Pith Number

pith:MDHM4CNN

pith:2026:MDHM4CNNW36TNV3YAAYKL7ZQ4A

not attested not anchored not stored refs resolved

Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation

Feng Gao, Junyu Dong, Qian Du, Yanhai Gan, Zhilin Jin

ARG-Mamba fuses optical and elevation features along axial relations within a state space model to improve remote sensing segmentation accuracy.

arxiv:2605.16768 v1 · 2026-05-16 · cs.CV · eess.IV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{MDHM4CNNW36TNV3YAAYKL7ZQ4A}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

ARG-Mamba consistently outperforms state-of-the-art methods while maintaining favorable computational efficiency on the ISPRS Vaihingen and Potsdam datasets.

C2weakest assumption

The assumption that explicitly modeling global cross-modal correlations along horizontal and vertical axes via the Axial-Relation Guided Fusion Module yields superior feature fusion compared with existing cross-modal methods, as described in the module design.

C3one line summary

ARG-Mamba combines a multi-scale state space module and an axial-relation guided fusion module to outperform prior methods on optical-elevation semantic segmentation for remote sensing.

References

16 extracted · 16 resolved · 1 Pith anchors

[1] Deep learning in remote sensing applications: A meta-analysis and review, 2019

[2] Adaptive frequency enhancement network for remote sensing image semantic segmentation, 2025

[3] Elevation information- guided multimodal fusion robust framework for remote sensing image segmentation, 2024

[4] A multilevel multimodal fusion Transformer for remote sensing semantic segmentation, 2024

[5] A multisensor data fusion model for semantic segmentation in aerial images, 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:20.937477Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

60cece09adb6fd36d7780030a5ff30e01edce490adb614796562eb5419c631ea

Aliases

arxiv: 2605.16768 · arxiv_version: 2605.16768v1 · doi: 10.48550/arxiv.2605.16768 · pith_short_12: MDHM4CNNW36T · pith_short_16: MDHM4CNNW36TNV3Y · pith_short_8: MDHM4CNN

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/MDHM4CNNW36TNV3YAAYKL7ZQ4A \
  | 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())"
# expect: 60cece09adb6fd36d7780030a5ff30e01edce490adb614796562eb5419c631ea

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "546a259ca469f2aa3409c5a120aab231c890243fcccc4541d9694d3953308e99",
    "cross_cats_sorted": [
      "eess.IV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T02:42:54Z",
    "title_canon_sha256": "e1aa8f9f281ad7293a33508405542aceb392dd54399bc778965eb0cdbdc47708"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16768",
    "kind": "arxiv",
    "version": 1
  }
}