A Deep Learning Method to Accelerate the Disaster Response Process

Antoniou, Vyron; Potsiou, Chryssy

doi:10.3390/rs12030544

Cited by 24 publications

(12 citation statements)

References 51 publications

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“…For disaster management, mostly deep learning and remote sensing images are used for monitoring and detection of natural or manmade disasters. Some of the papers have focused on disaster preparedness and disaster management side such as Antoniou & Potsiou [22]. Most of the paper focused on the monitoring/detection of the hazard itself than that of mapping its impact.…”

Section: Disaster Monitoring and Post-disaster Estimationmentioning

confidence: 99%

Methods in the spatial deep learning: current status and future direction

Mishra¹,

Dahal

Luintel

et al. 2022

Spat. Inf. Res.

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A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.

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Section: Disaster Monitoring and Post-disaster Estimationmentioning

confidence: 99%

Methods in the spatial deep learning: current status and future direction

Mishra¹,

Dahal

Luintel

et al. 2022

Spat. Inf. Res.

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“…AI development can be evident in disaster preparedness, crowdsourced information systems, rescue, and humanitarian distribution [15,19]. Although AI has many forms, this report concentrates on using AI such as robotics, drones, machine learning, deep learning, sensors, and algorithms utilized in the context of catastrophe prediction and facilitating speedier rescue and relief delivery activities [20][21][22][23]. Robotics and robots have been around for decades, but due to the recent increasing trend in sensor and compute The process illustrates four stages of disaster management, which implies interlinked activities further classified into three activities: Pre-disaster activities, current, and postdisaster activities.…”

Section: Artificial Intelligence and Disaster Managementmentioning

confidence: 99%

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

et al. 2021

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Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

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“…For various reasons (Gilmer et al 2018), including the immaturity of the AI and ML fields (Heaven 2019), humans are still needed in the decision-making process to provide their intuition, imagination, and reasoning, which are not possible to mimic via AI or ML algorithms. However, as AI and ML are becoming increasingly accessible, the collaboration of AI and ML can provide benefits in time, resources, and effort needed for both simple and critical tasks (Antoniou and Potsiou 2020). Moreover, the intertwining of volunteerism with the power of AI and ML can boost citizen science projects' effectiveness in user engagement and project usability.…”

Section: Future Of Apps In Citizen Sciencementioning

confidence: 99%

Citizen Science in the Digital World of Apps

Lemmens

Antoniou²,

Hummer

et al. 2021

The Science of Citizen Science

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In this chapter, we highlight the added value of mobile and web apps to the field of citizen science. We provide an overview of app types and their functionalities to facilitate appropriate app selection for citizen science projects. We identify different app types according to methodology, data specifics, and data collection format.The chapter outlines good practices for creating apps. Citizen science apps need to ensure high levels of performance and usability. Social features for citizen science projects with a focus on mobile apps are helpful for user motivation and immersion and, also, can improve data quality via community feedback. The design, look and feel, and project identity are essential features of citizen science apps.We provide recommendations aimed at establishing good practice in citizen science app development. We also highlight future developments in technology and, in particular, how artificial intelligence (AI) and machine learning (ML) can impact citizen science projects.

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A Deep Learning Method to Accelerate the Disaster Response Process

Cited by 24 publications

References 51 publications

Methods in the spatial deep learning: current status and future direction

Methods in the spatial deep learning: current status and future direction

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Citizen Science in the Digital World of Apps

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