Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

Abstract

Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

“…Flood susceptibility and inundation models are compared with techniques such as frequency ratio (Popa et al, 2019), a type of MCDA model; the soil conservation service runoff model (Jahangir et al, 2019), a hydrologic model; and automatic threshold model (Nemni et al, 2020), a histogram-based model. They are also compared with machine learning techniques, such as support vector machines (e.g., Sarker et al, 2019;Gebrehiwot et al, 2019;Zhao et al, 2020b), random forest (e.g., Darabi et al, 2021;Zhao et al, 2020b), adaptive neuro-fuzzy inference system (Panahi et al, 2021), and radial basis function (Nogueira et al, 2017). DL models show to outperform both traditional and ML models in terms of the accuracy of the results.…”

Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

“…Several researches also adopt machine learning-based techniques for detecting flood water using SAR imagery. Examples include artificial neural network (ANN) (Sarker et al, 2019, Tien, Bui et al, 2020, k-nearest neighbors (KNN) (Aristizabal, Judge, & Monsivais-Huertero, 2020), random forest (RF) (Chaabani et al, 2018, Woznicki et al, 2019 and support vector machines (SVM) (Tehrany et al, 2015, Insom et al, 2015.…”

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

“…Flood detection attracted significant amount of attention after the competition and new datasets and deep learning algorithms had been developed for this purpose. Deep learning algorithms such as deep neural network [29], deep convolutional neural network (DCNN) [30,31] and fully convolutional neural networks (F-CNNs) [32] with satellite images including Sentinel-2 [30], Planet [31], Landsat [32] and Google Earth aerial imagery [29] have been used for flood detection. Unmanned aerial vehicles (UAV) image data with VGG-based fully convolutional network (FCN-16s) was also developed for flood detection [33].…”