Chapter

# Towards Improved Air Quality Monitoring Using Publicly Available Sky Images

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## Abstract

Air pollution causes nearly half a million premature deaths each year in Europe. Despite air quality directives that demand compliance with air pollution value limits, many urban populations continue being exposed to air pollution levels that exceed by far the guidelines. Unfortunately, official air quality sensors are sparse, limiting the accuracy of the provided air quality information. In this chapter, we explore the possibility of extending the number of air quality measurements that are fed into existing air quality monitoring systems by exploiting techniques that estimate air quality based on sky-depicting images. We first describe a comprehensive data collection mechanism and the results of an empirical study on the availability of sky images in social image sharing platforms and on webcam sites. In addition, we present a methodology for automatically detecting and extracting the sky part of the images leveraging deep learning models for concept detection and localization. Finally, we present an air quality estimation model that operates on statistics computed from the pixel color values of the detected sky regions.

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... The geo-tagged social data adds to the knowledge of the pollution levels such as hotspot, health exposures, human responses and awareness (Charitidis et al., 2019). Data extracted from publicly available images from social media correlated with monitoring data, satellite and meteorology data is proving to be useful in nowcasting and AQI prediction (Kosmidis et al., 2018;Spyromitros-Xioufis et al., 2018;Khaefi et al. (2018). ...
... al., 2019;Piedrahita et al., 2014;Hu et al., 2014;Maag et al., 2018;Henderson et al., 2016;Arvind et al., 2016;Chen et al., 2018; Yarza et al.al., 2015;Castell et al., 2015;Gately et al., 2017;Steinle et al., 2015;Skjetne and Liu, 2017;Steinle et al., 2015;Constant, 2018;de Nazelle et al., 2013;Larkin and Hystad, 2017;Nyhan et al., 2016Nyhan et al., , 2019 Steinle et al.al., 2019;Kosmidis et al., 2018;Spyromitros-Xioufis et al., 2018;Khaefi et al., 2018;Du et al., 2016;Jiang et al., 2019;Dong et al., 2019;Zheng et al., 2019aZheng et al., , 2019bUpadhyay and Upadhyay, 2017;Yan et al., 2019; Jiang et al.al., 2015;Degbelo et al., 2016; EEA, 2019;English et al., 2018;Jerrett et al., 2017;Liu et al., 2014;Castell et al., 2018;Davis et al., 2020;Mahajan et al., 2020;Snyder et al., 2013;Zheng et al., 2019a;US EPA, 2015;Hano et al. ...
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... The second approach for sky localization consists of a set of heuristic rules that aim to identify pixels that meet certain criteria with respect to their color values and the color values of neighboring pixels. In rough terms (a more detailed description of this algorithm can be found in [18]), if R, G, and B denote the Red, Green, and Blue values of each pixel, sky pixels must satisfy the following three conditions: ...
... Furthermore, sky radiances obtained by digital cameras were compared with CIMEL sunphotometer radiances, finding mean absolute differences between 2% and 15% except for pixels near the sun and high scattering angles [25]. The method used in hackAIR is based on the comparison of the R/G and G/B ratios from images and precalculated LUTs to retrieve AOD [18]. ...
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... The sky regions of the images are used in their analysis and achieved maximum accuracy of 59.38% using CNN. Spyromitros-Xioufis E et al. [4] also used the sky region of visual image for development of air quality estimation model. They performed the whole analysis into three-step: sky region detection, sky region localization, air quality estimation. ...
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... Finally, it would be interesting to study whether better accuracy could be obtained by exploiting the image content of tweets using image-based air quality estimation approaches (e.g. [25]). ...
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This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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