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

Abstract

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.

Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data

“…Compared with the research of Middleton, S.E. et al [18], Yin, Jie, et al [19], and Avvenuti, Marco, et al [21], our research slightly differs in the starting point of the study. First, although the techniques presented in this article may not be the state-of-the-art, the results show the great capability and potential of applying simple geographical information that is present in social media.…”

“…Second, combined with other disaster-related data, such as geographical information and remote sensing images, social media data can enhance and improve useful information extracted for emergency response [14][15][16][17]. Last, but important, smart mobile devices allow users to report information about disasters (e.g., locations with specific names), which is likely to be useful for estimating disaster-related information, including the affected area, damaged infrastructure, affected people, and evacuation zones [18][19][20][21].…”

Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria

“…Compared to situational awareness research, very limited focus has been given to learn the actionability of social media content during disasters. A recent study by Avvenuti et al (2018) investigates this crucial aspect and presents a system called CrisMap, which 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 geo-parse tweets. The system then visualises the extracted information in customisable web-based dashboards and maps.…”

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

“…We compare our proposed classifier based on random forest learning with the work presented in [58]. For our classifier, we use a 3-day observation window.…”

“…In [58], the extraction of localities embedded in social network messages contain the following metrics: lexical, morpho-syntactic, and lexical expansion, which are associated with three classes-damage, non-damage, and non-relevant. For the events that occurred during the earthquake of 20 May 2012 in Northern Italy, that work trains messages using a Support Vector Machine (SVM) for word embeddings (EMB) Table 4 tabulates the performance metrics of the class damage from [58] and the entity clases LOC, O, ORG, PER of this work (Note that the entity class LOC is the most comparable metric with the class damage from [58] ). Figures 6a-c present the hotspots obtained from the KDE of spatial features collected over a span of three days as described in 5.4.…”