Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Understanding Disaster Preparedness Level in the South Indian City of Chennai
    (Springer Science and Business Media Deutschland GmbH, 2022) Kolathayar, S.; Priyatham, K.; Karan Kumar, V.; Rohith, V.R.; Nikil, S.
    Understanding and developing frameworks to quantify disaster preparedness levels is the first step that should be taken in the process of building a disaster-resilient society. This paper presents the use of Disaster Preparedness Index Tool to assess the preparedness level of each and every individual to face an impending natural disaster. It is a survey-based tool to analyze the preparedness indices of the respondents over psychological and various social factors. The Disaster Preparedness Index (DPI) is a valid and reliable tool that assess individuals on a three-level scale with index values ranging between 0–14. An attempt was made to quantify disaster preparedness of the South Indian city of Chennai by the application of the developed Disaster Preparedness Index Tool and the disaster mindset of the residents of the city was studied. The influence factors like psychological mindset and socio-economic conditions over the levels of disaster preparedness were analyzed and presented in the paper. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Assessing Damage of Natural Disasters from Satellite Imagery Using a Deep Learning Model
    (Springer Science and Business Media Deutschland GmbH, 2023) Tikle, S.; Jidesh, P.; Smitha, A.
    Natural disasters are events that arise anywhere on the planet. It causes enormous devastation and places entire cities in need of significant support. The capability to swiftly and precisely deploy rescue services in the affected regions is critical for reducing the impact and saving lives. A two-step model is developed in an attempt to resolve this problem by using satellite images as input. The model draws attention to the structures such as buildings, which are severely damaged. The current deep learning-based computer vision models use pre- and post-disaster satellite images to semantically infer the level of damage to individual buildings after natural disasters. This model alleviates an important roadblock in disaster managerial decisions by simplifying the evaluations of damages caused to the building. We used DeepLabv3+ for semantic segmentation and a custom CNN model for image classification to analyze disaster-related images. This paper describes how satellite data and proficient image analysis may effectively be used to conduct disaster and crisis management to assist jobs that require fast mappings. This model’s performance and accuracy are sub-optimal and are being studied to further improvisations. However, it surpasses the current cutting-edge model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.