Unconstrained Flood Event Detection Using Adversarial Data Augmentation

Pouyanfar, Samira; Tao, Yudong; Sadiq, Saad; Tian, Haiman; Tu, Yuexuan; Wang, Tianyi; Chen, Shu‐Ching; Shyu, Mei‐Ling

doi:10.1109/icip.2019.8802923

Cited by 22 publications

(16 citation statements)

References 12 publications

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“…However, collecting large‐scale image dataset with labels is time‐consuming, tedious, and expensive, especially in this problem. Therefore, in this study, a powerful technique called data augmentation (Pouyanfar, Tao et al., 2019) is utilized to generate synthetic training images from the existing data. This method helps the deep learning model to be generalized to the new conditions and environments that are never experienced beforehand.…”

Section: Methodsmentioning

confidence: 99%

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Valikhani

Jahromi

Pouyanfar

et al. 2020

Computer aided Civil Eng

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Casting concrete at different ages for new construction and repairing or retrofitting concrete structures requires a sufficient bond between concrete casts. The bond strength between different casts is attributed to surface roughness. Surface roughness can be achieved in many ways, such as water‐jetting or sandblasting. To evaluate the degree of surface roughness, qualitative and quantitative methods are introduced by many researchers; however, several drawbacks are associated with most of these methods, including cost, availability, human errors, and inability to assess old structures from prior inspection records. Two novel industrial implementation methods are introduced in this paper to estimate, quantitatively, the concrete surface roughness from images with sufficient resolution. In the first application method, a digital image processing method is proposed to distinguish the coarse aggregate from cement paste, and a new index is presented as a function of aggregate proportional area to the surface area. In the second application method, data augmentation and transfer learning techniques in computer vision and machine learning are utilized to classify new images based on predefined images during the learning process. Both application methods were related to a well‐established method of 3D laser scanning from sandblasted concrete surfaces. Finally, a brand new set of images of sandblasted surfaces was used to test and validate both methods. The results show that both methods successfully estimate the concrete surface roughness with an accuracy of more than 93%.

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Section: Methodsmentioning

confidence: 99%

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Valikhani

Jahromi

Pouyanfar

et al. 2020

Computer aided Civil Eng

Self Cite

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“…It has also been used to forecast floods in urban areas. Research conducted at the University of Dundee, United Kingdom, used this tool to determine the urban flooding rate using crowd-sourced data collected from social media platforms [18].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

“…Different countries around the world have prioritised to achieve this target which can be achieved by tapping into human resources, developing cuttingedge technologies and increasing adaptability through authorities. Resilience infrastructure and capabilities should focus on reducing calamities and economic losses [16][17][18]. The only way forward is to timely detect the hazard and minimise it with the application of appropriate technology.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

et al. 2021

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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

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“…Neubig et al (2011) introduces a real-time system for the Japan 2011 earthquake that classifies the relatedness of the posts and extracts surface information like named entities. Other approaches include BiLSTM models for tweet classification (Ma), event detection based on Twitter streams (Sakaki et al, 2010), adversarial data augmentation for image classification (Pouyanfar et al, 2019) and domain-adaptation across different events using an adversarial network.…”

Section: Related Workmentioning

confidence: 99%

Event-Related Bias Removal for Real-time Disaster Events

Maza¹,

Spiliopoulou²,

Hovy³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on outof-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.

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Unconstrained Flood Event Detection Using Adversarial Data Augmentation

Cited by 22 publications

References 12 publications

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

Event-Related Bias Removal for Real-time Disaster Events

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