Inferring Landmarks for Pedestrian Navigation from Mobile Eye-Tracking Data and Google Street View

Automatic Recognition of Playful Physical Activity Opportunities of the Urban Environment

“…There exists previous work that has used street level photographs as an information source for geographical information analysis, with either automated or crowdsourced data, to identify accessibility problems [13], landmarks for pedestrian navigation [20], curb ramps [14], and scenic driving routes [39], but we know of no previous work on recognizing parkourability or other physical activity opportunities.…”

It’s also about timing! When do pedestrians want to receive navigation instructions

“…Empirical evidence has been provided that the use of landmarks has a positive impact on wayfinding performance (see, e.g., Ross, May & Thompson, 2004;Tom & Denis, 2004) and that the absence of landmarks in an environment is compensated by an increased granularity in verbal humanto-human route instructions (see Hirtle, Richter, Srinivas & Firth, 2010). Research on incorporating landmarks (see Richter & Winter, 2014, for a thorough overview of the concept) in route instructions for wayfinding assistance systems has, consequently, become a predominant research topic, including modeling (see, e.g., Caduff & Timpf, 2008;Nothegger, Winter & Raubal, 2004;Nuhn & Timpf, 2017;Raubal & Winter, 2002;Winter, 2003), empirical assessment (see, e.g., Götze & Boye, 2016;Kattenbeck, 2017;Kattenbeck, Nuhn & Timpf, 2018;Quesnot & Roche, 2015) of salience and the automatic selection of landmarks (see, e.g., Duckham, Winter & Robinson, 2010;Lander, Herbig, Löchtefeld, Wiehr & Krüger, 2017;Lazem & Sheta, 2005;Rousell & Zipf, 2017;Wang & Ishikawa, 2018).…”

Urban Mosaic

“…Urban Pulse [58] uses computational topology techniques and data from Twitter and Flickr to visualize spatio-temporal activity across various resolutions, and Urban Space Explorer [42] proposes an approach to explore public-space-related activity. Another is using computer vision algorithms to assess the built environment through street-view images, for example to: assess and map greenery and openness in urban settings [51,49], quantify the daily exposure of urban residents to eye-level street greenery [90], extract land use information [50], measure visual quality [82], visual enclosure [91], urban form [80] and sky exposure [19] of street spaces, and assess traffic signs [11], curb ramps [37] and urban landmarks [46]. Street-level images have also been used to predict relationships between a city's built environment and socioeconomic conditions [5], and as the basis for automatically extracting a city's most distinctive visual elements [25].…”