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Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature.

Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. In this study, we used Monte Carlo dropout (MCDO) to characterize the uncertainty of deep learning image classification algorithms, including feature fusion models, on simulated synthetic aperture radar (SAR) images of persistent ship wakes. Comparing to a baseline, we used the distribution of predictions from dropout with simple mean value ensembling and the Kolmogorov—Smirnov (KS) test to classify in-domain and out-of-domain (OOD) test samples, created by rotating images to angles not present in the training data. Our objective was to improve the classification robustness and identify OOD images during the test time. The mean value ensembling did not improve the performance over the baseline, in that there was a –1.05% difference in the Matthews correlation coefficient (MCC) from the baseline model averaged across all SAR bands. The KS test, by contrast, saw an improvement of +12.5% difference in MCC and was able to identify the majority of OOD samples. Leveraging the full distribution of predictions improved the classification robustness and allowed labeling test images as OOD. The feature fusion models, however, did not improve the performance over the single SAR-band models, demonstrating that it is best to rely on the highest quality data source available (in our case, C-band).

Training deep learning models requires having the right data for the problem and understanding both your data and the models’ performance on that data. Training deep learning models is difficult when data are limited, so in this paper, we seek to answer the following question: how can we train a deep learning model to increase its performance on a targeted area with limited data? We do this by applying rotation data augmentations to a simulated synthetic aperture radar (SAR) image dataset. We use the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique to understand the effects of augmentations on the data in latent space. Using this latent space representation, we can understand the data and choose specific training samples aimed at boosting model performance in targeted under-performing regions without the need to increase training set sizes. Results show that using latent space to choose training data significantly improves model performance in some cases; however, there are other cases where no improvements are made. We show that linking patterns in latent space is a possible predictor of model performance, but results require some experimentation and domain knowledge to determine the best options.

On 25 March 1995, a large solitary wave, seemingly from nowhere, washed ashore along the normally tranquil Gulf Coast of Florida from Tampa Bay to south of Naples. On this Saturday morning, many beachgoers and coastal residents saw either a large wave, a surge, or a seiche. The wave was typically described as 3 m or greater, breaking between 0.5 and 3 km offshore, and taking 120–180 s to arrive at the shore. Just prior to the wave’s arrival at the beach, witnesses reported a rapid runout of water, then a huge 15–25-m runup of water onto the beach corresponding to a 2–3-m vertical run-up height. Some people reported several smaller waves. This was likely due to local effects. This wave was generated and amplified by a large-amplitude atmospheric gravity wave transiting southeastward over the eastern Gulf of Mexico. The atmospheric gravity wave and the water wave moved over a channel of water depth sufficient to maintain the waves in phase allowing resonation of the shallow water wave. Surface winds appeared to have a negligible affect, increasing only slightly (3–5 m s−1) along the path of the atmospheric gravity wave and opposing propagation of the water wave.