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CADE: A lightweight change-adaptive dual-path encoder framework for predicting and generating future changes in satellite imagery using semi-supervised learning.

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Title: CADE: A lightweight change-adaptive dual-path encoder framework for predicting and generating future changes in satellite imagery using semi-supervised learning.
Authors: Sutradhar, Debopom1, Rahman, Md. Abdur1, Yeo, Kheng Cher2, Azid, Sheikh Izzal3, Jonkman, Mirjam2, Azam, Sami2, sami.azam@cdu.edu.au
Source: Neural Computing & Applications; Sep2026, Vol. 38 Issue 18, p1-23, 23p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 197296516
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: CADE: A lightweight change-adaptive dual-path encoder framework for predicting and generating future changes in satellite imagery using semi-supervised learning.
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>; Sep2026, Vol. 38 Issue 18, p1-23, 23p
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        Value: 10.1007/s00521-026-12429-w
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      – Code: eng
        Text: English
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      – TitleFull: CADE: A lightweight change-adaptive dual-path encoder framework for predicting and generating future changes in satellite imagery using semi-supervised learning.
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              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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