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ISPRS Int. J. Geo-Inf. 2019, 8(2), 68; https://doi.org/10.3390/ijgi8020068

Semantics-Constrained Advantageous Information Selection of Multimodal Spatiotemporal Data for Landslide Disaster Assessment

1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 999077, China
3
College of Engineering, Peking University, Beijing 100871, China
4
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
*
Authors to whom correspondence should be addressed.
Received: 27 December 2018 / Revised: 21 January 2019 / Accepted: 27 January 2019 / Published: 30 January 2019
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Abstract

Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spatiotemporal data that is advantageous for credible and comprehensive disaster assessment. In disaster scenarios, multimodal data exhibit intrinsic relationships, and their interactions can greatly influence selection results. Previous data retrieval methods have mainly focused on candidate ranking while ignoring the generation and evaluation of candidate subsets. In this paper, a semantic-constrained data selection approach is proposed. First, multitype relationships are defined and reasoned through the heterogeneous information network. Then, relevance, redundancy, and complementarity are redefined to evaluate data sets in terms of semantic proximity and similarity. Finally, the approach is tested using Mao County (China) landslide data. The proposed method can automatically and effectively generate suitable datasets for certain tasks rather than simply ranking by similarity, and the selection results are compared with manual results to verify their effectiveness. View Full-Text
Keywords: multimodal data; data retrieval; data selection; semantic proximity and similarity multimodal data; data retrieval; data selection; semantic proximity and similarity
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Zhu, Q.; Zhang, J.; Ding, Y.; Liu, M.; Li, Y.; Feng, B.; Miao, S.; Yang, W.; He, H.; Zhu, J. Semantics-Constrained Advantageous Information Selection of Multimodal Spatiotemporal Data for Landslide Disaster Assessment. ISPRS Int. J. Geo-Inf. 2019, 8, 68.

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