BuiltNet: Graph based Spatio-Temporal Indoor Thermal Variation Detection

Abstract

Monitoring thermal condition with thermal cameras is a potential non-intrusive way to supervise the structural well-being of buildings. Thermal variation can infer various structural damages or construction deficiencies including air leakages through inside and outside surfaces of buildings. Frequent monitoring with thermal images can track the thermal characteristics of different places of built environments which helps to prevent damages beforehand. Previous literature studied thermal conditions in buildings with thermal images are limited to specific regions with constrained environmental settings. In this work, we propose an automated scalable framework BuiltNet for analyzing spatial and temporal temperature variation over various building elements i.e., walls, windows, doors, etc. using longitudinal thermal images. We collected thermal images from a residential apartment home for 10 minutes in consecutive 4-5 hours on different days. The spatial and temporal relations among different spots in a region from sequential thermal images of the corresponding region are represented by graph. We propose an unsupervised deep clustering algorithm based on graph neural network, considering both spatial and temporal features from longitudinal thermal images. Our analysis on the spatial and temporal features of regions in the collected thermal images (from both day and night of different weather conditions) identifies the thermal variation and characterizes the spatiotemporal dynamics over different places in the built environment.