4,808 research outputs found

    Deep learning models for road passability detection during flood events using social media data

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    During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently

    Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques in Blue Tarpaulin Inspection

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    This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature v identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize or label the objects, or classify the pixels in the picture. Finally, supervised classification is based on a system in which a user picks sample pixel in an image that are indicative of certain classes and then tells image-processing software to categorize the other pixels in the picture using these training sites as references. To group pixels together, the user also specifies the limits for how similar they must be. The number of classifications into which the image is categorized is likewise determined by the user. The importance of tracking blue roofs is multifaceted. Structures with roof damage from natural disasters face many immediate dangers, such as further water and wind damage. These communities are at a critical moment as responding to the damage efficiently and effectively should occur in the immediate aftermath of a disaster. In part due to strategies such as FEMA and the United States Army Corps of Engineers’ (USACE) Operation Blue Roof, most often blue tarpaulins are installed on structures to prevent further damage caused by wind and rain. From a Unmanned Arial Vehicles (UAV) perspective, these blue tarps stand out amid the downed trees, devastated infrastructure, and other debris that will populate the area. Understanding that recovery can be one of the most important stages of Emergency Management, testing techniques vi for speed, accuracy, and effectiveness will assist in creating more effective Emergency Management (EM) specialists

    Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

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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

    CrisMap: A Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

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    Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis- related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy

    Social Media Analytics in Disaster Response: A Comprehensive Review

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    Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

    Social media analytics with applications in disaster management and COVID-19 events

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    Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv
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