RESUMEN
This paper describes three datasets which include 443 folders and approximately 4430 images. The images were obtained from the interior of a 1:50 scale model using a fisheye camera connected to a Raspberry Pi microcomputer. This dataset aims to analyze the photobiological effects (visual and non-visual) of the interplay between coloured surfaces and different types of lighting strategies. The experiments were conducted under three types of light sources: simulated daylight through a mirror-box artificial sky simulator, direct daylight, and an electric lighting system that allows for colour temperature modification. This dataset includes low dynamic range images to generate high dynamic range images, which in turn can be used to plot false colour maps concerning photopic luminance, melanopic luminance, CCT of an image, M/P ratio, and brightness distribution maps. This dataset can be useful for architects, interior designers, and building engineers to integrate lighting and colour strategies according to the visual and non-visual needs of the users. This research was partially used in the research of Espinoza-Sanhueza et al. [1,2]. The datasets are published and shared through a Mendeley repository [3].
RESUMEN
This paper details an imagery dataset of interior and exterior ambiances to assess and represent photobiological outcomes of the built environment in northern territories. The images were obtained using a Raspberry Pi Camera Module (RPiCM) mounted in a holder that fixes the camera in place. This holder allows to rotate the camera by 30° and take 12 high dynamic range (HDR) images which are then combined to create a panoramic image. The HDR images enable the calculation of photobiological effects concerning photopic light intensity for vision, and the spectral dominance regarding vision and circadian stimulation. This dataset includes 13 captures in 7 interior and 6 exterior settings, each divided into 4 subfolders containing the photographic data: the sequence of low-dynamic range images (LDR), the tone-mapped images obtained from the HDR calculation, the analysis of photopic luminance and false color, and 360° panoramic images (tone-mapped HDR, false color luminance, and spectral dominance). Each space is also supplemented with photometric data presented as a .csv file containing lux and EML units obtained via a radiometer. This dataset is valuable for architects, designers, and neuroscientists to identify opportunities for enhancing human-centric lighting in existing architecture and landscape, as well as to propose solutions that promote vision and circadian stimulation in northern territories. This research was partially used in previous studies from [10]. The dataset is published and shared through a Mendeley repository [9].
RESUMEN
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].