[Objectives]Currently, systematic research in content retrieval for We-maps is lacking. To address this gap, this paper proposes an approach for geographic feature extraction and retrieval in hand-drawn map scenes using the YOLOv8l-FMSC-Spatial model (You Only Look Once v8l - Fewer Multi-Scale Convolution-Spatial). [Methods]First, different YOLO models were compared to select the optimal YOLOv8l model. The C2f-FMSC module was introduced to improve this model, resulting in the YOLOv8l-FMSC training model specifically designed for We-maps. This model was applied to extract geographic features from raster maps. Next, to meet the retrieval needs of geographic features, a spatial relationship database for these features was established. A spatial computation and retrieval module, Spatial, was designed to process geographic feature information by transmitting and filtering it. The module further calculates spatial correlations between user queries and the geographic feature information in the database. Based on the degree of spatial relationship association, the model indexes maps containing relevant geographic feature information from the We-maps database, enabling the construction of a spatial relationship-based geographic feature retrieval model. The method was validated using hand-drawn campus map retrieval scenarios. The experimental dataset comprised publicly available maps from schools and maps freely created by students, totaling 493 hand-drawn campus maps. These maps were used to study the retrieval of representative geographical elements such as water bodies, sports fields, and unique architectural structures associated with schools nationwide. The focus was on accurately identifying and retrieving these characteristic elements to ensure the model’s practical applicability. [Results] The experimental results indicate: (1) The trained YOLOv8l model effectively identifies geographical elements in self-made maps, with its effectiveness and robustness verified on the proposed dataset; (2)The YOLOv8l model, enhanced with the FMSC module, achieved a precision of 0.8 and a recall of 0.764, making it the optimal choice for practical comparisons; (3)The Spatial calculation model effectively captures the spatial information of relevant geographical elements, narrowing the gap with orthographic map retrieval. By applying this method, the retrieval of geographical elements from hand-drawn campus maps, while considering spatial relationships, becomes achievable. [Conclusions] The proposed model can quickly and accurately retrieve content-relevant hand-drawn maps based on geographic feature conditions, effectively filling the research gap in content retrieval for We-maps.

The We-Map, a novel cartographic phenomenon emerging in the era of social media, is distinctively characterized by mass participation, personalization, and swift dissemination. However, existing research on We-Map falls short in addressing the intricate challenges posed by point symbol design, thereby hampering the fulfillment of the public's desire for personalized cartographic representations. To bridge this gap, this paper starts from the perspective of We-Map mapping production, taking common map symbols in hand-drawn maps as the research object, and constructs an open hand-drawn map dataset. To this end, we have constructed a comprehensive dataset encompassing a diverse array of hand-drawn map symbols, encompassing various types and styles. This dataset serves as a valuable resource for exploring and enhancing the automated extraction of common map symbols. Drawing inspiration from existing research, we have embarked on a journey to identify and evaluate the most suitable model for our task. Among the numerous models for object detection, the performance of the YOLOv5 series models is well-known, and therefore this article will not delve into it excessively. Specifically, through comparison, we ultimately chose the YOLOv5-X model, which boasts advanced capabilities in object detection and classification. By leveraging the YOLOv5-X model, we have achieved remarkable results in the automatic extraction of common map symbols from hand-drawn maps. Our experiments reveal that the model achieves high levels of accuracy, recall, and F1 score in identifying and extracting point categories from the hand-drawn map dataset. These scores stand testament to the model's effectiveness in capturing the intricate details and unique characteristics of hand-drawn map symbols. Moreover, to further validate the generalizability of our model, we have conducted additional experiments on the Quick Draw doodle dataset. The results obtained from these experiments confirm that our model performs equally well in extracting common map symbols from diverse and varying datasets. The significance of this study lies not only in enhancing the dataset available for personalized point symbol research in We-Map but also in advancing the techniques for extracting common map symbols. By introducing more diversified elements into We-Map cartography, we have opened up new avenues for more flexible and personalized mapmaking in the age of self-media. This study represents a significant step forward in the evolution of cartography in the rea of self-media, catering to the evolving needs and preferences of the modern audience. The finally extracted point symbols can provide a data foundation for downstream tasks related to We-Map.

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.

