Post flood assessment using deep learning techniques

Author:

Kulkarni Sanket S.,Mahapatra Ansuman

Publisher

AIP Publishing

Reference22 articles.

1. Arvind, C. S., Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar, Flood assessmentusing multi-temporal MODIS satellite images, (Procedia Computer Science, 2016), vol. 89, pp. 575–586.

2. Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning

3. Detection of Expanded Reformed Geographical Area in Bi-temporal Multispectral Satellite Images Using Machine Intelligence Neural Network

4. Jain, Pallavi, Bianca Schoen-Phelan, and Robert Ross. Tri-Band Assessment of Multi-Spectral Satellite Datafor Flood Detection, in MAChine Learning for EArth ObservatioN Workshop co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020, vol 2766.

5. Gupta, Ritwik, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, and Matthew Gaston, xBD: A dataset for assessing building damage from satellite imagery, arXiv preprint arXiv:1911.09296, 2019, pp. 10–17.

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