# A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence

arXiv: https://arxiv.org/abs/2202.07475 · HTML: https://mlsanity.com/paper/2202.07475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response](https://mlsanity.com/paper/2004.11838/) (2020)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises](https://mlsanity.com/paper/1704.02602/) (2017)
- [Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence](https://mlsanity.com/paper/2004.06675/) (2020)
- [Landslide Detection in Real-Time Social Media Image Streams](https://mlsanity.com/paper/2110.04080/) (2021)

## Built on by (most cited first, 1 of 1)

- [Analyzing social media with crowdsourcing in Crowd4SDG](https://mlsanity.com/paper/2208.02689/) (2022)

## Abstract (arXiv)

This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques. The designed system can (i) reduce the information overload by eliminating duplicate and irrelevant content, (ii) identify landslide images, (iii) infer geolocation of the images, and (iv) categorize the user type (organization or person) of the account sharing the information. The system was deployed in February 2020 online at https://landslide-aidr.qcri.org/landslide_system.php to monitor live Twitter data stream and has been running continuously since then to provide time-critical information to partners such as British Geological Survey and European Mediterranean Seismological Centre. We trust this system can both contribute to harvesting of global landslide data for further research and support global landslide maps to facilitate emergency response and decision making.