Abstract: Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated.

Abstract: Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.

Abstract: Globally floods are categorized as one of most devastating natural disasters and annually causing a major loss to human lives and economy. For rapid damage assessment and planning relief activities a large scale spatio-temporal overview is required to assist local authorities. This paper aims to provide an overview of a Sentinel-1 based near-real time flood mapping/monitoring service; which is implemented as an operational service under the framework of I-REACT (Improving Resilience to Emergencies through Advanced Cyber Technologies) project.

Abstract: This document will discuss the current methodologies used by New Zealand DART and USAR teams to collect manage, analyse and report on information gathered during the initial and subsequent phases of deployments to a sudden onset disaster (SOD). This will include some of the experiences that have formed the current methodology and the outcomes of disaster events with new methodologies applied. It will further identify and discuss the current systems and processes in place and how they have come about, and then identify a range of opportunities and issues that exist within the Geospatial Intelligence environment to be more effective, both in systems and the development of partnerships to enhance the usability and intuitive nature of these systems and methods. Finally, the discussion will look to identify a future state for responders to SOD's and the ability and outcomes of proposed and imagined future systems, leveraging off the current Esri packages to provide a starting platform and a desired end state.

Abstract: Interferometric synthetic aperture radar (InSAR) has been widely used for mapping terrain and monitoring ground deformation. For example, the advanced time series InSAR (TS-InSAR) technique has been increasingly used to measure mm-level urban deformation. Subsidence from underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan in Southern China, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK). We found that significant surface displacement rates occurred in the study area varying from -35 mm/year to 10 mm/year. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. A detailed analysis on the sinkhole collapsed in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Continuous monitoring of the deforming areas is important in order to minimise the risk of land subsidence and prevention of disasters.

Abstract: Urban Search and Rescue (USAR) teams are particularly exposed to the risk of collapse of buildings due to aftershocks, making concept of earthquake early warning (EEW) particularly interesting. In addition to scientific advances in EEW, it is crucial to understand what are the real expectations and needs of USAR teams, and to what extent EEW solutions could meet them. In this study, we conduct a survey to collect insights from USAR rescuers: it highlights that aftershocks are a major concern for them. In this context, we find that the concept of EEW is very favorably received by the respondents, who consider different types of possible actions upon receipt of an early warning. This study provides a basis for the functional specifications of future solutions of EEW useful to all USAR teams, as well as for the definition of their modalities of engagement on the field.

Abstract: A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art.

Abstract: Society is faced with a growing amount of property damage and casualties from man-made and natural disasters. Developing societal resilience to those disasters is critical but challenging. In particular, societal resilience is jointly determined by federal and local governments, private and non-profit sectors, and private citizens. We present a sequence of games among players such as federal, local, and foreign governments, private citizens, and adaptive adversaries. In particular, the governments and private citizens seek to protect lives, property, and critical infrastructure from both adaptive terrorists and non-adaptive natural disasters. The federal government can provide grants to local governments and foreign aid to foreign governments to protect against both natural and man-made disasters. All levels of government can provide pre-disaster preparation and post-disaster relief to private citizens. Private citizens can also make their own investments. The tradeoffs between protecting against man-made and natural disasters – specifically between preparedness and relief, efficiency and equity – and between private and public investment, will be discussed. © 2012 ISCRAM.