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

• URL: http://arxiv.org/abs/2004.07438v4
• Date: Thu, 12 Nov 2020 14:45:11 GMT
• Title: Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic
• Authors: Rodrigo Minetto, Mauricio Pamplona Segundo, Gilbert Rotich, Sudeep Sarkar
• Abstract summary: We use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) This CNN ensemble framework ranked third place in the US Department of Defense xView challenge. We show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators.
• Score: 6.623965960680924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 work, 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 (https://github.com/maups/covid19-satellite-analysis).

Related papers

• Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning [11.697034536189094]
  We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification. Our findings demonstrate that our method surpasses both passive and conventional active learning techniques. It effectively addresses the'sampling bias' challenge in traditional active learning strategies.
  arXiv  Detail & Related papers  (2024-05-28T01:34:35Z)
• The State of Aerial Surveillance: A Survey [62.198765910573556]
  This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
  arXiv  Detail & Related papers  (2022-01-09T20:13:27Z)
• IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [90.97859312029615]
  We propose an approach to estimate object count statistics over large geographies through sampling. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S.
  arXiv  Detail & Related papers  (2021-12-16T18:59:29Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Measuring economic activity from space: a case study using flying airplanes and COVID-19 [15.003277734711583]
  We present a case study for the COVID-19 coronavirus outbreak, which imposed severe mobility restrictions and caused worldwide disruptions. We use flying airplane detection around the 30 busiest airports in Europe to quantify and analyze the lockdown's effects and post-lockdown recovery. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities.
  arXiv  Detail & Related papers  (2021-04-21T04:01:25Z)
• Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features [0.2538209532048866]
  We propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster.
  arXiv  Detail & Related papers  (2020-12-15T21:30:19Z)
• Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping [17.324397643429638]
  We have developed a global multisensor and multitemporal dataset for building damage mapping. The global dataset contains high-resolution optical imagery and high-to-moderate-resolution multiband SAR data. We defined a damage mapping framework for the semantic segmentation of damaged buildings based on a deep convolutional neural network algorithm.
  arXiv  Detail & Related papers  (2020-09-14T05:04:19Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• Improving Emergency Response during Hurricane Season using Computer Vision [0.06882042556551608]
  We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster. We have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery.
  arXiv  Detail & Related papers  (2020-08-17T15:42:02Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)
• Generating Interpretable Poverty Maps using Object Detection in Satellite Images [80.35540308137043]
  We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
  arXiv  Detail & Related papers  (2020-02-05T02:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.