Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood watermore »
Modest flooding can trigger catastrophic road network collapse due to compound failure
Compound failures occur when urban flooding coincides with traffic congestion, and their impact on network connectivity is poorly understood. Firstly, either three-dimensional road networks or the traffic on the roads has been considered, but not both. Secondly, we lack network science frameworks to consider compound failures in infrastructure networks. Here we present a network-theory-based framework that bridges this gap by considering compound structural, functional, and topological failures. We analyze high-resolution traffic data using network percolation theory to study the response of the transportation network in Harris County, Texas, US to Hurricane Harvey in 2017. We find that 2.2% of flood-induced compound failure may lead to a reduction in the size of the largest cluster where network connectivity exists, the giant component, 17.7%. We conclude that indirect effects, such as changes in traffic patterns, must be accounted for when assessing the impacts of flooding on transportation network connectivity and functioning.
- Award ID(s):
- 1832662
- Publication Date:
- NSF-PAR ID:
- 10363169
- Journal Name:
- Communications Earth & Environment
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2662-4435
- Publisher:
- Nature Publishing Group
- Sponsoring Org:
- National Science Foundation
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