Imagery datasets for photobiological lighting analysis of architectural models with shading panels

Mojtaba Parsaee; Claude Mh Demers; Marc Hébert; Jean-François Lalonde

doi:10.1016/j.dib.2022.108278

Imagery datasets for photobiological lighting analysis of architectural models with shading panels

Data Brief. 2022 May 15:42:108278. doi: 10.1016/j.dib.2022.108278. eCollection 2022 Jun.

Authors

Mojtaba Parsaee¹, Claude Mh Demers¹, Marc Hébert², Jean-François Lalonde³

Affiliations

¹ GRAP (Groupe de Recherche en Ambiances Physiques), School of Architecture, Laval University, Quebec, Canada.
² CERVO Brain Research Centre, Faculty of Medicine, Laval University, Quebec, Canada.
³ Computer Vision and Systems Lab, Department of Electrical and Computer Engineering, Laval University, Quebec, Canada.

Abstract

This paper describes eight imagery datasets including around 12000 images grouped in 1220 sets. The images were captured inside an architectural model aimed at exploring the impact of shading panels on photobiological lighting parameters. The architectural model represents a generic space at 1:10 scale with a single side fully glazing façade used to install shading panels. The datasets present interior lighting conditions under different shading configurations in terms of surface colors and glossiness, horizontal and vertical orientations and upwards, downwards, and left/right inclinations of panels, V-shape opening, low to high densities, and top and bottom positions at the window. The experiments of shading panel configurations were conducted under four to six different exterior overcast daylighting conditions simulated with very cool to very warm color temperatures and high to low intensities inside an artificial sky chamber. The datasets include bracketed low dynamic range (LDR) images which enable generating high dynamic range (HDR) images for photobiological lighting evaluations. Images were captured from the side and back viewpoints inside the model by using Raspberry Pi camera modules mounted with fisheye lenses. The datasets are reusable and useful for architects, lighting designers, and building engineers to study the impact of architectural variables and shading panels on photobiological lighting conditions in space. The datasets will also be interesting for computer vision specialists to run machine learning techniques and train artificial intelligence for architectural applications. The datasets are partially used in Parsaee, et al. [1]. The datasets are compiled as part of a doctoral dissertation in architecture at Laval University authored by Mojtaba Parsaee [2]. The datasets are shared through two Mendeley data repositories [3,4].

Keywords: Adaptive façade; Color; Computer vision; Daylight; Healthy building; High and low dynamic range image; Interior design; Surface.