Abstract
Because of the superiority of crowdsourcing, many companies are eager to seek the solutions for some issues through crowdsourcing, e.g., assembling crowd wisdom crystallization for a wonderful design, collecting personal-privacy information or geographical location information, etc. Location services are becoming more and more important to the lives and lifestyles of modern people, so the Internet companies and traditional enterprises placed great emphasis on this theme. We can obtain valuable information from the large-scale datasets, but in certain circumstances, a large amount of data will be a burden for us to analyze problems. On account of redundant data that carries little weight with us, in this paper, we mainly consider trajectory data extraction of timeline-based location information and route fitting in the field of military (e.g., GIS) and civilian (e.g., traffic). For the purpose, we refer to the idea of Vector Quantization, denoted as VQ, and give a model of TDERTM (Timeline-based Data Extraction and Route Fitting Model) to solve that. In this sense, it is efficient and convenient to determine whether the target (ship, aircraft, etc.) cross the border and draw a navigation trajectory with low redundancy.
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Xu, N., Wang, Y., Chen, X., Lian, H. (2019). Vector Quantization: Timeline-Based Location Data Extraction and Route Fitting for Crowdsourcing. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018). CHREOC 2018. Lecture Notes in Electrical Engineering, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-13-6553-9_4
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DOI: https://doi.org/10.1007/978-981-13-6553-9_4
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