Drought classification and prediction with satellite image-based indices using variants of deep learning models

Author:

Chaudhari ShilpaORCID,Sardar Vandana,Ghosh Prosenjit

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Reference48 articles.

1. Rahman MS, Di L, Eugene Yu, Lin Li, Zhiqi Yu (2021) Remote sensing based rapid assessment of flood crop damage using novel disaster vegetation damage index (DVDI). Int J Disaster Risk Sci 12(1):90–110

2. Ghaffarian S, Roy D, Filatova T, Kerle N (2021) Agent-based modelling of post-disaster recovery with remote sensing data. Int J Disaster Risk Reduct 60:102285

3. Kim S, Nho H, Lee J, Kim J, Kim J (2020) Remote sensing and geo-spatial information utilization for managing disaster in korean peninsula. Korean J Remote Sens 36(5–4):1139–1151

4. Long L, He F, Liu H (2021) The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River. J Supercomput 77:1–17

5. Dhanaraj K, Angadi DP (2021) Urban expansion quantification from remote sensing data for sustainable land-use planning in Mangaluru, India. Remote Sens Appl Soc Environ 23:100602

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