Classification of Natural Disaster Using Satellite &amp; Drone Images with CNN Using Transfer Learning

Agrawal, Tushar; Suraj, Suraj; Meleet, Merin

doi:10.1109/icses52305.2021.9633803

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…The suggested methodology uses federated learning to provide effective disaster classification while protecting the privacy of sensitive data. ResNet50 is one deep learning architecture that is used to further improve the system's accuracy and durability [2]. The proper categorization of disasters is essential for emergency response and resource management.…”

Section: Introductionmentioning

confidence: 99%

Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning

Shah

Patel

Shah

et al. 2023

Preprint

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Classification of disasters is crucial for effectivedisaster management and response. This paper proposes amethodology that combines computer vision techniques andfederated learning to improve the classification accuracy ofdisasters while addressing the issue of data transfer and the timesquandered doing so. This methodology employs computer visionalgorithms to analyse captured visual data from a variety ofsources. It seeks to accurately classify disasters such as wildfires,floods, earthquakes, and cyclones by extracting pertinent featuresand patterns from these images. Using federated learning toresolve the issues of data privacy and transfer latency is theproposed solution. Federated learning makes it possible to trainmodels on decentralised data sources without requiring datacentralization. Each participating device or data source trainsa local model using its own data, and only model updatesare shared and aggregated to create a global model. Extensiveexperiments utilising videos of actual disasters are conductedto evaluate the proposed methodology. The evaluation focuseson precision and effectiveness. This strategy is anticipated toresult in improved disaster classification models, making themappropriate for deployment in disaster management systems.

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Section: Introductionmentioning

confidence: 99%

Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning

Shah

Patel

Shah

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Recently, several works have focused on formulating the CNNs from drone view based images for developing several applications. For example, the work by Bidare et al [29] proposed sidewalk detection toward autonomous drone navigation development, while work by Agrawal and Meleet [30] introduced natural disaster classification through the drone view image. Other than that, the study by Hafeez et al [31] implemented CNNs model for monitoring the crop from the drone view image.…”

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confidence: 99%

Drone Image Based Illegal Crowd Detection For Covid-19 Disease Prevention Via Convolutional Neural Networks (CNNs) Transfer Learning

Ong

As’ari

2022

HumEnTech

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COVID-19 originated in Wuhan, China, in December 2019 and quickly became a global outbreak in January 2020. COVID-19 is a disease caused by SARS-CoV-2, which is a human transmission disease. Since it is a human transmission disease, thus mass gathering in public is not allowed to prevent the possible spread of COVID-19. However, the current monitoring technology, such as closed-circuit television (CCTV), only cover a limited area of the public and lack of mobility. Image classification is one of the approaches that can detect crowds in an image and can be done through either machine learning or deep learning approach. Recently, deep learning, especially convolutional neural networks (CNNs) outperform classical machine learning in image classification and the common approach for modelling CNN is through transfer learning. Thus, this study aims to develop a convolutional neural network that can detect illegal crowd gathering from offline drone view images through image classification using the transfer learning technique. Several models are used to train on the same dataset obtained, and the all-model performance is evaluated through a confusion matrix. Based on performance analysis, it shows that the ResNet50 model outperforms the VGG16 model and InceptionV3 model by achieving 95% test accuracy, 95% precision, 95% recall and 95% F1-score. In conclusion, it can be concluded that the deep learning approach uses a pre-trained convolutional neural network that can be used to classify object images in this study.

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A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images

Shianios,

Kyrkou,

Kolios

2023

Lecture Notes in Computer Science

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Classification of Natural Disaster Using Satellite & Drone Images with CNN Using Transfer Learning

Cited by 10 publications

References 7 publications

Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning

Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning

Drone Image Based Illegal Crowd Detection For Covid-19 Disease Prevention Via Convolutional Neural Networks (CNNs) Transfer Learning

A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images

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