Remote sensing-enhanced transfer learning approach for agricultural damage and change detection: A deep learning perspective-Reference-Cited by-同舟云学术

Remote sensing-enhanced transfer learning approach for agricultural damage and change detection: A deep learning perspective

Published:2024-05 Issue: Volume:36 Page:100449
ISSN:2214-5796
Container-title:Big Data Research
language:en
Short-container-title:Big Data Research

Author:

Liu Zehua,Li Jiuhao,Ashraf Mahmood,Syam M.S.,Asif Muhammad,Awwad Emad Mahrous,Al-Razgan Muna,Bhatti Uzair Aslam

Funder

King Saud University

Publisher

Elsevier BV

Reference30 articles.

1. New Earths: Assessing planetary geographic constructs;Deudney,2016

2. Global production patterns: Understanding the relationship between greenhouse gas emissions, agriculture greening and climate variability;Bhatti;Environ. Res.,2024

3. Acuña, A.A.R.L. (2019). Power, planning, and the right to the post-disaster city: exploring opportunities for long-term housing affordability and resilience in earthquake recovery (Doctoral dissertation, Massachusetts Institute of Technology).

4. Overview of the 2010 Haiti earthquake;DesRoches;Earthquake Spectra,2011

5. Hurricane katrina deaths, louisiana, 2005;Brunkard;Disaster. Med. Public Health Prep.,2008

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