Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

By Quanlong Feng, Jiantao Liu, Jianhua Gong. 2015

306 citations recorded by the source corpus.

Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.

Subfields: Environmental Engineering, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/0eafe079ba47715f311448613b7059745d68e82c

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Citations in the corpus

Wireless Sensor Networks and Multi-UAV systems for natural disaster management (2017, methodology): “propose a flood monitoring system, using data obtained by a fleet of UAVs [25].”
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019, result): “The 398 successful results of the RF model are in agreement with Wang et al. (2015), Lee et al. (2017), and 399 Feng et al. (2015).”
The Future of Earth Observation in Hydrology. (2017, unknown): “…recent contributions have demonstrated the utility of UAVs in hydrological process studies, with snow depth retrieval (Vander Jagt et al., 2015), flood mapping (Feng et al., 2015), irrigation monitoring (Bellvert et al., 2016) and evaporation estimation (Hoffmann et al., 2016) all being explored.”
Unmanned Aerial Vehicles for Search and Rescue: A Survey (2023, methodology): “The paper [57] introduced the UAV mapping to urban ﬂoods by Random Forest Classiﬁer.”