Street View Imagery is used extensively to predict and understand the perception of the urban space by pedestrians (e.g. feeling of safety). While traditional approaches use perspective images (implying a limited field of view) to collect the opinion of participants, this paper evaluates the use of 360° imagery in crowdsourcing experiments. 360° imagery has the benefit of adding visual context compared to perspective images (‘out-of-frame’ information) and provides a better immersion for the participant, while being accessible on regular computer monitors. We developed a website to collect the opinion of 364 participants on both types of images. Based on 48 comparisons, our experiment shows that in 19% of the cases, the evaluation of the safety of a place differs if the context is shown to the participant beforehand. Additionally, we demonstrate that the out-of-frame information plays a role in how a participant rates an image. Finally, we provide comparisons with a Machine Learning model trained on the popular Place Pulse 2.0 dataset and show that using 360° imagery has a smoothing effect over the ratings of a place, compared to perspective images. These results have strong implications for the creation of urban perception datasets via crowdsourcing techniques.

Conducting a detailed assessment of solar irradiation at the pedestrian scale on all ground surfaces of a city can assist in identifying cooler routes for pedestrian navigation or preparing the city for potential overheating issues by pinpointing overexposed areas. This article proposes an effective method for conducting such an assessment within a GIS with metric resolution across territories exceeding 100 km². It is based on standard datasets and implements an efficient strategy that relies on separation of variables, domain decomposition, and dimensionality reduction. This strategy involves creating a synthetic representation of the facades of the surrounding buildings (spatial dimension) which accelerates the calculation of shadows based on the sun’s position (temporal dimension). To demonstrate the effectiveness of this method, we applied it to a French city, generating fourteen maps illustrating the solar irradiation of the area for different months of the year or for two given dates with specific weather conditions. The proposed strategy, along with the synthetic representation of building facades, opens up a wide range of possibilities. In addition to synthesizing machine learning labeled datasets, we can also consider calculating solar irradiation with time steps of a few minutes to update weather conditions throughout a journey.

Abstract Understanding visual complexity of urban environments may improve urban design strategies and limit visual pollution due to advertising, road signage, telecommunication systems and machinery. This paper aims at quantifying visual complexity specifically in urban streetscapes, by submitting a collection of geo-referenced photographs to a group of more than 450 internet users. The average complexity ranking issued from this survey was compared with a set of computer vision predictions, attempting to find the optimal match. Overall, a computer vision indicator matching comprehensively the survey outcome did not clearly emerge from the analysis, but a set of perceptual hypotheses demonstrated that some categories of stimuli are more relevant. The results show how images with contrasting colour regions and sharp edges are more prone to drive the feeling of high complexity.

Thomas Leduca* & Michel Leducba Ecole Nationale Supérieure d’Architecture de Nantes, Nantes, Franceb Department of Mathematics, University of Le Havre, Le Havre, FranceDr. Thomas Leduc has a master degree in mathematical engineering. He holds a doctorate in computer science from the University of Paris VI, now renamed Sorbonne University. He was Deputy Director of the AAU laboratory (CNRS) at the School of Architecture in Nantes from 2014 to 2018. His research activities focus on the urban morphology characterization using geographic information science and technology. He is involved in several projects that aim to describe the morphology of open spaces using visibility-based methods.Honorary Prof. Michel Leduc defended a state doctorate in mathematics in 1971. Appointed lecturer in 1964 and assistant professor in 1966 at the Faculty of Sciences of Orsay, he was successively appointed professor at the University of Rouen in 1971 and of the University of Le Havre in 1984. His research has focused on mathematical analysis, geometry, and optimization. He supervised five theses of modelling in the Laboratory of Ultrasonic Acoustics and Electronics at the University of Le Havre. He was President of the University of Le Havre from 1991 to 1994.CONTACT Thomas Leduc [email protected] geographic information science and technology, various methods and studies exist to characterize the linearity, rectangularity, convexity, circularity or compactness, sinuosity or tortuosity of a given spatial shape. Although there is much work on ellipticity in image processing, we do not address, in geomatics, the issue of matching to a reference elliptical shape. Regarding this issue, this article is a contribution to the qualification of urban open spaces. It provides an operating algorithm for determining a minimum-area bounding ellipse for any given polygonal shape. It also proposes an implementation of this algorithm in the context of a Geographic Information System and a Jupyter Notebook. As an application, it focuses on two real urban configurations on fields of about 300 isovists. The results from the application of this approach in two urban areas in France show that the ellipse is a better minimum bounding geometry than are circle or rectangles, at least for the half-dozen descriptors studied. The improvement relatively to the minimum bounding rectangle is particularly significant in terms of correlation concerning the orientation (+20%) and drift (+10%).

To develop and manage urban territories and to analyze urbanisation impact on environment, an accurate knowledge of the city and its urban fabric is necessary. A physical description like building morphology or land cover/use allows some characterization of the urban terrain. A specific package for a GIS software is developed for urban analysis: UrbSAT (Urban Spatial Analysis Tool). It allows extracting knowledge from various data sources and gives some indicators to real applications (sustainable development, management policy, air quality improvement, etc). It raises issues such as database accuracy and quality, cell shape, size and orientation for the construction of spatial indicators. We present some tests to address those issues.