Abstract
The COVID-19 epidemic has made governments around the world to enforce lockdowns and isolations to stop the spread of virus. Both human and financial activities are affected throughout the globe. It takes time to recover from these losses. Financial actions influence social activities which incorporate signatures in satellite images that can be perceived and categorized. Satellite imagery aids in making decisions of predictors and decision makers by offering diverse types of perceptibility in the relating financial changes. In this paper, deep learning methods including Fast Region-based Convolutional Network (Fast R-CNN) and You Only Look Once (YOLO) are employed to identify the detailed elements in satellite images that can be used to find the financial indicators based on it. The proposed system uses Histogram Equalizer (HE) for enhancing the satellite pictures to provide accurate analysis about human movements. The system shows results on genuine instances of various destinations when COVID-19 flares up to delineate extraordinary quantifiable markers. The area is partitioned into different sections and the human and economic activities are identified. Mobility of people shows the spreading of COVID-19. YOLO offers the best performance in object (vehicle) identification from which the presence of economic downfall is predicted.
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Deva Priya, M., Sahaya Gebin, A., Selva Kumar, S., Vipin, R.G. (2022). Deep Learning Algorithms based Vehicle Mobility Prediction from Satellite Imagery During Pandemic. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds) Proceedings of International Conference on Recent Trends in Computing . Lecture Notes in Networks and Systems, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-16-7118-0_31
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DOI: https://doi.org/10.1007/978-981-16-7118-0_31
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