Real-time water level monitoring using live cameras and computer vision techniques

Navid Jafari, Xin Li, Qin Chen, Canyu Le, Yongqing Liang

Abstract: Characterizing urban hydrographs during rain storms, hurricanes, and river floods is important to decrease loss of lives and assist emergency responders with mapping disruptions to operation of major cities. High water marks, stream gages, and rapidly deployed instrumentation are the current state-of-practice for hydrological data during a flood event. The objective of this study was to develop technology that can provide accurate and timely flood hydrographs while harnessing the Big Data generated from videos and images. In particular, levels are predicted from images by using reference objects as a scale. The novelty of this work involved leveraging object-based image analysis (OBIA), which used image segmentation training algorithms to differentiate areas of images or videos. In particular, the deep learning-based semantic segmentation technique was trained using images from an MIT database along with …