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Automated detection of retrogressive thaw slumps in high-resolution optical imagery using a knowledge-distilled Transformer
ABSTRACT Climate warming is accelerating Arctic permafrost degradation and intensifying retrogressive thaw slump (RTS) activity. To address the limited availability of RTS samples, large target-scale variation and unstable bounding-box localization in complex high-resolution optical remote-sensing imagery, this study proposes an improved DINO-based detection framework, SM-DINO.
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