People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Giannakeris, Panagiotis; Avgerinakis, Konstantinos; Karakostas, Anastasios; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1109/ivmspw.2018.8448732

Cited by 20 publications

(3 citation statements)

References 23 publications

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“…The variations of the object posture also cause di culty with recognition. This nding was consistent with a previous study (Giannakeris et al, 2018), which found that the recall of recognizing a re in an image was lower than that of recognizing a ood in an image, because the variable shape of the re made it more di cult to be recognized accurately.…”

Section: Resultssupporting

confidence: 92%

Detection of urban flood inundation from traffic images using deep learning methods

Zhong

Liu

Zheng

et al. 2023

Preprint

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Urban hydrological monitoring is the basis for urban hydrological analysis and storm flood control. However, current monitoring of urban hydrological data is insufficient, including flood inundation depth. This limits calibration and flood early warning ability of the hydrological model. In response to this limitation, a method for evaluating the depth of urban floods based on image recognition using deep learning was established in this study. This method can identify the submerged positions of pedestrians or vehicles in the image, such as pedestrian legs and car exhaust pipes, using the object recognition model YOLOv4. The mean average precision of water depth recognition in a dataset of 1177 flood images reached 89.29%. The established method extracted on-site, real-time, and continuous water depth data from images or video data provided by existing traffic cameras. This system does not require installation of additional water gauges and thus has a low cost and immediate usability.

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Section: Resultssupporting

confidence: 92%

Detection of urban flood inundation from traffic images using deep learning methods

Zhong

Liu

Zheng

et al. 2023

Preprint

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“…Bashiri et al [25] -who collected the MCIndoor20000 dataset which we also use in our work here -applied transfer learning (AlexNet) and achieved 90.40 percent accuracy on the original non-redundant part of their dataset. Giannakeris et al [30] presented a detection approach for classifying objects (e.g. flood, fire) in disaster scenarios.…”

Section: Related Workmentioning

confidence: 99%

A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations

Buettner¹,

Baumgartl²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Using convolutional neural networks we extend the work by Dugdale's group on socially relevant multi-agent systems in crisis and emergency situations by giving the artificial agent the ability to precisely recognize escape signs, doors and stairs for escape route planning. We build an efficient recognition module consisting of three blocks of a depth-wise separable convolutional layer, a max-pooling layer, and a batchnormalization layer before dense, dropout and classifying the image. A rigorous evaluation based on the MCIndoor20000 dataset shows excellent performance values (e.g. over 99.81 percent accuracy). In addition, our module architecture is 78 times smaller than the MCIndoor20000 benchmark-making it suitable for embedding in operational drones and robots.

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“…Manual inspection is used to define a line segmenting the submerged area in the photo and, finally, a homographic transformation of the flood line coordinates is used to project this information into the reference image, from which the flood height is derived (i.e., by analyzing the height of the part of a building that is submerged, and crossing this information with building height data from public records). Giannakeris et al described steps towards the development of a warning system, capable of detecting people and vehicles in danger over crowdsourced images [9]. In the proposed pipeline, a VGG-16 convolutional neural network, pre-trained on the Places365 dataset, is first used to classify images according to 3 classes of emergency events (i.e., fire, flood, or other).…”

Section: Related Workmentioning

confidence: 99%

Assessing flood severity from georeferenced photos

Pereira

Monteiro

Estima

et al. 2019

Proceedings of the 13th Workshop on Geographic Information Retrieval

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The use of georeferenced social media data in disaster and crisis management is increasing rapidly. Particularly in connection to flooding events, georeferenced images shared by citizens can provide situational awareness to emergency responders, as well as assistance to financial loss assessment, giving information that would otherwise be very hard to collect through conventional sensors or remote sensing products. Moreover, recent advances in computer vision and deep learning can perhaps support the automated analysis of these data. In this paper, focusing on ground-level images taken by humans during flooding events, we evaluate the use of deep convolutional neural networks for (i) discriminating images showing direct evidence of a flood, and (ii) estimating the severity of the flooding event. Considering distinct datasets (i.e., the European Flood 2013 dataset, and data from different editions of the MediaEval Multimedia Satellite Task), we specifically evaluated models based on the DenseNet and EfficientNet neural network architectures, concluding that these models for image classification can achieve a very high accuracy on both tasks. CCS CONCEPTS • Computing methodologies → Computer vision tasks; Neural networks; Visual content-based indexing and retrieval; Supervised learning.

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People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Cited by 20 publications

References 23 publications

Detection of urban flood inundation from traffic images using deep learning methods

Detection of urban flood inundation from traffic images using deep learning methods

A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations

Assessing flood severity from georeferenced photos

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