Review on computer vision techniques in emergency situations

Lopez-Fuentes, Laura; Weijer, Joost van de; González-Hidalgo, Manuel; Skinnemoen, Harald; Bagdanov, Andrew D.

doi:10.1007/s11042-017-5276-7

Cited by 52 publications

(20 citation statements)

References 157 publications

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“…Drones have already been used by humanitarian organizations in places like Haiti and the Philippines to map areas after a natural disaster, using high-resolution embedded cameras, as documented in a recent United Nation report [1]. While research efforts have mostly focused on developing videobased solutions for this task [2], UAV-embedded audio-based localization has received relatively less attention [3], [4], [5], [6], [7]. Though, UAVs equipped with a microphone array could be of critical help to localize people in emergency situations, in particular when video sensors are limited by a lack of visual feedback due to bad lighting conditions (night, fog, etc.)…”

Section: Introductionmentioning

confidence: 99%

Audio-Based Search and Rescue With a Drone: Highlights From the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions]

Deleforge

Carlo

Strauss

et al. 2019

IEEE Signal Process. Mag.

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Section: Introductionmentioning

confidence: 99%

Audio-Based Search and Rescue With a Drone: Highlights From the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions]

Deleforge

Carlo

Strauss

et al. 2019

IEEE Signal Process. Mag.

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“…The incremental usage of surveillance cameras in areas prone to natural disasters has raised interest in such events in the scientific community, especially in the computer vision [2] domain.Background subtraction techniques together with morphological operations and color probability has been used to determine water presence in videos [3]. When it comes to static images, most algorithms are based on light, texture and color features, and on clustering or classification models to segment the regions containing water [4].…”

Section: Related Workmentioning

confidence: 99%

River segmentation for flood monitoring

Lopez-Fuentes

Rossi

Skinnemoen

2017

2017 IEEE International Conference on Big Data (Big Data)

Self Cite

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Abstract-Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation.

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“…AI has also been used on images provided by citizens affected by flooding for emergency responders to have situational awareness. In [8], the authors explored the use of algorithms based on ground photography shared within social networks. Use of specific algorithms for satellite images or aerial imagery [9] to detect flood extent was also explored.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events

Saravi

Kalawsky

Joannou

et al. 2019

Water

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The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.

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Review on computer vision techniques in emergency situations

Cited by 52 publications

References 157 publications

Audio-Based Search and Rescue With a Drone: Highlights From the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions]

Audio-Based Search and Rescue With a Drone: Highlights From the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions]

River segmentation for flood monitoring

Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events

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