A We-Map Mapping Method for Urban Waterlogging Scenarios
The escalating urbanization in China has exacerbated waterlogging disasters,posing substantial threats to both human lives and property.In response to the challenges of inadequate mapping and redundant map data in urban waterlogging contexts,this study introduces a comprehensive four-stage methodology for We-Map cartography.This cartography encompasses data acquisition,extraction of waterlogging points,route opti-mization,and scene application.The initial step involves the retrieval of social media text data through queries to the Weibo Application Programming Interface(API)within a defined timeframe.The retrieved data are subse-quently subjected to thorough cleaning and preprocessing procedures.Following this,the BiLSTM-CRF model is harnessed to discern urban waterlogging locations from the social media content,thereby enhancing recogni-tion accuracy through contextual insights.Then,users are provided with optimal route for bypassing perilous road segments,achieved via the shortest path algorithm.Leveraging the online map as the foundational frame-work,the We-Map is generated within the urban waterlogging setting by overlaying multiple layers.Notably,the proposed method attains an impressive overall accuracy rate of 92.7％in pinpointing urban waterlogging loca-tions,thereby substantially enhancing mapping efficiency.A comparative analysis between map-derived water-logging points and official records reveals a substantial overlap,thus offering valuable supplemental information to conventional monitoring techniques.Furthermore,a road network-level map of urban waterlogging points is also generated to avoid redundancies in vast geospatial information.The identified flood-prone road sections can serve as a reference,while real-time display of urban waterlogging points,coupled with the shortest path algo-rithm,aids in recommending optimal routes.By leveraging the inherent attributes of"we-content"within the We-Map,this method expedites rapid mapping and fulfills the exigencies of swift mapping during emergencies.To cater to diverse user needs,urban flooding scenarios map are categorized with different tags aligned with their in-tended applications,encompassing home-bound routes,rescue maps,driving maps,walking maps,storm assis-tance maps,nearest rescue supplies maps,and more.Each map is endowed with at least one tag,streamlining ac-curate searches and usage by other users,and concurrently providing a reference for urban rescue operations.The proposed method ensures the coherence of map content and user requisites,facilitating efficient map sharing among users.The real-time dissemination of urban waterlogging information empowers users to swiftly compre-hend disaster scenes,engendering their active involvement in We-Map production,and combining optimal path recommendation to augment cartographic responsiveness in emergency disaster scenarios.This approach bears substantial practical significance and promising application potential,constituting a robust for urban waterlog-ging emergency responses.
urban waterloggingWe-mapssocial medianatural language processDijkstra algorithmBiLSTM-CRFoptimal route
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