期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 8, 页码 11314-11324出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3102747
关键词
Three-dimensional displays; Sensor phenomena and characterization; Intelligent sensors; Convolution; Deep learning; Feature extraction; Task analysis; Geo-sensory data; spatio-temporal point clouds; STPC-Net; deep learning
资金
- Natural Science Foundation of China [61872306]
- Xiamen Science and Technology Bureau [3502Z20193017]
- Fundamental Research Funds for the Central Universities [20720200031]
This paper introduces a novel deep neural network called STPC-Net for processing massive geo-sensory data represented as spatio-temporal point clouds (STPC). STPC-Net captures intra-sensor and inter-sensor correlations from STPC, enabling it to learn key information and overcome challenges posed by data irregularity. Experiments show that STPC-Net achieves state-of-the-art performance in various tasks on both mobile and stationary geo-sensory data.
Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data as spatio-temporal point clouds (STPC), and present STPC-Net, a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available at https://github.com/zhengchuanpan/STPC-Net.
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