Abstract
In the race towards carbon neutrality, the building sector has fallen behind and bears the potential to endanger the progress made across other industries. This is because buildings exhibit a life span of several decades which creates substantial inertia in the face of climate change. This inertia is further exacerbated by the scale of the existing building stock. With several billion operational buildings around the globe, working towards a carbon-neutral building sector requires solutions which enable stakeholders to accurately identify and retrofit subpar buildings at scale. However, improving the energy efficiency of the existing building stock through retrofits in a targeted and efficient way remains challenging. This is because, as of today, the energy efficiency of buildings is generally determined by on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete. In order to accelerate the identification of promising retrofit targets, this work proposes a new method which can estimate a building's energy efficiency using purely remotely sensed data such as street view and aerial imagery, OSM-derived footprint areas, and satellite-borne land surface temperature (LST) measurements. We find that in the binary setting of distinguishing efficient from inefficient buildings, our end-to-end deep learning model achieves a macro-averaged F1-score of 62.06\%. As such, this work shows the potential and complementary nature of remotely sensed data in predicting building attributes such as energy efficiency and opens up new opportunities for future work to integrate additional data sources.
Abstract (translated)
URL
https://arxiv.org/abs/2206.02270
