Thermal anomaly and rooftop unit (RTU) detection in buildings through machine learning

Samuel Fernandes; Rohullah Najibi; Anand Prakash; Reshma Singh; Marina Zafiris; Jessica Granderson

doi:10.1117/12.2644396

26 October 2022 Thermal anomaly and rooftop unit (RTU) detection in buildings through machine learning

Samuel Fernandes, Rohullah Najibi, Anand Prakash, Reshma Singh, Marina Zafiris, Jessica Granderson

Proceedings Volume 12269, Remote Sensing Technologies and Applications in Urban Environments VII; 1226909 (2022) https://doi.org/10.1117/12.2644396
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. In this work, we present a machine learning approach for asset extraction of a building’s roof top unit (RTU) using a state-of-the-art object detection algorithm. We also present an approach to identify potential thermal anomalies on the building envelope. Our object detection algorithm achieves 89% accuracy on the test dataset, while our thermal anomaly algorithms are able to identify potential anomalies, but require further testing for accuracy. The asset information and anomalies are relevant to a variety of urban and energy applications.

Citation Download Citation

Samuel Fernandes, Rohullah Najibi, Anand Prakash, Reshma Singh, Marina Zafiris, and Jessica Granderson "Thermal anomaly and rooftop unit (RTU) detection in buildings through machine learning", Proc. SPIE 12269, Remote Sensing Technologies and Applications in Urban Environments VII, 1226909 (26 October 2022); https://doi.org/10.1117/12.2644396

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