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pith:ANTH6KUT

pith:2026:ANTH6KUTYTO2J7OVIL3BKGF2GA
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Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

Arsalaan Ahmad, Oktay Karakus, Paul L. Rosin

Sequential forward floating selection finds an 8-channel subset that matches or exceeds full 30-channel accuracy for landslide segmentation.

arxiv:2605.09746 v2 · 2026-05-10 · cs.LG · cs.AI

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Claims

C1strongest claim

Beyond identifying a compact 8-channel subset that matches or exceeds the segmentation F1 of configurations using up to 30 channels, we use the selection process itself to interrogate which spectral and topographic features landslide models genuinely rely on, and what this reveals about the physical cues driving their predictions.

C2weakest assumption

That the lightweight U-Net++ proxy model used during SFFS iterations accurately captures interaction effects and performance trends of the final full-scale segmentation model, and that single-band drop tests miss interactions while SFFS does not.

C3one line summary

Sequential Forward Floating Selection with a U-Net++ proxy identifies an 8-channel subset from multi-spectral and terrain data that matches or exceeds F1 scores of full 30-channel configurations for landslide segmentation.

Receipt and verification
First computed 2026-06-23T01:12:08.113468Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

03667f2a93c4dda4fdd542f61518ba30394b5b39a1e5765a18a234673d029dfe

Aliases

arxiv: 2605.09746 · arxiv_version: 2605.09746v2 · doi: 10.48550/arxiv.2605.09746 · pith_short_12: ANTH6KUTYTO2 · pith_short_16: ANTH6KUTYTO2J7OV · pith_short_8: ANTH6KUT
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/ANTH6KUTYTO2J7OVIL3BKGF2GA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-10T20:46:30Z",
    "title_canon_sha256": "81d58aea45f890d5296417d52cf14f7101a9c2a77b3e771bb25c93aecb6ea106"
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