Pith Number

pith:ZL4FCCFP

pith:2026:ZL4FCCFPROJ5MGRRVMFO7DGLP5

not attested not anchored not stored refs resolved

Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery

(2) YM Lab., 3) ((1) DeepForest Technologies Co., (3) Graduate School of Agriculture, 4), (4) Graduate School of Science, 5), (5) Faculty of Tropical Forestry, (6) Forest Research Centre), Kanehiro Kitayama (3, Kengo Ikebata (1), Kyaw Kyaw Htoo (3), Kyoto University, Ltd., Masanori Onishi (1, Mitsutaka Nakada (1), Osaka Metropolitan University, Robert Ong (6), Ryuichi Takeshige (3, Takahiko Ikebata (1), Universiti Malaysia Sabah, Yuji Mizuno (2), Yusuke Onoda (3)

A Mask2Former model trained on 18,507 hand-annotated crown polygons segments individual tree crowns in complex broadleaf forests from UAV RGB imagery and generalizes to other regions and forest types.

arxiv:2605.15673 v1 · 2026-05-15 · eess.IV · cs.CV · cs.LG

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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

The best model achieved high segmentation performance in structurally complex broadleaf forests using only RGB imagery. This performance was maintained when applied to geographically distinct forests within Japan, as well as to biologically distinct tropical rainforests in Borneo.

C2weakest assumption

The 18,507 manually delineated crown polygons from seven Japanese forests supply accurate ground truth and sufficient ecological diversity for the model to generalize reliably to structurally and biologically different forests such as Bornean tropical rainforests.

C3one line summary

Mask2Former instance segmentation model trained on 18,507 high-quality crown annotations from Japanese broadleaf forests achieves strong performance and cross-region generalization to tropical rainforests using only RGB UAV data.

References

24 extracted · 24 resolved · 0 Pith anchors

[1] Detection of individual tree crowns in airborne lidar data 2006

[2] Imai, and Antonio M.G 2017

[3] Individual tree parameters estimation for plantation forests based on uav oblique photography.IEEE Access, 8:96184–96198, 2020 2020

[4] Automatic classification of trees using a uav onboard camera and deep learning 2018

[5] Kyaw Kyaw Htoo, Masanori Onishi, Md Farhadur Rahman, Ryuichi Takeshige, Kaoru Kitajima, and Yusuke Onoda. Development of crown-based allometric equations for estimating stem diameter and above-ground 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:11.658373Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

caf85108af8b93d61a31ab0aef8ccb7f4217fa5d0bca3ea18f3fea07468a9efe

Aliases

arxiv: 2605.15673 · arxiv_version: 2605.15673v1 · doi: 10.48550/arxiv.2605.15673 · pith_short_12: ZL4FCCFPROJ5 · pith_short_16: ZL4FCCFPROJ5MGRR · pith_short_8: ZL4FCCFP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZL4FCCFPROJ5MGRRVMFO7DGLP5 \
  | 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: caf85108af8b93d61a31ab0aef8ccb7f4217fa5d0bca3ea18f3fea07468a9efe

Canonical record JSON

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    "abstract_canon_sha256": "53203010ad77c4da22bbaac965ff5854fbc676674a3f99bf0eeb242c0016d125",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-15T06:50:32Z",
    "title_canon_sha256": "84d590705d63c6ea5b4d31fd34c5517ac7c1227378283cc193153f20af5dd041"
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  "source": {
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    "kind": "arxiv",
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}