The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 613–620, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-613-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 613–620, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-613-2022
 
02 Jun 2022
02 Jun 2022

SMARTPHONE-BASED TRAFFIC NOISE MAPPING SYSTEM

R. Dubey, S. Bharadwaj, V. B. Sharma, A. Bhatt, and S. Biswas R. Dubey et al.
  • Dept. of Computer Science & Engineering, Rajiv Gandhi Institute of Petroleum Technology Jais, Amethi, Uttar Pradesh, India

Keywords: Anechoic chamber, Barrier attenuation, Calibration, GIS, Health hazard, Mapping, Road Noise, Smartphone-based

Abstract. Noise pollution is one of the most serious environmental threats to human health. Noise is becoming more prevalent in urban areas, and it is having a negative impact on human health. The increase in noise is due to the increase in the number of vehicles that creates chaos over the road due to honking. Smart monitoring using is smartphones is required to reduce human dependency and monitor data efficiently to reduce logistical obstacles. A smartphone-based noise monitoring solution can handle the problem of monitoring noise at various traffic crossings in a metropolis. The topographical data, noise data, and noise prediction models are required for forecasting noise levels and showing them as maps. In the Indian city of Lucknow, the entire procedure is being performed by providing a map of 2D and 3D forms. The smartphone-based software tracks noise levels at three road crossings at three different times each day. The collected noise levels were calibrated against a standard noise metre to achieve correct noise levels for these sites. Following that, three noise environment types are chosen and mapped using open-source satellite images and conventional noise models through the web on the GIS platform. The anticipated noise levels on the maps were compared to recorded noise data from identical locations using a conventional noise metre for these three crossings and were found to be within 5.5 dB of accuracy. For 3D mapping, shadow height provides the Z value for point cloud DEM generation for 3D model for noise data of city of Lucknow.