data into different compositions with different details of original data, which were determined into those containing true information or white noise. The modeling results showed that the EEMD method was effective in excluding noises and obtaining accurate elevation data. Findings of this study can help transport authorities to create an accurate elevation data set for all highways or other road classes.
Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition
Chen, Xinqiang (author) / Li, Zhibin (author) / Wang, Yinhai (author) / Tang, Jinjun (author) / Zhu, Wenbo (author) / Shi, Chaojian (author) / Wu, Huafeng (author)
2018-03-14
Article (Journal)
Electronic Resource
Unknown
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