Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images

Tessio Novack,Alexander Zipf,Heinrich Lorei,Leonard Vorbeck

doi:10.3390/ijgi9020098

Tessio Novack, Alexander Zipf + Show 2 more

Open Access

https://doi.org/10.3390/ijgi9020098

Copy DOI

Abstract

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.

Highlights

As a category of street art, graffiti makes use of the city’s walls, roofs and pavements as its canvas
In the introduction part of this paper, we argued that, according to how the OSM-Wiki page advises contributors to tag graffiti artworks, to a query performed in TagInfo, and to our empirical knowledge of London, OSM is still incomplete regarding this type of information in this city
It can be seen that the number of potential graffiti artworks and their hot spots is considerably higher in Figure 6b than in Figure 6a, making the point that, as a dataset, Flickr is complementary to OSM and can indicate the areas where a more focused search of graffiti artwork should be undertaken with the aim of detecting the specific facades in which they are painted

Summary

Introduction

As a category of street art, graffiti makes use of the city’s walls, roofs and pavements as its canvas. Through a careful manual inspection, we found out that OSM contributors frequently tag street art and graffiti features in London as ’name=Street Art’, but a query with these tags only returned 74 features. Another issue of graffiti artwork data in OSM is that it is usually represented as nodes, i.e., as points in space, only indicating the approximate location of the artwork. This is not a problem for most map use purposes, applications like generating street art walking routes and the analysis of correlations between graffiti paintings and building characteristics (e.g., use, geometry, visibility) require knowing on which specific facades graffiti artworks are painted

Objectives

Methods

Results

Conclusion