Mining multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions
Conference or Workshop item
Paper/Presentation Title | Mining multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions |
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Authors | Munawar, Hafiz Suliman (Author), Zhang, Ji (Author), Li, Hongzhou (Author), Mo, Deqing (Author) and Chang, Liang (Author) |
Editors | Leong, Hou U. and Lauw, Hady W. |
Journal or Proceedings Title | Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019) |
Journal Citation | 11607, pp. 189-200 |
Number of Pages | 12 |
Year | 2019 |
Publisher | Springer |
Place of Publication | Cham, Switzerland |
ISBN | 9783030261412 |
9783030261429 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-26142-9_17 |
Conference/Event | 8th Workshop on Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019), held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019) |
Event Details | 8th Workshop on Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019), held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019) Parent Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Event Date 14 to end of 17 Apr 2019 Event Location Macau, China |
Abstract | We propose in this paper an image mining technique based on multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions. Bridge detection from aerial images is a key landmark that has vital importance in disaster management and relief missions. UAVs have been increasingly used in recent years for various relief missions during the natural disasters such as floods and earthquakes and a huge amount of multispectral aerial images are generated by UAVs in the missions. Being a multi- stage technique, our method utilizes these multispectral aerial images for identifying patterns for effective mining of bridge locations. Experimental results on real-world and synthetic images are conducted to demonstrate the effectiveness of our proposed method, showing that it is 40% faster than the existing Automatic Target Recognition (ATR) systems and can achieve a 95% accuracy. Our technique is believed to be able to help accelerate and enhance the effectiveness of the relief missions carried out during disasters. |
Keywords | image mining, disaster management, isotropic surround suppression, image processing, object recognition, linear object detection, bridge recognition, road recognition |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Artificial Intelligence (Book series) |
Byline Affiliations | University of New South Wales |
School of Agricultural, Computational and Environmental Sciences | |
Guilin University of Electronic Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59xz/mining-multispectral-aerial-images-for-automatic-detection-of-strategic-bridge-locations-for-disaster-relief-missions
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