Intelligent services for discovery of complex geospatial features from remote sensing imagery |
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| Institution: | 1. The Southern Regional Testing Center of Food Safety, Institute of Public Health Ho Chi Minh City, Ministry of Health, 159 Hung Phu St, Ward 8, Dist. 8, Ho Chi Minh City, Viet Nam;2. Department of Infectious Disease, Osaka Prefectural Institute of Public Health, Nakamichi, Osaka 537-0025, Japan;3. Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka 598-8531, Japan;4. Global Collaboration Center, Osaka University, Osaka 565-0871, Japan;1. Lab of Microbial Engineering (Infection and Immunity), Hainan University, Haikou 570228, China;2. One health institute, Hainan university, Haikou 570228, China;3. Institute of Tropical Bioscience and Biotechnology, China Academy of Tropical Agricultural Sciences, Haikou, Hainan, China.;4. Department of Bioinformatics, Hazara University, Mansehra 21300, Pakistan;1. Department of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy;2. Department of Veterinary Sciences, University of Messina, Italy;3. Department of Veterinary Sciences, University of Pisa, Italy;4. Department of Veterinary Sciences, University of Turin, Italy;1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;3. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;4. School of Geographic Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China;5. Center for Spatial Information Science and Systems, George Mason University, Fairfax 22030, USA |
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| Abstract: | Remote sensing imagery has been commonly used by intelligence analysts to discover geospatial features, including complex ones. The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. The methods of extraction of elementary ground features such as buildings and roads from remote sensing imagery have been studied extensively. The discovery of complex geospatial features, however, is still rather understudied. A complex feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., buildings for hosting fuel concentration machines, cooling towers, transportation roads, and fences). Such spatial semantics, together with thematic semantics of feature types, can be used to discover complex geospatial features. This paper proposes a workflow-based approach for discovery of complex geospatial features that uses geospatial semantics and services. The elementary features extracted from imagery are archived in distributed Web Feature Services (WFSs) and discoverable from a catalogue service. Using spatial semantics among elementary features and thematic semantics among feature types, workflow-based service chains can be constructed to locate semantically-related complex features in imagery. The workflows are reusable and can provide on-demand discovery of complex features in a distributed environment. |
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