Indoor–Outdoor Detection Using Low-Cost Air Quality Sensors
Abstract
Distinguishing between indoor and outdoor environments is crucial for various location-based applications, including navigation tools and context-aware services. We present an innovative and highly efficient approach that combines air quality sensors with machine learning techniques to achieve this distinction. Extensive experiments across diverse settings—buildings and vehicles—and data from two cities, Helsinki, Finland, and Milan, Italy, with distinct environmental characteristics, demonstrate significant performance improvements. Specifically, our method achieves over 90% accuracy, a 30% increase compared to approaches relying solely on location information. This work highlights a novel application of air quality data and provides an enhanced methodology for accurately distinguishing indoor and outdoor spaces.
Fields of Study & Subjects
Air Quality Monitoring and Forecasting
Indoor and Outdoor Localization Technologies
Human Mobility and Location-Based Analysis
Publication details
- Journal / source
- IEEE Sensors Journal
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 26
- Issue
- 5
- Pages
- 7692–7705
- Publication date
- 14 January 2026
- Language
- EN