---
pretty_name: AID scene classification evaluation results
language:
- en
license: unknown
task_categories:
- image-classification
size_categories:
- 10K<n<100K
tags:
- evaluation
- remote-sensing
- aerial-imagery
- robustness
- predictions
configs:
- config_name: class_definitions
  default: true
  data_files:
  - split: test
    path: data/class_definitions.parquet
- config_name: native_baseline
  data_files:
  - split: test
    path: data/native_baseline.parquet
- config_name: controlled_definitions
  data_files:
  - split: test
    path: data/controlled_definitions.parquet
---

# AID scene classification evaluation results

30,500 saved prediction records from five models evaluated on AID aerial scene classification. This release contains numeric predictions, reference-label identifiers, source references, hashes, prompts, protocols, and recomputed accuracy counts. **It contains no AID image bytes, thumbnails, contact sheets, or transformed images.**

The original images could not be cleared for redistribution. The upstream Hugging Face repositories declare an unknown license, and the original dataset consists of Google Earth imagery. Public availability of a mirror does not establish redistribution permission. See [RIGHTS.md](RIGHTS.md) for the checked sources and release boundary. This repository does not grant a license to upstream imagery or annotations, or claim that the original benchmark is openly licensed. The metadata remains `license: unknown`; no blanket open license is asserted.

## Included studies

| Configuration | Predictions | Original images | Conditions |
| --- | ---: | ---: | --- |
| `native_baseline` | 10,000 | 2,000 | Historical short-prompt run |
| `class_definitions` | 10,000 | The same 2,000 | Later run with class definitions |
| `controlled_definitions` | 10,500 | 300 of those 2,000 | Fresh control plus six transformations |

Each study includes Luna, Jev, Clef, D1, and D1 Ultra, with one saved final prediction per case and model. Requested and served model identifiers are retained as recorded. These are historical experiment identifiers, not a claim of present public model availability. The five-model rows are correlated measurements, not 30,500 independent images.

The release covers the completed native baseline, class-definition follow-up, and definitions-matched robustness studies. Earlier duplicate reruns, pilot runs, the older robustness study without definitions, specialist checkpoints, paired-sharpness tasks, Wikimedia images, travel photographs, and the non-blind shared-error review are outside this release.

## Load

```python
from datasets import load_dataset

predictions = load_dataset(
    "BraintrustDataDev/aid-evaluation-results",
    "class_definitions",
    split="test",
)
```

Use either other configuration name to load that study. The `test` designation is inherited from a fixed community partition; it is not an independent train/test split of the prediction table. Keep every model, prompt condition, and image transformation for a source together when resampling or dividing these records.

## Files and fields

- `data/*.parquet`: typed prediction tables used by the dataset loader. Matching `data/*.jsonl` files contain the same allowlisted records; raw provider responses and internal attachment records are omitted.
- `references/test_images.jsonl`: 2,000 image identifiers, upstream class references, byte hashes, dimensions, and pinned upstream paths. No images are downloaded by loading this release.
- `prompts/*.txt`: exact instructions used for each study.
- `protocols/*.json`: label order and frozen study protocols; local absolute paths are replaced with basenames. Preparation-time status fields describe the frozen plan; `release_summary.json` records the completed exports.
- `metrics.json`: independently recomputed counts and accuracy by study/model/condition. Invalid outputs count as incorrect.
- `release_summary.json`: exact release counts, source prediction/manifest checksums, and verification results for all 15 runs.
- `SHA256SUMS.txt`: checksums of the published release files, excluding itself.

Join records on `original_id` for the source image and on `model` for paired model comparisons. `case_id` identifies the actual original or transformed input. `condition` is `original` for native runs, and one of `control`, `blur_1`, `blur_2`, `contrast_0.5`, `contrast_1.5`, `shift_left_60`, or `shift_right_60` for robustness.

