Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. The deadly Covid 19 viruses suddenly stopped the fast-moving world and all the commercial and noncommercial activities were stalled for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification (PPC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper classification (SAMC) and Spectral information divergence classification (SIDC) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that PPChas obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through MDC, MaDC, and MLCclassification algorithms. © 2022 The Author(s)

Social Distancing Violation Detection in Video Using ChessBoard and Bird's-eye Perspective

Thousands of infections, hundreds of deaths every day - these are numbers that speak the current serious status, numbers that each of us is no longer unfamiliar with in the current context, the context of the raging epidemic - Coronavirus disease epidemic. Therefore, we need solutions and technologies to fight the epidemic promptly and quickly to prevent or reduce the effect of the epidemic. Numerous studies have warned that if we contact an infected person within a distance of fewer than two meters, it can be considered a high risk of infecting Coronavirus. To detect a contact distance shorter than two meters and provides warnings to violations in monitoring systems based on a camera, we present an approach to solving two problems, including detecting objects - here are humans and calculating the distance between objects using Chessboard and bird's eye perspective. We have leveraged the pre-trained InceptionV2 model, a famous convolutional neural network for object detection, to detect people in the video. Also, we propose to use a perspective transformation algorithm for the distance calculation converting pixels from the camera perspective to a bird's eye view. Then, we choose the minimum distance from the distance in the determined field to the distance in pixels and calculate the distance violation based on the bird's eye view, with camera calibration and minimum distance selection process based on field distance. The proposed method is tested in some scenarios to provide warnings of social distancing violations. The work is expected to generate a safe area providing warnings to protect employees in administrative environments with a high risk of contacting numerous people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Optimum supervised classification algorithm identification by investigating PlanetScope and Skysat multispectral satellite data of Covid lockdown

This research identifies the optimum supervised classification algorithm based on modeling Covid 19 lockdown situations all around the World. As the deadly Covid 19 viruses suddenly stopped the fast-moving World. All the commercial and noncommercial activities suddenly stop for an uncertain period during 2020-2021. In this work, object-based image classification approaches have been used to compare pre-Covid and post-Covid (at the time lockdown) images of the study area. These study areas are Washington DC, USA, Sao Paulo, Brazil, Cairo, Egypt, Afghanistan/Iran border, and Beijing, China. All the study areas possess different geographical conditions but have a similar situation of Covid 19 lockdowns. Six supervised image classification techniques are known as Parallelepiped classification (PPC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper classification (SAMC) and Spectral information divergence classification (SIDC) are used to classify the satellite data of the study area. Thus based on classification results and statistical features, it has been observed that PPChas obtained the least significant results. In contrast, the most reliable results and highest classification accuracies are obtained through MDC, MaDC, and MLCclassification algorithms.

Neutrosophic sets in determining Corona virus.

An attempt that is made here is to apply neutrosophic sets to a medical data. By means of extended Hausdorff minimum distance we find out the core symptoms of the patients. From the minimum distance or the core symptoms we can get a clue for the type of disease affecting the patient.