GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Noise is a universal problem that is particularly prominent in developing nations like India. Short-term noise-sensitive events like New Year’s Eve, derby matches, DJ night, Diwali night (celebration with firecracker) in India, etc. create lots of noise in a short period. There is a need to come up with a system that can predict the noise level for an area for a short period indicating its detailed variations. GIS (Geographic Information System)-based google maps for terrain data and crowd-sourced or indirect collection of noise data can overcome this challenge to a great extent. Authors have tried to map the highly noisy Diwali night for Lucknow, a northern city of India. The mapping was done by collecting the data from 100 points using the noise capture app (30% were close to the source and 70% were away from the source (receiver). Noise data were predicted for 750 data points using the modeling interpolation technique. A noise map is generated for this Diwali night using the crowd-sourcing technique for Diwali night. The results were also varied with 50 test points and are found to be within ±4.4 dB. Further, a noise map is also developed for the same site using indirect data of noise produced from the air pollution open-sourced data. The produced noise map is also verified with 50 test points and found to be ±6.2 dB. The results are also corroborated with the health assessment survey report of the residents of nearby areas.

Acoustic balance between highway and metro: a case study of Dos Hermanas

With the Directive 2002/49 of the European Parliament, commitments are established for the control of noise pollution for member countries. Based on such determination, an important tool for the noise pollution control is the noise map, which represents an area or its population exposed to different ranges of noise. The same Directive defines that environmental noise is influenced by different sources, including transport routes such as road traffic and the subway. This study evaluates the acoustic balance between the Sevilla metro and the A-376 highway traffic. For such assessment, different mobility scenarios have been developed and all of them have been evaluated using noise maps. A residential block in Dos Hermanas, a town in Sevilla province, has been taken as a case study. According to the evaluated scenarios, the population affected by high level noises decreases as the metro is more used than the highway.

Noise2Grad: Extract Image Noise to Denoise

In many image denoising tasks, the difficulty of collecting noisy/clean image pairs limits the application of supervised CNNs. We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. To ease the interference of the image background, we use a noise removal module to aid noise extraction. The noise removal module first roughly removes noise from the noisy image, which is equivalent to excluding much background information. A noise approximation module can therefore easily extract a new noise map from the removed noise to match the gradient of the noisy input. This noise map is added to a random clean image to synthesize a new data pair, which is then fed back to the noise removal module to correct the noise removal process. These two modules cooperate to extract noise finely. After convergence, the noise removal module can remove noise without damaging other background details, so we use it as our final denoising network. Experiments show that the denoising performance of the proposed method is competitive with other supervised CNNs.

A closer look at rail methodology in the BTS National Transportation Noise Map

In 2017, the Bureau of Transportation Statistics released the inaugural national, multi-modal transportation noise map prototype. The noise modeling and mapping effort was envisioned as a way to facilitate the geographic tracking of national trends and provide insight into transportation noise-related questions as changes occur over time - changes between modes, types of vehicles within modes and the geographic shifts of populations. How do changes in aircraft technology change the transportation noise landscape? Does increased high speed rail availability affect highway-related noise? How does a population shift away from urban centers affect the soundscape? The inaugural model included aviation and highway sources. The first update, released in November 2020, includes passenger rail-related noise in addition to aviation and highway sources. Operations in this new mode include commuter rail mainline, high-speed electric, light rail, heavy rail and streetcars, along with commuter rail horns at highway-rail grade crossings. The data for this noise map were modeled based on USDOT methods, with adjustments and simplifications to model on a national scale. This paper focuses on the modeling methods and geospatial approach used to develop the passenger rail noise data layer.

Statistical Road-Traffic Noise Mapping Based on Elementary Urban Forms in Two Cities of South Korea

Statistical models that can generate a road-traffic noise map for a city or area where only elementary urban design factors are determined, and where no concrete urban morphology, including buildings and roads, is given, can provide basic but essential information for developing a quiet and sustainable city. Long-term cost-effective measures for a quiet urban area can be considered at early city planning stages by using the statistical road-traffic noise map. An artificial neural network (ANN) and an ordinary least squares (OLS) model were developed by utilizing data on urban form indicators, based on a 3D urban model and road-traffic noise levels from a normal noise map of city A (Gwangju). The developed ANN and OLS models were applied to city B (Cheongju), and the resultant statistical noise map of city B was compared to an existing normal road-traffic noise map of city B. The urban form indicators that showed multi-collinearity were excluded by the OLS model, and among the remaining urban forms, road-related urban form indicators such as traffic volume and road area density were found to be important variables to predict the road-traffic noise level and to design a quiet city. Comparisons of the statistical ANN and OLS noise maps with the normal noise map showed that the OLS model tends to under-estimate road-traffic noise levels, and the ANN model tends to over-estimate them.