اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles
110
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.
قيم البحث
اقرأ أيضاً
Street imagery is a promising big data source providing current and historical images in more than 100 countries. Previous studies used this data to audit built environment features. Here we explore a novel application, using Google Street View (GSV)
to predict travel patterns at the city level. We sampled 34 cities in Great Britain. In each city, we accessed GSV images from 1000 random locations from years overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. Outcomes included Census-reported commute shares of four modes (walking plus public transport, cycling, motorcycle, and car), and APS-reported past-month participation in walking and cycling. In bivariate analyses, we found high correlations between GSV counts of cyclists (GSV-cyclists) and cycle commute mode share (r=0.92) and past-month cycling (r=0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r=0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r=0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r=0.81). GSV-car was not correlated with car commute mode share (r=-0.12). However, in multivariable regression models, all mode shares were predicted well. Cross-validation analyses showed good prediction performance for all the outcomes except past-month walking. Street imagery is a promising new big data source to predict urban mobility patterns. Further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.
Out of the numerous hazards posing a threat to sustainable environmental conditions in the 21st century, only a few have a graver impact than air pollution. Its importance in determining the health and living standards in urban settings is only expec
ted to increase with time. Various factors ranging from emissions from traffic and power plants, household emissions, natural causes are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We analyze and explore the dataset to bring out inferences which we can derive by modeling the data. Also, we provide a set of benchmarks for the problem of estimating or forecasting pollutant levels with a set of diverse models and methodologies. Through our paper, we seek to provide a ground base for further research into this domain that will demand critical attention of ours in the near future.
Streetscapes are an important part of the urban landscape, analysing and studying them can increase the understanding of the cities infrastructure, which can lead to better planning and design of the urban living environment. In this paper, we used G
oogle API to obtain street view images of Osaka City. The semantic segmentation model PSPNet is used to segment the Osaka City street view images and analyse the Green View Index (GVI) data of Osaka area. Based on the GVI data, three methods, namely corridor analysis, geometric network and a combination of them, were then used to calculate the optimal GVI paths in Osaka City. The corridor analysis and geometric network methods allow for a more detailed delineation of the optimal GVI path from general areas to specific routes. Our analysis not only allows for the calculation of specific routes for the optimal GVI paths, but also allows for the visualisation and integration of neighbourhood landscape data. By summarising all the data, a more specific and objective analysis of the landscape in the study area can be carried out and based on this, the available natural resources can be maximised for a better life.
Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurrin
g increases with the ever-growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pede
strians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic output image such that the moving object is not visible anymore. We evaluate our results on a dataset of 1000 images to obtain a peak noise-to-signal ratio (PSNR) and L1 loss of 27.2 dB and 2.5%, respectively. To ensure the subjective quality, To assess overall quality, we also report the results of a survey conducted on 35 professionals, asked to visually inspect the images whether object removal and inpainting had taken place. The inpainting dataset will be made publicly available for scientific benchmarking purposes at https://research.cyclomedia.com
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
