Extracting Flood Maps from Social Media for Assimilation

This abstract states the position of the Publimape project, and unveils progress achieved since its recent start.

[1]  P. D. Batesa,et al.  A simple raster-based model for flood inundation simulation , 2000 .

[2]  Anthony Stefanidis,et al.  #Earthquake: Twitter as a Distributed Sensor System , 2013, Trans. GIS.

[3]  Halit Oguztüzün,et al.  A survey on location estimation techniques for events detected in Twitter , 2017, Knowledge and Information Systems.

[4]  Marco Chini,et al.  A new approach for improving flood model predictions based on the sequential assimilation of SAR-derived flood extent maps , 2014 .

[5]  Dmitri Kavetski,et al.  Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development , 2011 .

[6]  Zhong Zhou,et al.  Tweet2Vec: Character-Based Distributed Representations for Social Media , 2016, ACL.

[7]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[8]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[9]  Thomas Tamisier,et al.  Integrating Data Streams from in-situ Measurements, Social Networks and Satellite Earth Observation to Augment Operational Flood Monitoring and Forecasting: the 2017 Hurricane Season in the Americas as a Large-scale Test Case , 2017 .

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[11]  Stuart E. Middleton,et al.  Real-Time Crisis Mapping of Natural Disasters Using Social Media , 2014, IEEE Intelligent Systems.