ubiMap-l: A Benchmark for Crowdsourced Thematic Map Layout Retrieval and Embedding-based Analysis

The ubiMap dataset is comprised of 3,530 map images collected from the Bing image search service (1,730 maps) and Geo-Journal (1,800 maps). Each image has been manually labeled with 22 types of map elements, including their boundary shapes and category properties, resulting in an average of 5.92 elements per map. ubiMap-l is built uopon ubiMap by removing maps that contained only one element, which results a total of 3,515 maps for map layout retrieval test. We first opensourced 703 maps in ubiMap-l that we used for testing our map layout representation learning framework, MapLayNet. Besides 703 map images and their layout label data, embedding of MapLayNet and its baseline model is provided along with the python codes for embedding visualizaiton. Please cite the paper if you use the dataset. Yang, J., Chen, C., Jia, F., Xie, X., Fang, L., Wang, G., & Meng, L. (2025). MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks. Cartography and Geographic Information Science, 1–22. https://doi.org/10.1080/15230406.2025.2533316

ubiMap数据集（ubiMap Dataset）共包含3530幅地图图像，其中1730幅采集自必应图片搜索服务（Bing Image Search Service），其余1800幅源自地理期刊（Geo-Journal）。每幅图像均经人工标注22类地图要素（map elements），涵盖其边界形状与类别属性，平均每幅地图含5.92个标注要素。ubiMap-l数据集（ubiMap-l）基于ubiMap数据集构建，通过移除仅含单个要素的地图，最终得到3515幅用于地图布局检索测试的样本。我们首先开源了ubiMap-l中用于测试其地图布局表征学习框架MapLayNet（MapLayNet）的703幅地图。除703幅地图图像及其布局标注数据外，本数据集还附带MapLayNet及其基线模型的嵌入结果，以及用于嵌入可视化的Python代码。若使用本数据集，请引用以下论文：Yang, J., Chen, C., Jia, F., Xie, X., Fang, L., Wang, G., & Meng, L. (2025). MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks. 《制图学与地理信息科学》（Cartography and Geographic Information Science）, 1–22. https://doi.org/10.1080/15230406.2025.2533316