Coronavirus is known to cause severe acute respiratory syndrome (SARS). The effects of the infections were severe in the case of premedical conditions in the subjects. A case in this point; the prolonged exposure of air pollution and associated health risks. In this work we study the relation between mortality and the long-term exposure to air pollution in urban centers of Maharashtra, India. In addition to analyzing the general trend, we focused on the cities in western Maharashtra, which are more developed as compared to the rest of the Maharashtra. The main objective of the study was to establish the relation between the air pollution and COVID-19 morbidity. The secondary objective was to establish the air pollution as a parameter for susceptibility to COVID-19 like pandemics. We used Sentinel-5P data for extracting the pollution concentration of sulphur dioxide (SO2), Nitrogen dioxide (NO2), Aerosol index, carbon monoxide (CO), and ozone (O3). The deaths in these cities were collected from the news reports. The relation between COVID-19 deaths and high-level of air pollution was amply evident from the analysis. The long-term exposure to pollution in the cities was found to be correlated with COVID-19 deaths. Furthermore, more industrialised cities showed stronger correlation. This may be attributed to the old part of cities where narrow roads confined by very closely space buildings on both the sides, heavy vehicular pollution, and poor ventilation often create a smoke chamber like situation. This needs to be investigated further using case specific data.
Any high spatial resolution space-borne electro-optical sensing system operating in long wavelengths, like Earth-observation facilities operating in the longwave infrared are subjected to an inherent design and implementation challenge of deploying large monolithic primary aperture mirrors, to achieve a ground resolution distance of a few tens of cm. To outflank this issue, many present-date missions design and commission lightweight segmented mirrors, mostly with equal sized sub-apertures. One step ahead, these sub-apertures could be of particular non-uniform size distributions (One-by-Three, Taylor-ln and Taylor-invtan), thereby ensuring a smaller and even lighter primary and with marginal compromise in imaging quality due to significant sidelobe suppression. This is also confirmed by the fact that these particular non-uniform sized mirrors have very less loss of spatial frequencies with respect to that of equal-sized segmented mirrors. Therefore, under lossless conditions, there is hardly any degradation in imaging performance of these two configurations. However, in the presence of gaussian, impulse and shot noise, the situation worsens because of the compromised collecting area as well as noise contribution. A simple deconvolution technique for image restoration in presence of noise is no longer possible because of the lack of convergence. This calls upon for the use of iterative reconstruction algorithms with denoisers like Total Variation (TV), Block Matching and 3D Filtering (BM3D) or Convolutional Neural Networks (CNN) in the post-processing step to ensure better output images with high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) along with good edge and texture preservation of the features. A comparison of these three kinds of denoisers, TV, BM3D and DnCNN implemented as a part of the Alternating Directions Method of Multipliers (ADMM) reconstruction technique is presented in this work. It is seen that in presence of some shot noise, random gaussian noise with σ= 0.03 and some impulse noise, the best performance is achieved for ADMM-BM3D technique with comparable performance from the ADMM-DnCNN method (except for Taylor-ln design). On the contrary, denoising with TV can perform well only in presence of shot noise. Additionally, this technique is nearly rejected for use in case of the Taylor-invtan model because of extremely low SSIM when all three noise types are incorporated.
Industrial areas identification is an important problem in detecting urban land use. The industrial area contributes significantly to the carbon footprint and economic status of the region. Industrial buildings/factories are marked by metal roofs. We attempt to leverage this reasonably characteristic association for detecting industrial buildings. The foundation of our study is the spectral properties of industrial roofs which have high reflectance and flat spectrum. With these spectral properties of metal roofs, we have designed an algorithm with less time complexity as compared to the other approaches like matching reference signature with every pixel in the image or matched filter target detection approaches. The algorithm to detect the metal roof and hence industrial shade is divided into two main parts: 1. Calculating the relative reflectance of the image. 2. Calculating the spectral flatness of the pixels. In step one we use the high reflectance characteristics to calculate the relative reflectance of the image based on percentile brightness. In step two we use the flatness of the spectrum with the mean of consecutive band ratios. Thresholding on this band ratio gives us the industrial roof pixels. The algorithm is tested on the very well known hyperspectral images like Pavia University and Urban Image.
Deformation map prediction is a critical tool to foresee signs of abnormal events. Such forecasting facilitates quick countermeasure to avoid undesirable conditions. This work presents a novel recurrent neural network to forecast time-series deformation maps from InSAR data. Our proposed recurrent network employs a multi-scale attention mechanism to identify vital temporal features that influences subsequent deformation maps. We have evaluated our model on volcanic monitoring data using the Micronesia islands (Canary and Cape Verde archipelagos) Sentinel-1 imagery acquired between 2015 and 2018. The proposed method achieves minimal prediction error compared to the observed deformation values, suggesting the high reliability of our approach. The experimental results indicate the superiority of the proposed method in forecasting deformation maps with high accuracy compared to existing state-of-the-art approaches. Various ablation studies were conducted to study and validate the effectiveness of the multi-scale attention mechanism for deformation map forecasting.
Urban land use classes of complex nature are marked by the presence of multiple land covers and/or objects in the specific spatial order. The spatial configuration of the constituent parts of the land use class is generally unique. To the extent that the specific spatial configuration is defining characteristic of a given land use class. These characteristics can be effectively leveraged to identify the land use class. In this research, we exploit the unique spatial structure of the constituent parts for the land use class for its detection. We use capsule network (CapsuleNet) for detecting some of the urban land use classes such as parking lot and golf courses. CapsuleNets use a group of neurons (called capsules) in a convolutional layer to detect a specific image primitive. Each subsequent layer detects higher order primitives, and its relationship with the lower level primitives. Thus, multiple such layers build a hierarchy of parts to learn the whole object, in this case the land use class. We conducted multiple experiments for detecting parking lots and golf courses in a collection of urban images. We used NWPU-RESISC45 dataset for conducting our experiments. Furthermore, we compared the results of CapsuleNet based architecture with standard architecture such as VGG16, which do not consider the spatial structure of the features. Our initial experiments suggest improvement in accuracy in classification of the land use classes such as parking lot and golf courses.
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