Pith Number

pith:CWP4EF74

pith:2026:CWP4EF74NTH73GU7XGDPU6SWE7

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Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones

Alex Dunnett, Basaran Bahadir Kocer, Leonie Bottomley, Mirko Kovac

A vision system lets tree-perching drones pick suitable branches by measuring width, slope, and curvature from single 2D photos.

arxiv:2605.15430 v1 · 2026-05-14 · cs.RO · cs.CV

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\usepackage{pith}
\pithnumber{CWP4EF74NTH73GU7XGDPU6SWE7}

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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

For a given tree-perching drone and a dataset of more than 10,000 urban tree images taken from February to October in a subtropical and temperate monsoon climate, the proposed method successfully produces a result for 76% of feasible targets.

C2weakest assumption

That standard 2D image segmentation and morphology can reliably extract accurate branch width, slope, and curvature measurements from single-view urban tree photographs without depth information or 3D reconstruction.

C3one line summary

A computer vision pipeline assesses urban tree branches for drone perching suitability on width, slope and curvature, achieving 76% success on feasible targets from over 10,000 images.

References

37 extracted · 37 resolved · 1 Pith anchors

[1] Sustainable ai: Environmental implications, challenges and opportunities 2022

[2] United Nations Department For Economic And Social.Sustainable Develop- ment Goals Report 2024. eng. OCLC: 1492039046. S.l.: UNITED NATIONS, 2024.isbn: 978-92-1-003135-6.url:https://unstats.un.org/sdgs 2024

[3] Bistable Helical Origami Gripper for Sensor Placement on Branches 2022 · doi:10.1002/aisy.202200087

[4] Seeing the Forest from Drones: Testing the Potential of Lightweight Drones as a Tool for Long-Term Forest Monitoring 2016 · doi:10.1016/j.biocon.2016.03.027

[5] Vision Based Crown Loss Estimation for Individual Trees with Remote Aerial Robots 2022 · doi:10.1016/j.isprsjprs.2022.04.002

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:58.248496Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

159fc217fc6ccffd9a9fb986fa7a5627e333a0bbdecc54bae95115d9c254e457

Aliases

arxiv: 2605.15430 · arxiv_version: 2605.15430v1 · doi: 10.48550/arxiv.2605.15430 · pith_short_12: CWP4EF74NTH7 · pith_short_16: CWP4EF74NTH73GU7 · pith_short_8: CWP4EF74

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/CWP4EF74NTH73GU7XGDPU6SWE7 \
  | 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: 159fc217fc6ccffd9a9fb986fa7a5627e333a0bbdecc54bae95115d9c254e457

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "823dfbfde1e2a5769cc9b9517cb04575b39e77d2a7420da179043bb6e3a0beeb",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-14T21:25:11Z",
    "title_canon_sha256": "7ebae8907adb44f1549cc5c6992a3fcb488a69b5218fb976c3b3615585e15570"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15430",
    "kind": "arxiv",
    "version": 1
  }
}