Measuring Human and Economic Activity From Satellite Imagery to Support City-Scale Decision-Making During COVID-19 Pandemic

作     者：Minetto, Rodrigo Segundo, Mauricio Pamplona Rotich, Gilbert Sarkar, Sudeep 

作者机构：Univ Tecnol Fed Parana UTFPR BR-80230901 Curitiba Parana Brazil Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA 

出 版 物：《IEEE TRANSACTIONS ON BIG DATA》 (IEEE Trans. Big Data)

年 卷 期：2021年第7卷第1期

页      面：56-68页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：USA National Science Foundation [CNS-1513126] NVIDIA Corporation University of South Florida for the Institute for Artificial Intelligence (AI+X) 

主　　题：Satellites COVID-19 Economics Big Data Buildings Pandemics Remote sensing CNN-based object detection human and economic activity assessment COVID-19 pandemic 

摘      要：The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.(1) 1. https://***/maups/covid19-satellite-analysis.