Tornado Speed Estimation Using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)-Based Video Processing Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4183-4_15
Reference10 articles.
1. Radhika S, Tamura Y, Matsui M (2012) Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis. J Wind Eng Ind Aerodyn 107:202–213
2. Sabareesh GR, Matsui M, Tamura Y (2011) Characteristics of surface pressures on a building under a tornado-like flow at different swirl ratios. J Wind Eng 8(2):30–40
3. Radhika S, Tamura Y, Matsui M (2017) Application of remote sensing images for post-wind storm damage analysis. In: Remote sensing of hydro-meteorological hazards. 1st edn. Taylor & Francis
4. Sabareesh GR, Matsui M, Tamura Y (2013) Characteristics of internal pressure and resulting roof wind force in tornado-like flow. J Wind Eng 112:52–57
5. Elhamod M, Levine MD (2013) Automated real-time detection of potentially suspicious behavior in public transport areas. In: IEEE transactions on intelligent transportation systems, vol 14(2), pp. 688–699
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