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ABSTRACT
When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation capability object field (SOCO-Field) with sensor observation capability particle (SOC-Particle) as its core. SOCO-Field integrates SOC-Objects and GeoField for the discovery and association of sensors. SOC-Particle objectively exists on every location point in the geospatial environment, and SOC-Particles in space-continuous areas can further aggregate into SOC-Particle cluster to represent single- or multi-sensor-associated observation capability information. SOCO-Field includes three basic association behaviours and four further association behaviours to solve associated observation capability, in which the dynamic GeoField is the influential factor. An experiment on flood monitoring in the lower reaches of Jinsha River Basin is conducted. The sensor planner can view any sensor combination’s associated observation capability under a specific association mode and can effectively dispatch a multi-sensor for collaborated observation due to the effective modelling of associated sensor observation capability information (SOCInfo).
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Chuli Hu
Chuli Hu received the B.Sc. degree in resources environment management from Anhui University of Architecture, China, in 2008, the M.S. degree in geographical information system from Wuhan University, China, in 2010, and the Ph.D. degree in photogrammetry and remote sensing form Wuhan University, China, in 2013. He is currently an associated professor in China University of Geosciences, Wuhan, China. His research interests include Sensor Integration Management and Smart City Sensing.
Jie Li
Jie Li received the B.Sc. degree in geographic information system in China University of Geosciences (Wuhan) in 2016 and received the master’s degree in surveying engineering in China University of Geosciences (Wuhan) in 2018. He is currently pursuing the doctor’s degree with the Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei. His current research interests include multi-sensor associated observation capability modeling, representation and cognition, which can be used as reliable information basis for task-oriented multi-sensor selection and planning.
Changjiang Xiao
Changjiang Xiao received the B.Sc. degree in spatial-informatics and digitalized technology from Wuhan University, China, in 2013, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, China, in 2019. He also conducted joint Ph.D. research at the CyberGIS Center for Advanced Digital and Spatial Studies, Department of Geography and Geographical Information Sciences, University of Illinois at Urbana-Champaign, United States. His research interests include Geospatial Sensor Web, spatiotemporal deep learning, and their applications in smart city, watershed and ocean.
Ke Wang
Ke Wang received the B.Sc. degree in geographical information system from China University of Petroleum, China, in 2009, the M.S. degree in cartography and geographical information engineering from China University of Petroleum, China, in 2012, and the Ph.D. degree in photogrammetry and remote sensing form Wuhan University, China, in 2016. He is currently an associated professor in China University of Geosciences, Wuhan, China. His research interests include Geospatial Sensor Web, Smart City Sensing, Spatio-temporal Optimization, and Intelligent Computing.
Nengcheng Chen
Nengcheng Chen received the B.Sc. degree in geodesy from Wuhan Technical University of Surveying and Mapping, China, in 1997, the M.S. degree in geographical information systems from Wuhan University, China, in 2000, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University in 2003. Currently, he is a Professor of geographic information science of the State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China. His research interests include Smart Planet, Sensor Web, Semantic Web, Digital Antarctica, Smart City and Web GIS.