Estimating Critical Infrastructure Inundation Levels Using Crowdsourced Images and AI-driven Data Analytics

Successive flood events have brought new challenges to human life, civil infrastructure system and the environment in the southeast United States. To address flooding impacts, this study developed artificial intelligence algorithms (image processing approaches) to detect floodwater extent on inundated roadways from image data captured by smartphones, traffic cameras, etc. A sample dataset collected in real-time from recent flooding in South Carolina, USA and location-matched reference images are used to compute flood depth and inundation extend. Different algorithms have been used including Cloud Vision API in the Google Cloud Console and Convolutional Neural Networks (CNNs). To create high-quality training datasets of annotated images, at least 100-200 images are required to train the algorithm and label the objects. For the sake of comparing the image quality of the compressed image with the original image, we calculated and compared the structural similarity (SSIM) index of the two images in addition to mean square error (MSE) or peak signal-to-noise ratio (PSNR). Analysis suggests that differences in image resolution and lighting, and environmental conditions have significant impact on annotating an image with a label and score. Further, crowdsourced images showed discrepancies in labeling dry/flooded image pairs specifically for differentiating “flood”, “floodplain”, “waterway”, and “road”. Specifically, all models showed high precision in detecting critical infrastructures (>70% precision) in the images such as road, bridges, and reservoirs while revealed significant challenges in detecting flood and water (50%-55% precision). More research is underway to integrate image processing algorithm with the watershed geometry for flood severity and inundation assessment and to apply the results for emergency response purposes in real time.