Few-Shot Learning for Post-Earthquake Urban Damage Detection

Among natural disasters, earthquakes are recorded to have the highest rates of human loss in the past 20 years. Their unexpected nature has severe consequences on both human lives and material infrastructure, demanding urgent action to be taken. For effective emergency relief, it is necessary to gain awareness about the level of damage in the affected areas. The use of remotely sensed imagery is popular in damage assessment applications; however, it requires a considerable amount of labeled data, which are not always easy to obtain. Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address data insufficiency and imbalance in post-earthquake urban damage classification. While small datasets have been tested against binary classification problems, which usually divide the urban structures into collapsed and non-collapsed, the potential of limited training data in multi-class classification has not been fully explored. To tackle this gap, four models were created, following different data balancing methods, namely cost-sensitive learning, oversampling, undersampling and Prototypical Networks. After a quantitative comparison among them, the best performing model was found to be the one based on Prototypical Networks, and it was used for the creation of damage assessment maps. The contribution of this work is twofold: we show that oversampling is the most suitable data balancing method for training Deep Convolutional Neural Networks (CNN) when compared to cost-sensitive learning and undersampling, and we demonstrate the appropriateness of Prototypical Networks in the damage classification context.

Urban Collectives and insurgency to fight COVID-19: an analysis of social media content

The high incidences of COVID-19 cases in low-income communities, the context of disinformation around the disease and the vacuum of public policies have made low income communities more vulnerable to the pandemic. Considering that scenario, this paper analyzes how urban collectives have used social media to create and share narratives about COVID-19. We frame those contributions through a lens of insurgency, an area of planning studies that is based on grassroots counter-hegemonic actions. We conduct a sentiment and thematic analysis of Instagram posts of urban collectives in Fortaleza to show how social media has been appropriated as a space to cope with the virus and support insurgency. Our findings show, even though most strategies are geared towards emergency relief, collectives also harness the atmosphere of crisis brought by COVID-19 to raise awareness to other structural issues. Collectives promote insurgency by creating their own content, information and research material about COVID-19 in their communities and by partnering with institutions to scale up their claims.

Research on Emergency Material Allocation Mode of Sudden Disaster with Improved NSGA-II

Background:In order to solve the problems of redundancy, unfairness, low satisfaction and high cost of emergency material allocation caused by unreasonable allocation effectively in the case of sudden disasters, and minimize the economic cost, punishment cost and maximizing the satisfaction rate of disaster victims, a 3-level network emergency material allocation mode based on big data is proposed in this paper.Methods:Taking the loss degree and the dynamic change of material demand in the disaster stricken areas as constraints, the demand forecasting, scheduling optimization, targeted allocation and disaster victims' satisfaction model based on emergency relief materials is constructed. The Sample Average Approximation method and improved NSGA-II algorithm are designed to solve the problem.Results:Compared with the results obtained by the improved NSGA-II, the value is significantly reduced. From the fairness evaluation results of the two model distribution schemes, the model obtained by the improved NSGA-II is more suitable for the distribution of emergency supplies with fair distribution requirements.Conclusions:It can be concluded that the 3-level network allocation mode and improved NSGA-II can solve emergency relief materials allocation based on big data effectively. The next step is to design scheduling model with all feasible medical supplies allocation route to improve the practicability of the model.

Emergency Relief Station

<p>This thesis explores the potential large public architecture offers for efficient transformation into a relief station in post-disaster situations. The increase in catastrophic disasters globally has demonstrated a widespread lack of preparedness in these situations. There is a shortage of safe, comfortable, and self-sufficient hubs for coordinating relief activity, for sheltering temporarily and providing emergency care to disaster victims, and relief personnel. Disaster relief generally involves the urgent dispatching of medical supplies, food, water, blankets, sanitation systems, temporary shelters, and relief personnel to affected locations. Following the recent devastating spate of earthquakes and flood disasters in New Zealand makeshift relief centres were set up in public parks, schools, and community facilities to house displaced victims. These were set up to function as efficient relief stations. The facilities also depend heavily on deployed relief supplies and the public for donations and support. In addition, these relief hubs are quickly overwhelmed and in adverse weather conditions, they are inadequate for providing warm, dry, hygienic, and safe environments for sheltering large numbers of people including the injured and the sick. This thesis explores how an airport may be designed for a dual purpose and the feasibility and complexity of planning and designing public space for transformation into a disaster relief station.</p>

Emergency Relief Station

<p>This thesis explores the potential large public architecture offers for efficient transformation into a relief station in post-disaster situations. The increase in catastrophic disasters globally has demonstrated a widespread lack of preparedness in these situations. There is a shortage of safe, comfortable, and self-sufficient hubs for coordinating relief activity, for sheltering temporarily and providing emergency care to disaster victims, and relief personnel. Disaster relief generally involves the urgent dispatching of medical supplies, food, water, blankets, sanitation systems, temporary shelters, and relief personnel to affected locations. Following the recent devastating spate of earthquakes and flood disasters in New Zealand makeshift relief centres were set up in public parks, schools, and community facilities to house displaced victims. These were set up to function as efficient relief stations. The facilities also depend heavily on deployed relief supplies and the public for donations and support. In addition, these relief hubs are quickly overwhelmed and in adverse weather conditions, they are inadequate for providing warm, dry, hygienic, and safe environments for sheltering large numbers of people including the injured and the sick. This thesis explores how an airport may be designed for a dual purpose and the feasibility and complexity of planning and designing public space for transformation into a disaster relief station.</p>

Multi-attribute decision making method based on probabilistic linguistic term sets and its application in the evaluation of emergency logistics capacity

The occurrence of public health emergency will cause huge economic losses and casualties, which posed a huge threat to the economic and social development. In response to the emergency, a large amount of emergency relief supplies will be transported to the affected areas. Faced with this public health emergency of international concern, the concept of emergency logistics capacity and the evaluation model based on probabilistic linguistic term sets are proposed. In this paper, the emergency logistics capability evaluation is transformed into user demand evaluation, and the importance of each index of emergency logistics capability is determined by using Quality Function Deployment (QFD) and prospect theory. Under the probabilistic language information environment, a multi-attribute decision making method is established by using TODIM method. Finally, an example is given to verify the feasibility of the proposed method.