COLLABORATIVE NOISE MAPPING USING SMARTPHONE

12 citations as recorded by crossref.

1. Spatial modeling of ambient traffic noise using open data S. Popović 10.5937/tehnika2501031P
2. Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment A. Boumchich et al. 10.3390/s22228832
3. General Review of Smartphone Usability in Noise Mapping F. Omar & B. Bostancı 10.34248/bsengineering.1425362
4. Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction S. Bharadwaj et al. 10.3390/asi5030058
5. Acoustic contamination assessment during the transition between the COVID-19 restrictions and reactivation: A exploratory analysis in Guayaquil A. Velastegui-Montoya et al. 10.1016/j.geosus.2023.02.001
6. Reducing Data Requirements for Simple and Effective Noise Mapping: A Case Study of Noise Mapping Using Computational Methods and GIS for the Raebareli City Intersection M. Zafar et al. 10.3390/acoustics5040061
7. Smartphones, a tool for noise monitoring and noise mapping: an overview A. Padilla-Ortiz et al. 10.1007/s13762-022-04240-6
8. Investigating Noise Mapping in Cities to Associate Noise Levels with Sources of Noise Using Crowdsourcing Applications E. Othman et al. 10.3390/urbansci8010013
9. Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review G. Meller et al. 10.1007/s10661-023-11268-9
10. Sequential Clustering Phases for Environmental Noise Level Monitoring on a Mobile Crowd Sourcing/Sensing Platform F. Alhazemi 10.3390/s25051601
11. GIS Based Road Traffic Noise Mapping and Assessment of Health Hazards for a Developing Urban Intersection M. Zafar et al. 10.3390/acoustics5010006
12. Spatial attenuation characteristics and influencing factors of traffic noise in high-density cities: A case study of Tianjin, China Z. Zhou et al. 10.1051/aacus/2025034