Abstract
Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions.
Highlights
Several authors have explored the use of Twitter in the context of environmental hazards prevention and mitigation in the literature
The authors mainly use Twitter to raise an alert over a region, which is coupled to the Global Flood Detection System (GFDS) [5] in order to trigger near real-time mapping, which is in a way that keeps up with GFDS data update frequency
We focus on flooded area estimation, and make use of a new corpus of tweets that were collected during Hurricane Harvey, which has affected the Houston urban region between mid-August and mid-September 2017
Summary
Several authors have explored the use of Twitter in the context of environmental hazards prevention and mitigation in the literature. The TAGGS platform aims at using Twitter for flood impact assessment at a global scale [2]. Jongman et al [4] use Twitter in order to trigger humanitarian actions for early flood response. The authors mainly use Twitter to raise an alert over a region, which is coupled to the Global Flood Detection System (GFDS) [5] in order to trigger near real-time mapping, which is in a way that keeps up with GFDS data update frequency. Opportunities for social media are identified as helping impact assessment and model verification, and for strengthening the acquisition of relevant information. Their literature review concludes that social media content is unlikely to increase spatial resolution of traditional sources, such as remotely
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