StreetSurfaceVis: a dataset of street-level imagery with annotations of road surface type and quality

StreetSurfaceVis StreetSurfaceVis is an image dataset containing 9,122 street-level images from Germany with labels on road surface type and quality. The CSV file streetSurfaceVis_v1_0.csv contains all image metadata and four folders contain the image files. All images are available in four different sizes, based on the image width, in 256px, 1024px, 2048px and the original size.Folders containing the images are named according to the respective image size. Image files are named based on the mapillary_image_id. You can find the corresponding publication here: StreetSurfaceVis: a dataset of crowdsourced street-level imagery with semi-automated annotations of road surface type and quality Image metadata Each CSV record contains information about one street-level image with the following attributes: mapillary_image_id: ID provided by Mapillary (see information below on Mapillary) user_id: Mapillary user ID of contributor user_name: Mapillary user name of contributor captured_at: timestamp, capture time of image longitude, latitude: location the image was taken at train: Suggestion to split train and test data. `True` for train data and `False` for test data. Test data contains data from 5 cities which are excluded in the training data. surface_type: Surface type of the road in the focal area (the center of the lower image half) of the image. Possible values: asphalt, concrete, paving_stones, sett, unpaved surface_quality: Surface quality of the road in the focal area of the image. Possible values: (1) excellent, (2) good, (3) intermediate, (4) bad, (5) very bad (see the attached Labeling Guide document for details) Image source Images are obtained from Mapillary, a crowd-sourcing plattform for street-level imagery. More metadata about each image can be obtained via the Mapillary API . User-generated images are shared by Mapillary under the CC-BY-SA License. For each image, the dataset contains the mapillary_image_id and user_name. You can access user information on the Mapillary website by https://www.mapillary.com/app/user/<USER_NAME> and image information by https://www.mapillary.com/app/?focus=photo&pKey=<MAPILLARY_IMAGE_ID> If you use the provided images, please adhere to the terms of use of Mapillary. Instances per class Total number of images: 9,122 excellent good intermediate bad very bad asphalt 971 1697 821 246 - concrete 314 350 250 58 - paving stones 385 1063 519 70 - sett - 129 694 540 - unpaved - - 326 387 303 For modeling, we recommend using a train-test split where the test data includes geospatially distinct areas, thereby ensuring the model's ability to generalize to unseen regions is tested. We propose five cities varying in population size and from different regions in Germany for testing - images are tagged accordingly. Number of test images (train-test split): 776 Inter-rater-reliablility Three annotators labeled the dataset, such that each image was annotated by one person. Annotators were encouraged to consult each other for a second opinion when uncertain.1,800 images were annotated by all three annotators, resulting in a Krippendorff's alpha of 0.96 for surface type and 0.74 for surface quality. Recommended image preprocessing As the focal road located in the bottom center of the street-level image is labeled, it is recommended to crop images to their lower and middle half prior using for classification tasks. This is an exemplary code for recommended image preprocessing in Python: from PIL import Imageimg = Image.open(image_path)width, height = img.sizeimg_cropped = img.crop((0.25 * width, 0.5 * height, 0.75 * width, height)) License CC-BY-SA Citation If you use this dataset, please cite as: Kapp, A., Hoffmann, E., Weigmann, E. et al. StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality. Sci Data 12, 92 (2025). https://doi.org/10.1038/s41597-024-04295-9 @article{kapp_streetsurfacevis_2025, title = {{StreetSurfaceVis}: a dataset of crowdsourced street-level