Automatic detection of passable roads after floods in remote sensed and social media data

作者：

Highlights：

• This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery.

• The tasks carried out in this work are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information.

• Mainly relies on the deep models for both task.

摘要

•This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery.•The tasks carried out in this work are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information.•Mainly relies on the deep models for both task.

论文关键词：Flood detection,Convolutional neural networks,Natural disasters,Social media,Satellite imagery,Multimedia indexing and retrieval

论文评审过程：Received 14 December 2018, Revised 6 February 2019, Accepted 6 February 2019, Available online 13 February 2019, Version of Record 19 February 2019.

论文官网地址：https://doi.org/10.1016/j.image.2019.02.002