Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images
Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images
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DOI：
10.3390/ijgi9020098
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
T. Novack;Leonard Vorbeck;Heinrich Lorei;A. Zipf
中科院分区：
文献类型：
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作者：
T. Novack;Leonard Vorbeck;Heinrich Lorei;A. Zipf
作为一种公认的艺术类型，涂鸦是一种文化资产，也是城市美学的重要方面。因此，涂鸦与社会和商业活力有关，并以吸引游客而闻名。然而，位置的不确定性和不完整性是当前开放的地理数据集包含涂鸦数据的问题。在本文中，我们提出了一种基于Google街景（GSV）图像的自动解释来检测带有涂鸦艺术品的建筑立面的方法。它首先识别照片共享媒体Flickr上发布的涂鸦艺术品的地理标记照片。然后从这些照片的周围提取GSV图像，并由定制的，即，转移学习卷积神经网络被分类为包含涂鸦艺术品的GSV图像的罗盘航向及其获取的可能位置被考虑用于根据其包含GSV图像中可观察到的艺术品的潜力对建筑物立面进行评分。超过36，000个GSV图像和5000个建筑物立面在OpenStreetMap中进行了处理和评估。精确率和召回率计算不同的门面分数阈值。假阳性错误主要是由建筑物立面上的广告和涂鸦以及包含涂鸦艺术品并阻碍立面的可移动物体引起的。然而，考虑更高的分数作为检测包含涂鸦的立面的阈值导致完美的准确率。我们的方法可以应用于识别以前未映射的涂鸦艺术品，并协助地图贡献者感兴趣的主题。此外，对涂鸦艺术品和社会经济因素之间的空间相关性感兴趣的研究人员可以从我们的开放获取代码和结果中获益。
As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In this paper, we present an approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV). It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e., transfer learned, convolutional neural network. The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5000 facades from buildings represented in OpenStreetMap were processed and evaluated. Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate. Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results.