imagery annotated by road surface type and quality}, volume = {12}, issn = {2052-4463}, url = {https://doi.org/10.1038/s41597-024-04295-9}, doi = {10.1038/s41597-024-04295-9}, pages = {92}, number = {1}, journaltitle = {Scientific Data}, shortjournal = {Scientific Data}, author = {Kapp, Alexandra and Hoffmann, Edith and Weigmann, Esther and Mihaljević, Helena}, date = {2025-01-16},} ----------------------------------------------------------------------------------------------------------------------------------------------------------- This is part of the SurfaceAI project at the University of Applied Sciences, HTW Berlin. - Prof. Dr. Helena Mihajlević- Alexandra Kapp- Edith Hoffmann- Esther Weigmann Contact: [e-mail] https://surfaceai.github.io/surfaceai/ Funding: SurfaceAI is a mFund project funded by the Federal Ministry for Digital and Transportation Germany.# StreetSurfaceVis StreetSurfaceVis 是一个图像数据集，包含来自德国的9122张街景图像，附带路面类型与路面质量的标注信息。数据集附带的`streetSurfaceVis_v1_0.csv`文件存储了所有图像的元数据，另有四个文件夹用于存放图像文件。所有图像均提供四种基于宽度的尺寸：256px、1024px、2048px以及原始尺寸，对应文件夹以各自的图像尺寸命名，图像文件则以`mapillary_image_id`命名。 你可通过以下链接查阅相关研究论文：*《StreetSurfaceVis：带有路面类型与质量半自动化标注的众包街景图像数据集》(StreetSurfaceVis: a dataset of crowdsourced street-level imagery with semi-automated annotations of road surface type and quality)* ## 图像元数据 每条CSV记录对应一张街景图像，包含以下属性： - `mapillary_image_id`：由Mapillary（Mapillary）提供的图像ID（详见下文关于Mapillary的说明） - `user_id`：贡献者的Mapillary用户ID - `user_name`：贡献者的Mapillary用户名 - `captured_at`：时间戳，代表图像的拍摄时间 - `longitude, latitude`：图像拍摄地点的经纬度坐标 - `train`：训练/测试数据划分建议，取值为`True`时代表训练数据，`False`时代表测试数据。测试数据包含来自5个城市的样本，这些城市未出现在训练数据集中。 - `surface_type`：图像下半边中心焦点区域的路面类型，可选值包括：asphalt（沥青路面）、concrete（混凝土路面）、paving_stones（铺路石板）、sett（块石路面）、unpaved（未铺装路面） - `surface_quality`：图像焦点区域的路面质量，可选值包括：(1) 优秀（excellent）、(2) 良好（good）、(3) 中等（intermediate）、(4) 较差（bad）、(5) 极差（very bad，详细说明请参阅附带的标注指南文档） ## 图像来源 本数据集的图像源自Mapillary（Mapillary），一个众包街景图像平台。你可通过Mapillary API获取更多图像元数据。用户上传的图像由Mapillary以CC-BY-SA许可协议共享。 对于每张图像，本数据集均包含`mapillary_image_id`与`user_name`字段。你可通过以下链接访问用户信息：`https://www.mapillary.com/app/user/<USER_NAME>`，以及访问图像信息：`https://www.mapillary.com/app/?focus=photo&pKey=<MAPILLARY_IMAGE_ID>` 若使用本数据集提供的图像，请遵守Mapillary的使用条款。 ## 类别样本分布 数据集总图像数：9122 各路面类型与质量的样本分布如下： | 路面类型 | 优秀 | 良好 | 中等 | 较差 | 极差 | |------------------------|------|------|------|------|------| | 沥青路面（asphalt） | 971 | 1697 | 821 | 246 | - | | 混凝土路面（concrete） | 314 | 350 | 250 | 58 | - | | 铺路石板（paving_stones） | 385 | 1063 | 519 | 70 | - | | 块石路面（sett） | - | 129 | 694 | 540 | - | | 未铺装路面（unpaved） | - | - | 326 | 387 | 303 | ## 建模推荐方案 在建模过程中，我们建议采用地理空间上相互独立的区域划分训练集与测试集，以此验证模型对未知区域的泛化能力。本数据集选取了德国不同地区、人口规模各异的5个城市作为测试集，样本已完成对应划分标注。 训练测试划分后的测试集样本数：776 ## 标注者间信度 本数据集由三名标注者完成标注，每张图像仅由一名标注者独立标注。标注者可在存疑时互相咨询以获取参考意见。其中1800张图像由三名标注者共同标注，最终计算得到克里彭多夫阿尔法系数（Krippendorff's alpha）：路面类型为0.96，路面质量为0.74。 ## 推荐图像预处理流程 由于标注的焦点路面位于街景图像的底部中心区域，因此在将图像用于分类任务前，建议将图像裁剪至下半部分及中部区域。 以下为Python语言实现的推荐图像预处理示例代码： python from PIL import Image img = Image.open(image_path) width, height = img.size img_cropped = img.crop((0.25 * width, 0.5 * height, 0.75 * width, height)) ## 许可协议 CC-BY-SA ## 引用格式 若使用本数据集，请按以下格式引用： > Kapp, A., Hoffmann, E., Weigmann, E. et al. StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality. Sci Data 12, 92 (2025). https://doi.org/10.1038/s41597-024-04295-9 对应的BibTeX引用格式如下： bibtex @article{kapp_streetsurfacevis_2025, title = "{{StreetSurfaceVis}: a dataset of crowdsourced street-level imagery annotated by road surface type and quality}", volume = {12}, issn = {2052-4463}, url = {https://doi.org/10.1038/s41597-024-04295-9}, doi = {10.1038/s41597-024-04295-9}, pages = {92}, number = {1}, journaltitle = {Scientific Data}, shortjournal = {Scientific Data}, author = {Kapp, Alexandra and Hoffmann, Edith and Weigmann, Esther and Mihaljević, Helena}, date = {2025-01-16}, } --- 本数据集属于柏林应用科技大学（HTW Berlin）的SurfaceAI项目。 - Prof. Dr. Helena Mihajlević - Alexandra Kapp - Edith Hoffmann - Esther Weigmann 联系方式：`[e-mail]` 项目官网：`https://surfaceai.github.io/surfaceai/` 资助信息：SurfaceAI是由德国联邦数字与交通部资助的mFund项目。