Machine Vision Based Flood Depth Estimation Using Crowdsourced Images of Humans

P, Vallimeena; Nair, Bhavana B.; Rao, Sethuraman N

doi:10.1109/iccic.2018.8782363

Cited by 19 publications

(2 citation statements)

References 2 publications

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“…developed a kernel function-based flood mapping model to map the probability distribution of flood occurrence in the study area using water elevation points of Twitter and stream gauges. Reference 18 introduced computer vision to identify crowdsourced flood photos, showing that social media and crowdsources can be used to complement datasets developed based on traditional remote sensing and eyewitness reports. Rosser et al 19 .…”

Section: Introductionmentioning

confidence: 99%

Combining remote sensing and social media data for flood mapping: a case study in Linhai, Zhejiang Province, China

Luo

Liao

Guo

2023

J. Appl. Rem. Sens.

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The rapid mapping of flood inundation is essential for timely assessment of damage and post-disaster recovery. Remote sensing technology provides clear spatial information for mapping flood inundation, but its real-time (RT) images are often not available due to the severe weather conditions during the disaster events; the combination of RT data can better compensate for the deficiency. In this study, a near RT (NRT) flood inundation probability mapping method based on post-event NRT remote sensing data combined with RT volunteer geographic information (VGI) was proposed for mapping the 2019 flood in Linhai City, Zhejiang Province. First, a probabilistic index distribution (PID) layer was constructed from high-resolution digital elevation model data using an inverse distance-weighted height filter based on each VGI point. Then, a quality evaluation method for non-reference data was introduced to evaluate the validity of the VGI data. The final flood probability map was generated by PID weighting. The results show that, by fusing NRT images and RT data, the proposed model for mapping flood inundation probability is more robust and enhances the spatial characteristics of the flood inundation probability index, allowing it to enable emergency responders to quickly identify areas requiring urgent attention.

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Section: Introductionmentioning

confidence: 99%

Combining remote sensing and social media data for flood mapping: a case study in Linhai, Zhejiang Province, China

Luo

Liao

Guo

2023

J. Appl. Rem. Sens.

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“…Photogrammetric techniques can detect camera movements to make this process more robust to external disturbances (Lin et al, 2018). Alternatively, reference objects within an image, including static objects such as structural elements (Jafari et al, 2021), or dynamic objects such as people (Vallimeena et al, 2018) and cars (Park et al, 2021), can calibrate pixel dimensions to estimate water level. A few studies have estimated outlet flow rates using water level information and computational fluid dynamics (Fach et al, 2008); however, extreme rainfall events limit the collection of water level information.…”

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confidence: 99%

Computer Vision Approach for Tile Drain Outflow Rate Estimation

Young¹,

Han²,

Peschel³

2023

Applied Engineering in Agriculture

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HighlightsVision-based approach for detecting outlet flow of tile drains in a laboratory environment.Method accurately detects and estimates flow within 12% or less of ground truth flow rate.A real-time application was developed that provides estimated flow rates from collected video.Abstract. This article presents a computer vision-based approach for monitoring water flow at outlet points of a tile drain system. The approach relies only on video capture of events at outlet points, thus a camera can be installed remotely and without contact with water. The algorithm detects, identifies, and tracks flows by motion, shape, and color features and measures flow rate based on a proposed model and two provided dimensions. The software was tested in a laboratory environment with three different target flow rate conditions: 0.312, 0.946, and 1.58 L/s (5, 15, and 25 gal/min). Flow rates reported by the computer vision approach are within 12% of the ground-truth flow rate baseline. The results of this work show that computer vision can be used as a reliable method for monitoring outlet flows from free-standing outlet structures under laboratory conditions. This work opens the possibility of applying computer vision techniques in tile drain monitoring from outlet points with mobile video recording devices in the field. Keywords: Keywords., Morphological transformations, Outflow detection, Outlet flow, Real-time application.

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