`reference_label` records the upstream category, not a new adjudication. `predicted_label`, `status`, `valid`, `correct`, and the 30-label `probabilities` describe the parsed result. Invalid rows retain their status; probabilities are null. `input_sha256` hashes exact submitted image bytes; `original_sha256` hashes the original source bytes. `prompt_sha256` hashes exact instruction text. `dataset_version` identifies the frozen Braintrust dataset snapshot.

`latency_ms` and `attempts` are historical runner measurements. Token counts and costs may be null when unavailable. `provider_reported_cost_usd` is read from numeric provider `usage.cost`; `recorded_cost_usd`, `token_estimated_cost_usd`, and `cost_source` retain the saved accounting fields. `legacy_estimated_api_cost_usd` preserves the older field without relabeling it as a billed amount. Do not sum multiple cost columns. Costs are historical and may omit unreported failed-attempt charges; they are not current price quotations or invoices. Missing fields are not zeros.

## Results and interpretation

| Model | Native baseline accuracy | Class-definition accuracy |
| --- | ---: | ---: |
| Luna | 74.30% | 85.55% |
| Jev | 78.95% | 86.15% |
| Clef | 78.05% | 83.85% |
| D1 | 77.25% | 84.30% |
| D1 Ultra | 78.95% | 85.85% |

The class-definition study was designed after inspecting baseline errors. Some definitions are working interpretations rather than official AID rules. Comparison with a historical baseline cannot isolate prompt effects from intervening provider or model changes. There is one trial per case; these records do not establish run-to-run variability or an unbiased confirmatory benchmark ranking.

The robustness study uses ten images per class, selected by the lowest SHA256 of `aid-controlled-20261022-v1` concatenated with the filename. All seven conditions use the same definitions prompt. Controls and transformed images are lossless 600×600 RGB PNGs. Blur uses Pillow GaussianBlur radii 1 and 2; contrast uses ImageEnhance.Contrast factors 0.5 and 1.5; horizontal shifts use 60-pixel reflected padding and remove 60 original boundary pixels. Exact recipes are documented in the frozen protocol. The original source bytes and resulting image hashes are separate because PNG re-encoding changes bytes. Pixel-equivalent re-encoding with another library version need not produce identical file hashes.

Compare robustness conditions against the fresh `control` rows for the same source and model. Full-test accuracy and historical native predictions are not valid controls for these transformations. The balanced 300-image subset has a different class distribution from the full test split. For statistical analysis, cluster by original image and preserve paired conditions; confidence intervals from these rows alone do not measure repeated-call variation. Reference categories can be ambiguous and are not corrected by model consensus.

## Provenance

Original benchmark: [AID, Xia et al.](https://captain-whu.github.io/AID/), 10,000 images and 30 scene categories. Source image references use [`blanchon/AID`](https://huggingface.co/datasets/blanchon/AID/tree/d9b5531544523f29d365a77a007b2ace3cea6882), revision `d9b5531544523f29d365a77a007b2ace3cea6882`. The 6,000/2,000/2,000 community train/validation/test partition comes from [`isaaccorley/aid`](https://huggingface.co/datasets/isaaccorley/aid/tree/e6767964e40f567a18eac6e6ca5fce397e4ce411), revision `e6767964e40f567a18eac6e6ca5fce397e4ce411`. This is not the original paper's repeated random-split benchmark protocol.

Evaluation project: `aid-single-label-comparison`. Native run versions: `1000198073523310044`; controlled run version: `1000198078684663546`. All prediction IDs, input hashes, row counts, valid counts, and aggregate accuracies were checked against the frozen local manifests and saved summaries before publication. This verification does not independently validate the upstream category judgments.

Please cite the original dataset when discussing results:

```bibtex
@article{xia2017aid,
  title={AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification},
  author={Xia, Gui-Song and Hu, Jingwen and Hu, Fan and Shi, Baoguang and Bai, Xiang and Zhong, Yanfei and Zhang, Liangpei and Lu, Xiaoqiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={55}, number={7}, pages={3965--3981}, year={2017}
}
```