城市内涝场景下的微地图制作方法

A We-Map Mapping Method for Urban Waterlogging Scenarios

随着我国城市化进程的加快,城市内涝灾害愈加频繁,人民的生命和财产安全受到了严重的威胁。然而,城市内涝灾害在愈加频繁的同时,产生了诸如信息获取不及时、制图时效性低、地图信息冗余等问题,难以为救援提供及时响应。基于此,本文提出了一种面向城市内涝场景下的微地图制作方法。首先,调用微博API获取任意时间段内特定主题下的社交媒体文本数据,对数据进行清洗与预处理;接着,选取BiLSTM-CRF模型提取社交媒体中有关城市内涝的地名地址信息;然后,结合Dijkstra算法为用户推荐避开危险路段的最优路径;最后,结合灾害信息与实际场景,制作城市内涝场景下的微地图。实验结果表明:①该方法能够有效识别城市内涝点,总体准确率达到92.7%;②对比官方发布的积水点,本文提取的积水点与官方积水点大部分重叠,能够有效为传统监测方法提供补充信息;③根据所得内涝点生成道路网级别的城市内涝地图,能够克服在海量地理空间信息中可视化冗余的不足,即有效缓解地理信息冗余的问题。由此可知,实时显示城市内涝信息,能够为用户掌握灾害现场的信息提供便利,调动他们参与制作微地图的积极性。与此同时,结合最优路径推荐,能够提高灾害应急场景下的制图时效。

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 optimization,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 subsequently 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 recognition 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 framework,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 locations,thereby substantially enhancing mapping efficiency.A comparative analysis between map-derived waterlogging 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 algorithm,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 intended applications,encompassing home-bound routes,rescue maps,driving maps,walking maps,storm assistance maps,nearest rescue supplies maps,and more.Each map is endowed with at least one tag,streamlining accurate 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 comprehend 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 waterlogging emergency responses.