From text to geoinformation – A modular approach for extraction of disaster information from web text data

Abstract. The implementation of effective disaster management measures requires comprehensive information about a given flooding situation. Text data from web news offer potentially large volumes of information for this purpose. However, the extraction and spatiotemporal analysis of flood event-related information is inherently demanding due to the immense volume of unstructured text. Addressing this challenge, we present a modular and scalable method that allows the extraction of disaster-relevant information from a large text corpus. This is accomplished by combining domain specific entity extraction with dictionaries, a machine learning model for toponym identification, and hand-crafted rules for entity linking in a modular workflow. The extracted information is augmented with geolocations in order to support spatial analysis. Using the West Germany flooding event 2021 as a case study, we evaluate the capacity of our approach to extract relevant geospatial information at a variety of spatial granularity levels and in the form of various thematic descriptors. By doing so, we outline the capabilities and limitations of this approach for text extraction and analysis. Furthermore, we demonstrate the potential for systematic utilization of text data for improved situational awareness and for disaster management support.