Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications
Author:
Funder
Universiti Putra Malaysia
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06503-6.pdf
Reference25 articles.
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2. Arshad B, Ogie R, Barthelemy J, Pradhan B, Verstaevel N, Perez P (2019) Computer vision and iot-based sensors in flood monitoring and mapping: a systematic review. Sensors (switzerland) 19(22):1–28. https://doi.org/10.3390/s19225012
3. CFE-DM (2019) Malaysia disaster management reference handbook. Center for Excellence in Disaster & Humanitarian Assistance (CFE-DM). Retrieved from http://reliefweb.int/map/chile/chilelocation-map-2013
4. Costa DG, Guedes LA, Vasques F, Portugal P (2013) Adaptive monitoring relevance in camera networks for critical surveillance applications. Int J Distrib Sensor Netw. https://doi.org/10.1155/2013/836721
5. Costabile P, Costanzo C, De Lorenzo G, De Santis R, Penna N, Macchione F (2021) Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives. Environ Model Softw 135:104889. https://doi.org/10.1016/j.envsoft.2020.104889
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