Modeling urban growth using video prediction technology: A time‐dependent convolutional encoder–decoder architecture

Jaad, Ahmed; Abdelghany, Khaled

doi:10.1111/mice.12503

Cited by 11 publications

(6 citation statements)

References 59 publications

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“…In this regard, ML assists urban growth and infrastructure development projection to better recognize areas in the high risk of flood due to high exposure. ML models can also help identify the potential land‐use change in the future that requires revisiting flood mitigation measures (Hosseini et al., 2020; Jaad & Abdelghany, 2020; Wagenaar et al., 2019). For example, Genetic Algorithm Rule‐Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST) are used to map the flood risk considering factors such as population and urban density as well as socioeconomic factors (Darabi et al., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Specifically, the UAV collected video frames are used to generate frames predicting future locations and posture of objects assisting the worker, including fu- ture speed and direction, effectively obtaining proactive safety information. Again for monitoring purposes, even Jaad et al 73 propose a future video frame generation-based system to predict the growth of urban areas during the years starting from historical images. Finally, unlike previous works, Cai et al 74 focus on an optimization-based approach of selfadapted video magnification for subtle color and motion amplification that can be useful, for example, to estimate heartbeat by observing blood flow or other vital signs inside video sequences; once again highlighting how video synthesis might be crucial to solve diverse and complex tasks.…”

Section: Related Workmentioning

confidence: 99%

Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

Avola,

Cascio,

Cinque

et al. 2022

Preprint

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The increasing availability of wireless access points (APs) is leading towards human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread visual sensors, where the signals enable to address well-known vision-related problems such as illumination changes or occlusions. Indeed, using image synthesis techniques to translate radio frequencies to the visible spectrum can become essential to obtain otherwise unavailable visual data. This domain-to-domain translation is feasible because both objects and people affect electromagnetic waves, causing radio and optical frequencies variations. In literature, models capable of inferring radio-to-visual features mappings have gained momentum in the last few years since frequency changes can be observed in the radio domain through the channel state information (CSI) of Wi-Fi APs, enabling signal-based feature extraction, e.g., amplitude. On this account, this paper presents a novel two-branch generative neural network that effectively maps radio data into visual features, following a teacher-student design that exploits a cross-modality supervision strategy. The latter conditions signal-based features in the visual domain to completely replace visual data. Once trained, the proposed method synthesizes human silhouette and skeleton videos using exclusively Wi-Fi signals. The approach is evaluated on publicly available data, where it obtains remarkable results for both silhouette and skeleton videos generation, demonstrating the effectiveness of the proposed cross-modality supervision strategy.

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“…Other techniques of steered predictions for self-driving vehicles have been investigated, including utilizing clear occurrence and integrating APS pictures in an end-to-end manner. A stacking spatial LSTM system, on the other hand, was suggested in [6] that delocalize 6-Dof positions from the occurrences, and the vision flow forecasts based on the application of supervised decoder and encoder infrastructures that have been recommended in [7], which makes use of the learning algorithms to produce pseudo labels that identify objects in the ego movement. By retraining a CNN on APS pictures, the pseudo categories are translated to the event image.…”

Section: Literature Review Y Wang and H Jiang Inmentioning

confidence: 99%

Range Imaging and Video Generation using Generative Adversarial Network

2021

JBSHA

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Latency, high temporal pixel density, and dynamic range are just a few of the benefits of event camera systems over conventional camera systems. Methods and algorithms cannot be applied directly because the output data of event camera systems are segments of synchronization events and experiences rather than precise pixel intensities. As a result, generating intensity photographs from occurrences for other functions is difficult. We use occurrence camera-based contingent deep convolutional connections to establish images and videos from a variable component of the occasion stream of data in this journal article. The system is designed to replicate visuals based on spatio-temporal intensity variations using bundles of spatial coordinates of occurrences as input data. The ability of event camera systems to produce High Dynamic Range (HDR) pictures even in exceptional lighting circumstances, as well as non-blurry pictures in rapid motion, is demonstrated. Furthermore, because event cameras have a transient response of about 1 s, the ability to generate very increased frame rate video content has been evidenced, conceivably up to 1 million arrays per second. The implementation of the proposed algorithms are compared to density images recorded onto a similar gridline in the image of events based on the application of accessible primary data obtained and synthesized datasets generated by the occurrence camera simulation model.

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Modeling urban growth using video prediction technology: A time‐dependent convolutional encoder–decoder architecture

Cited by 11 publications

References 59 publications

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

Range Imaging and Video Generation using Generative Adversarial Network

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