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

arXiv: https://arxiv.org/abs/1901.03298 · HTML: https://mlsanity.com/paper/1901.03298/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques](https://mlsanity.com/paper/2301.00321/) (2023)
- [Relevance Classification of Flood-related Twitter Posts via Multiple Transformers](https://mlsanity.com/paper/2301.00320/) (2023)
- [Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter](https://mlsanity.com/paper/2206.03248/) (2022)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)
- [Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies](https://mlsanity.com/paper/2504.20203/) (2025)

## Abstract (arXiv)

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.