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Detecting flash floods in real-time and taking rapid actions are of utmost importance to save human lives, loss of infrastructures, and personal properties in a smart city. In this paper, we develop a low-cost low-power cyber-physical System prototype using a Raspberry Pi camera to detect the rising water level. We deployed the system in the real word and collected data in different environmental conditions (early morning in the presence of fog, sunny afternoon, late afternoon with sunsetting). We employ image processing and text recognition techniques to detect the rising water level and articulate several challenges in deploying such a system in the real environment. We envision this prototype design will pave the way for mass deployment of the flash flood detection system with minimal human intervention. 

Computer Vision and Image Processing are emerging research paradigms. The increasing popularity of social media, micro- blogging services and ubiquitous availability of high-resolution smartphone cameras with pervasive connectivity are propelling our digital footprints and cyber activities. Such online human footprints related with an event-of-interest, if mined appropriately, can provide meaningful information to analyze the current course and pre- and post- impact leading to the organizational planning of various real-time smart city applications. In this paper, we investigate the narrative (texts) and visual (images) components of Twitter feeds to improve the results of queries by exploiting the deep contexts of each data modality. We employ Latent Semantic Analysis (LSA)-based techniques to analyze the texts and Discrete Cosine Transformation (DCT) to analyze the images which help establish the cross-correlations between the textual and image dimensions of a query. While each of the data dimensions helps improve the results of a specific query on its own, the contributions from the dual modalities can potentially provide insights that are greater than what can be obtained from the individual modalities. We validate our proposed approach using real Twitter feeds from a recent devastating flash flood in Ellicott City near the University of Maryland campus. Our results show that the images and texts can be classified with 67% and 94% accuracies respectively.