From raw sensor data to detailed spatial knowledge

Publisher:
`AAAI Press / International Joint Conferences on Artificial Intelligence
Publication Type:
Conference Proceeding
Citation:
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015, pp. 910 - 917
Issue Date:
2015-01-01
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Qualitative spatial reasoning deals with relational spatial knowledge and with how this knowledge can be processed efficiently. Identifying suitable representations for spatial knowledge and checking whether the given knowledge is consistent has been the main research focus in the past two decades. However, where the spatial information comes from, what kind of information can be obtained and how it can be obtained has been largely ignored. This paper is an attempt to start filling this gap. We present a method for extracting detailed spatial information from sensor measurements of regions. We analyse how different sparse sensor measurements can be integrated and what spatial information can be extracted from sensor measurements. Different from previous approaches to qualitative spatial reasoning, our method allows us to obtain detailed information about the internal structure of regions. The result has practical implications, for example, in disaster management scenarios, which include identifying the safe zones in bushfire and flood regions.
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