Analysis of satellite images for disaster detection

Amit, Siti Nor Khuzaimah Binti; Shiraishi, Shinya; Inoshita, Tetsuo; Aoki, Yoshimitsu

doi:10.1109/igarss.2016.7730352

Cited by 48 publications

(23 citation statements)

References 4 publications

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“…Liu et al [36] proposed a deep architecture along with a wavelet transformationbased pre-processing scheme for the identification of disaster affected areas in satellite imagery. Amit et al [9] propose a CNN-based deep architecture composed of five weighted layers for landslides and flood detection in satellite imagery.…”

Section: Disaster Events Detection In Satellite Imagerymentioning

confidence: 99%

Automatic detection of passable roads after floods in remote sensed and social media data

Ahmad

Pogorelov

Riegler

et al. 2019

Signal Processing: Image Communication

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This paper addresses the problem of floods classification and floods aftermath detection utilizing both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods are carried out on the largescale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.

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Section: Disaster Events Detection In Satellite Imagerymentioning

confidence: 99%

Automatic detection of passable roads after floods in remote sensed and social media data

Ahmad

Pogorelov

Riegler

et al. 2019

Signal Processing: Image Communication

View full text Add to dashboard Cite

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“…LeCun et al in 1998 first proved high performance of a CNN model for handwriting recognition [30]. Since then, CNNs have been becoming more and more popular in more different fields, such as natural language processing [31], disaster climate discovery [32], and clinical medicine [33], which all proved the great performance of the CNNs. What is more, to be noted, a CNN is used to analyze visual images at the very beginning and it can reduce the complexity of the model by sharing convolution weight and in a way help solving an overfitting problem.…”

Section: B Detection Modelmentioning

confidence: 99%

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Liu

Lee

2020

IEEE Access

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Although a variety of techniques to detect malicious websites have been proposed, it becomes more and more difficult for those methods to provide a satisfying result nowadays. Many malicious websites can still escape detection with various Web spam techniques. In this paper, we first summarize three types of Web spam techniques used by malicious websites, such as redirection spam, hidden IFrame spam, and content hiding spam. We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. In order to verify the effectiveness of the method, two different experiments have been conducted. First, the proposed method was tested based on a constructed complex dataset. We present comparison results between the proposed method and representative machine learning-based detection algorithms. Second, the proposed method was tested to detect malicious websites in a real-world Web environment for three months. These experimental results illustrate that the proposed method has a better performance and is applicable to a practical Web environment. INDEX TERMS Convolutional neural network, machine learning, malicious website detection.

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“…In our discussion we prefer instead to focus our attention on very specific information sources that can be adopted for benchmarking, with the aim of developing algorithms for disaster detection. 11 https://www.preventionweb.net/risk/datasets# panel1-4…”

Section: Datasetsmentioning

confidence: 99%

Natural disasters detection in social media and satellite imagery: a survey

Said

Ahmad

Riegler

et al. 2019

Multimed Tools Appl

109

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The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disasterrelated multimedia content. Satellite imagery has also been widely explored for disasters analysis. In this paper, we survey the existing literature on disaster detection and analysis of the retrieved information from social media and satellites. Literature on disaster detection and analysis of related multimedia content on the basis of the nature of the content can be categorized into three groups, namely (i) disaster detection in text; (ii) analysis of disaster-related visual content from social media; and (iii) disaster detection in satellite imagery. We extensively review different approaches proposed in these three domains. Furthermore, we also review benchmarking datasets available for the evaluation of disaster detection frameworks. Moreover, we provide a detailed discussion on the insights obtained from the literature review, and identify future trends and challenges, which will provide an important starting point for the researchers in the field.

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Analysis of satellite images for disaster detection

Cited by 48 publications

References 4 publications

Automatic detection of passable roads after floods in remote sensed and social media data

Automatic detection of passable roads after floods in remote sensed and social media data

CNN Based Malicious Website Detection by Invalidating Multiple Web Spams

Natural disasters detection in social media and satellite imagery: a survey

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