The existing WeMaps recommendation algorithm cannot fully exploit the features of users and maps, resulting in a low recommendation accuracy. In this study, a WeMaps recommendation algorithm is proposed which combines feature interaction and user history behavior sequence. To begin with, a multi-head attention mechanism and skip connections are introduced in the process of feature interaction between the user and the map. The multi-head attention mechanism allows for interactions between different features in multiple subspaces, resulting in richer feature combinations. The skip connections combine low-level and high-level feature interactions, ensuring model effectiveness and avoiding the occurrence of model degradation. Additionally, a cross-attention mechanism is incorporated into the user's historical behavior sequence to identify the points of interest related to the candidate map. This mechanism effectively captures points of interest within the user's historical behavior sequence that align with the recommended map. It assigns greater attention to the recommended map that corresponds to the user's short-term dynamic interests. Finally, by utilizing a deep neural network, the output results from the feature interaction module and the user's historical behavior sequence module are fused together, resulting in a more accurate recommendation that takes multiple dimensions into consideration. The values of log loss on the public datasets Criteo and WeMaps datasets are 0.446 1, and 0.379 7, respectively, and the values of area under the receiver operating characteristic curve are 0.805 2, and 0.788 3, respectively. For convenience of expression, the area under the receiver operating characteristic curve will be referred to as AUC in the following text. Compared to the second-order feature interaction model in the experimental study of this paper, the loss values decreases by 1.7% and 14.2%, respectively, while the AUC values increases by 0.8% and 0.4%, respectively. Compared to the high-order feature interaction model in the experimental study of this paper, the average decrease in loss values is 1.3% and 2.6%, respectively, while the average increase in AUC values is 0.6% and 0.2%, respectively. When the model has a low loss value and a high AUC value, it indicates that the model has a small discrepancy between its predicted results on the training data and the actual labels, and the model is capable of effectively distinguishing between positive and negative examples at different thresholds. This is generally considered as a good indicator of model performance, indicating that the model has higher accuracy and better classification ability. Experimental results show that the model proposed in this paper can not only provide users with more accurate recommendation results but also make the recommendation results have good explainability.

We-Map is a new type of map for the general public, which represents the development and supplement of traditional maps in the era of We media. To address the problems of We-Map data such as widespread data sources and varying data quality, this paper constructs a spatial similarity metric that takes into account the contextual information based on the concept of shape context, and proposes a We-Map road network quality evaluation method. Firstly, the spatial similarity between We-Map data and the reference data is calculated by combining the contextual information including shape, direction, distance, topological relations, and one-dimensional line features. Then, the entropy method is used to calculate the objective weights of different combinations of sample data, and the average value is taken as the weight coefficient of each evaluation indicator. Finally, considering the fuzziness, randomness, and incompatibility of the evaluation process, the extension cloud evaluation method is used to complete a comprehensive evaluation for We-Map data through quantitative calculation and qualitative analysis. In this paper, a subset of the north campus of Lanzhou Jiaotong University is selected as the experimental area, and the reference data of road network is obtained from Baidu map. The feasibility of the proposed method is verified through the experimental analysis of nine groups of We-Map data with different quality. The experimental results show that the proposed spatial similarity metric aligns well with the characteristics of We-Map such as low accuracy and limited content, which generates relatively accurate evaluation results. In comparison with the fuzzy comprehensive evaluation method, the evaluation results of the extension cloud evaluation method are more satisfactory. Also, the extension cloud evaluation method can not only reveals the quality of We-Map data at the same evaluation level, but also reflects the reliability of evaluation results. Compared with the existing spatial similarity evaluation method of line features, it has a low evaluation level for We-Map data division. However, the proposed method in this paper can better divide the We-Map data into appropriate levels, which is in line with the characteristics of the We-Map road network data and demonstrates its suitability for evaluating We-Map road network data. The proposed method in this paper fills the gap in We-Map quality evaluation, providing a feasible method for We-Map road network quality evaluation, which is beneficial for the wide utilization of We-Map.

Landmarks play an important role in spatial information transmission, especially for wayfinding navigation. Numerous studies have shown that the inclusion of landmarks in route tasks can effectively reduce steering errors. How to incorporate landmarks into navigation systems and break the barrier of using distance information as the indicator to guide users in wayfinding is currently a difficult problem to solve. And we-map provides a platform for users to produce and disseminate map content. Because we-map does not distinguish between mapmakers and map users, it lowers the threshold for mapping and enables users to have self-make maps. In the process of route mapping, we-map platform can provide a collection of landmarks for users to choose from and use them to complete their wayfinding, enabling the solution to the challenge of incorporating landmarks into navigation systems. In order to extract we-map landmarks accorded with people's spatial cognition, the method of extracting landmarks by user-generated content is proposed. First, there are three indicators (public awareness, city centrality, and individual characteristic value) that are calculated separately, and each of them is obtained by the entropy value method. Then, landmarks are extracted in a hierarchical manner in term of the difference in the significance of landmarks to establish a set of landmarks for serving the users of the we-map. Last, the user-generated landmarks are collected during the process of publishing, sharing, and disseminating the we-map to enrich the landmark library, aiming at realizing secondary dissemination about the extraction of landmarks from user-generated contents. The experiment selects POI data of An Ning District in Lanzhou City to calculate landmark salience, selects landmarks at different levels according to different scales, designs tasks for participants to describe routes and complete connections between landmarks, collects usage landmarks, forms user-generated content to disseminate landmark data, and draws personalized routes that meet different user needs. This study simulates the process of route-finding cartography using landmarks by we-map users to pave the ground for personalized navigation on the we-map platform. The experimental results show that the content generated by using users' shared service data effectively solves the problem of acquiring and timely updating landmark candidate sets, expresses the user's cognitive expressiveness to the greatest extent, and reduces the burden of wayfinding for pedestrians walking out. This study is applied to daily wayfinding, where we-map users participate in constructing and sharing service data, forming spontaneous dissemination of user-generated content, timely update, and dissemination of landmark data, providing reference for rapid we-map drawing, and improving wayfinding efficiency.