# Object Detection on Ground-level urban tree detection dataset

Source: [Object Detection on Ground-level urban tree detection dataset](<https://www.sota2.com/research/sota/object-detection-on-ground-level-urban-tree-detection-dataset>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-07T07:58:16.065Z

Evaluation of Object Detection performance on the Ground-level urban tree detection dataset, reporting Precision, Recall, and F1-Score.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Object Detection |
| Dataset (including any stated split) | Ground-level urban tree detection dataset |
| Metric | Precision |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 4 of 4 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Precision](<https://www.sota2.com/research/sota/object-detection-on-ground-level-urban-tree-detection-dataset.md?metric=Precision>): Higher is better; unit: PERCENT.

- [Recall](<https://www.sota2.com/research/sota/object-detection-on-ground-level-urban-tree-detection-dataset.md?metric=Recall>): Higher is better; unit: PERCENT.

- [F1-Score](<https://www.sota2.com/research/sota/object-detection-on-ground-level-urban-tree-detection-dataset.md?metric=F1-Score>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Precision | Result date | Report paper |
| --- | --- | --- | --- |
| RF-DETR (Hybrid Learning) | 90 | Not recorded | 69d48fa47d0172140ec74408 |
| RF-DETR (Active Learning) | 89 | Not recorded | 69d48fa47d0172140ec74408 |
| RF-DETR (Semi-Supervised) | 85 | Not recorded | 69d48fa47d0172140ec74408 |
| Satellite Model | 81 | Not recorded | 69d48fa47d0172140ec74408 |

### RF-DETR (Hybrid Learning)

Precision: 90. Result ID: 69d4b91747f34ce924bc53f4.

[Reporting paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

Recorded evaluation configuration: {"Learning Strategy":"Hybrid Learning (Avg)"}. This record does not assert independent reproduction.

### RF-DETR (Active Learning)

Precision: 89. Result ID: 69d4b91747f34ce924bc53f3.

[Reporting paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

Recorded evaluation configuration: {"Learning Strategy":"Active Learning (Avg)"}. This record does not assert independent reproduction.

### RF-DETR (Semi-Supervised)

Precision: 85. Result ID: 69d4b91747f34ce924bc53f2.

[Reporting paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

[Method origin paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

Recorded evaluation configuration: {"Learning Strategy":"Semi-Supervised (Avg)"}. This record does not assert independent reproduction.

### Satellite Model

Precision: 81. Result ID: 69d4b91747f34ce924bc53f1.

[Reporting paper](<https://www.sota2.com/research/paper/69d48fa47d0172140ec74408.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa781a1e209131cea76ccf.md>)

Recorded evaluation configuration: {"Learning Strategy":"Satellite Model"}. This record does not assert independent reproduction.

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