Abstract
Recently, the geospatial semantic information of remote sensing (RS) has attracted attention due to its various applications. This paper introduces a model for ontology based geospatial data integration using novel deep learning techniques. Here, we use a semantic web technology to establish the spatial ontology of risk knowledge with deep learning (DL), namely deep attention based bidirectional search and rescue LSTM for analysis. This approach takes into consideration of the study which presents the technique driven by the spatial ontology which minimizes the cost of modelling. The classification results from DL model enhances the performance of the ontology module. In this paper, ontological reasoning and DL model are jointly used for increase the module efficiency. The implementation of the proposed scheme is implemented on MATLAB 2020a. The performance of the implemented scheme is compared against the existing models like U-Net, Semantic referee and collaboratively boosting framework (CBF). The Overall accuracy (OA) of the system is found to be 0.923 on UCM dataset. Thus, the developed spatial ontologies provide the semantic foundation to achieve a semantic knowledge of geospatial data understandings.
Published Version
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