Next Article in Journal
Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data
Next Article in Special Issue
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies
Previous Article in Journal
Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries
Previous Article in Special Issue
A Sensor Web and Web Service-Based Approach for Active Hydrological Disaster Monitoring

Field Motion Estimation with a Geosensor Network

Institute of Cartography and Geoinformatics, Leibniz University Hanover, 30167 Hannover, Germany
Author to whom correspondence should be addressed.
This is an extended version of our paper published in the Proceedings of the 5th International Conference on Sensor Networks, 2016; pp. 13–24.
Academic Editors: Silvia Nittel and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(10), 175;
Received: 8 July 2016 / Revised: 9 August 2016 / Accepted: 18 September 2016 / Published: 27 September 2016
(This article belongs to the Special Issue Geosensor Networks and Sensor Web)
Physical environmental processes, such as the evolution of precipitation or the diffusion of chemical clouds in the atmosphere, can be approximated by numerical models based on the underlying physics, e.g., for the purpose of prediction. As the modeling process is often very complex and resource demanding, such models are sometimes replaced by those that use historic and current data for calibration. For atmospheric (e.g., precipitation) or oceanographic (e.g., sea surface temperature) fields, the data-driven methods often concern the horizontal displacement driven by transport processes (called advection). These methods rely on flow fields estimated from images of the phenomenon by computer vision techniques, such as optical flow (OF). In this work, an algorithm is proposed for estimating the motion of spatio-temporal fields with the nodes of a geosensor network (GSN) deployed in situ when images are not available. The approach adapts a well-known raster-based OF algorithm to the specifics of GSNs, especially to the spatial irregularity of data. In this paper, the previously introduced approach has been further developed by introducing an error model that derives probabilistic error measures based on spatial node configuration. Further, a more generic motion model is provided, as well as comprehensive simulations that illustrate the performance of the algorithm in different conditions (fields, motion behaviors, node densities and deployments) for the two error measures of motion direction and motion speed. Finally, the algorithm is applied to data sampled from weather radar images, and the algorithm performance is compared to that of a state-of-the-art OF algorithm applied to the weather radar images directly, as often done in nowcasting. View Full-Text
Keywords: motion estimation; decentralized; optical flow; geosensor network motion estimation; decentralized; optical flow; geosensor network
Show Figures

Figure 1

MDPI and ACS Style

Fitzner, D.; Sester, M. Field Motion Estimation with a Geosensor Network. ISPRS Int. J. Geo-Inf. 2016, 5, 175.

AMA Style

Fitzner D, Sester M. Field Motion Estimation with a Geosensor Network. ISPRS International Journal of Geo-Information. 2016; 5(10):175.

Chicago/Turabian Style

Fitzner, Daniel, and Monika Sester. 2016. "Field Motion Estimation with a Geosensor Network" ISPRS International Journal of Geo-Information 5, no. 10: 175.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop