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

## TL;DR

This paper presents novel deep learning methods for detecting passable roads after floods using satellite and social media images, improving accuracy over existing approaches.

## Contribution

It introduces two new solutions for flood aftermath detection, combining object/scene features and transfer learning for improved classification accuracy.

## Key findings

- Significant performance improvement over state-of-the-art methods
- Effective fusion of multi-source image features
- Successful application of deep models for flood-related road passability detection

## Abstract

This paper addresses the problem of floods classification and floods aftermath detection utilizing both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks 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. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods are carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.