Appraising daylight changes in window views: systematic procedures for classifying and capturing dynamic outdoor scenes.

Exposure to environmental changes in windows are essential to sustain healthy conditions indoors; however, there are no procedures for capturing these variations dynamically. In this study, we provide the foundations for building a framework aimed at understanding the effects of luminous variability in window views. First, we labelled a sample of views (n = 40) using a surface layout approach, resulting in four view type categories. Next, we captured time-lapse imagery from selected views (n = 8) to produce panorama sequences. We calculated the absolute lightness variation in Matlab, resulting in three luminous change categories (i.e. Global, Local, and Minimal variation). Concurrently, we collected environmental measures to define variability thresholds. The integrated analysis showed a higher occurrence of luminous changes in two categories in controlled conditions, suggesting that viewing configuration might prevail over window orientation for conveying light changes over time. [ABSTRACT FROM AUTHOR]