Image dataset: Year-long hourly façade photos of a university building
View abstract on PubMed
Summary
This summary is machine-generated.This study captured one year of building façade data, offering insights into user interaction with manually controlled adaptive systems. The dataset aids in developing energy simulation models and computer vision applications.
Area Of Science
- Building Science
- Sustainable Architecture
- Human-Building Interaction
Background
- Manually controlled adaptive façades are crucial for building energy efficiency and occupant comfort.
- There is a significant lack of observational data on how users interact with these dynamic façade systems.
- Understanding user behavior is key to optimizing building performance and energy simulations.
Purpose Of The Study
- To present a comprehensive dataset of building façade interactions over one year.
- To provide data for developing and validating user behavior models for adaptive façades.
- To facilitate research in computer vision and machine learning for façade element analysis.
Main Methods
- Acquisition of 9596 high-resolution images of building façades over a full year.
- Utilizing two identical GoPro Hero 10 cameras for hourly image capture.
- Systematic documentation of East and West building façades, detailing window configurations and solar protections.
Main Results
- A rich dataset of 9596 façade images, capturing hourly changes.
- Extracted 1.7 million individual window images for detailed analysis.
- Established a foundation for analyzing user-driven façade adjustments and their impact.
Conclusions
- The dataset offers unprecedented insights into real-world user-building interactions with adaptive façades.
- This data is vital for enhancing the accuracy of building energy simulations and predictive models.
- The generated image resources support advancements in machine learning applications for building performance monitoring.

