Datasets:
Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
License:
| license: cc-by-4.0 | |
| task_categories: | |
| - image-to-image | |
| language: | |
| - en | |
| tags: | |
| - shadow-removal | |
| - shadow-transfer | |
| - shadow-generation | |
| - benchmark | |
| - computer-vision | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: ShadowTransfer | |
| # ShadowTransfer | |
| A benchmark for measuring **geographic transfer** in overhead shadow detection. 4,500 human-verified shadow masks across three U.S. cities (Chicago, Miami, Phoenix) at two native NAIP resolutions (0.3 m/px, 0.6 m/px), released in two complementary forms: | |
| - **`data_cities/`** — raw per-city dataset organized by city, resolution, and split. Use this when you need full control over splits or want to construct your own protocols. | |
| - **`data_loco/`** — pre-built leave-one-city-out (LOCO) folds derived from `data_cities/`. Use this when you want to reproduce the paper's transfer evaluation, or when comparing a new method against the reported baselines. | |
| > Both directories contain the **same underlying images and masks**. `data_loco/` is a re-organization of `data_cities/` into the LOCO protocol with frozen, paper-matched train/val/test counts. Pick whichever matches your workflow. | |
| --- | |
| ## Quick start | |
| ```bash | |
| # Hosted at: | |
| # https://huggingface.co/datasets/shadow-transfer-bench/ShadowTransfer | |
| from huggingface_hub import snapshot_download | |
| snapshot_download(repo_id="shadow-transfer-bench/ShadowTransfer", | |
| repo_type="dataset", local_dir="ShadowTransfer") | |
| ``` | |
| To reproduce the paper's LOCO numbers, point any segmentation training pipeline at one fold: | |
| ``` | |
| ShadowTransfer/data_loco/fold_0_holdout_phoenix/highres/ | |
| train/images/ train/masks/ | |
| val/images/ val/masks/ | |
| test/images/ test/masks/ | |
| ``` | |
| That's it — `train/`, `val/`, and `test/` already contain the 450 / 150 / 150 images the paper uses. | |
| --- | |
| ## Schema | |
| ### `data_cities/` — per-city raw dataset | |
| ``` | |
| data_cities/ | |
| ├── chicago/ | |
| │ ├── highres/ # 0.3 m/px native NAIP | |
| │ │ ├── train/ | |
| │ │ │ ├── images/ # 450 RGB .png, 384×384 | |
| │ │ │ ├── masks/ # 450 binary .png (0 / 255) | |
| │ │ │ └── masks_multiclass/ # optional, 0–6 class IDs (see below) | |
| │ │ ├── val/ | |
| │ │ │ ├── images/ # 150 .png | |
| │ │ │ ├── masks/ # 150 .png | |
| │ │ │ └── masks_multiclass/ | |
| │ │ ├── test/ | |
| │ │ │ ├── images/ # 150 .png | |
| │ │ │ ├── masks/ # 150 .png | |
| │ │ │ └── masks_multiclass/ | |
| │ │ ├── metadata_train.json | |
| │ │ ├── metadata_val.json | |
| │ │ └── metadata_test.json | |
| │ └── midres/ # 0.6 m/px native NAIP, same layout | |
| ├── miami/ # same layout | |
| └── phoenix/ # same layout | |
| ``` | |
| **Counts (per city, per resolution):** 450 train + 150 val + 150 test = **750 images**. | |
| **Total:** 3 cities × 2 resolutions × 750 = **4,500 images**. | |
| **File formats** | |
| | Path | Type | Encoding | | |
| | --- | --- | --- | | |
| | `images/*.png` | RGB image | 8-bit, 3 channels, 384×384 | | |
| | `masks/*.png` | binary shadow mask | 8-bit, 1 channel, `{0, 255}` (255 = shadow) | | |
| | `masks_multiclass/*.png` | multiclass mask | 8-bit, 1 channel, integer class IDs `0–6` | | |
| **Multiclass IDs** (used in `masks_multiclass/`): | |
| | ID | Class | | |
| | --- | --- | | |
| | 0 | Background (no shadow) | | |
| | 1 | Building / canyon shadow | | |
| | 2 | Under-structure shadow | | |
| | 3 | Tree-canopy dapple | | |
| | 4 | Topography-cast shadow | | |
| | 5 | Vehicle-cast shadow | | |
| | 6 | Thin-linear shadow | | |
| The benchmark in the paper evaluates on binary masks only; the multiclass masks are released for downstream analysis. Image and mask filenames match within a split (`images/foo.png` ↔ `masks/foo.png`). | |
| **`metadata_{split}.json`** — one JSON list per split, one entry per image: | |
| ```jsonc | |
| { | |
| "original_filename": "phoenix_session01_highres_paired_010.png", | |
| "random_filename": "img_005.png", // anonymized name on disk | |
| "city": "phoenix", | |
| "resolution": "highres", // "highres" (0.3 m) | "midres" (0.6 m) | |
| "split": "test", // "train" | "val" | "test" | |
| "type": "type2", // sampling scheme tag | |
| "image_type": "paired", // "paired" if also in the other resolution | |
| "pair_id": "010", // links a paired pair across resolutions | |
| "center_lon": -112.17278007840696, | |
| "center_lat": 33.443872697021, | |
| "tile_name": "m_3311239_ne_12_030_20230917", // source NAIP tile | |
| "source_session": 1, | |
| "annotation_session": 31, | |
| "session_num": 31, | |
| "has_annotations": true, | |
| "shadow_types": ["Building/canyon shadow", | |
| "Vehicle-cast shadow", | |
| "Tree-canopy dapple"] | |
| } | |
| ``` | |
| The on-disk filename is `random_filename`. `original_filename` is the human-readable name. `pair_id` lets you join the 0.3 m/px and 0.6 m/px patches that share ground coordinates (300 paired patches per city — see paper §3.1). | |
| --- | |
| ### `data_loco/` — pre-built LOCO folds | |
| Three folds, one per held-out city. Each fold contains the same train / val / test directory layout as the per-city dataset, plus a `manifest.json` and per-split metadata. | |
| ``` | |
| data_loco/ | |
| ├── fold_0_holdout_phoenix/ # train: chicago + miami, test: phoenix | |
| │ ├── highres/ # 0.3 m/px | |
| │ │ ├── manifest.json # provenance + counts | |
| │ │ ├── metadata_train.json | |
| │ │ ├── metadata_val.json | |
| │ │ ├── metadata_test.json | |
| │ │ ├── train/ | |
| │ │ │ ├── images/ # 450 .png (225 chicago + 225 miami) | |
| │ │ │ ├── masks/ # 450 .png | |
| │ │ │ └── masks_multiclass/ # where present upstream | |
| │ │ ├── val/ | |
| │ │ │ ├── images/ # 150 .png (75 chicago + 75 miami) | |
| │ │ │ ├── masks/ # 150 .png | |
| │ │ │ └── masks_multiclass/ | |
| │ │ └── test/ | |
| │ │ ├── images/ # 150 .png (full phoenix test pool) | |
| │ │ ├── masks/ # 150 .png | |
| │ │ └── masks_multiclass/ | |
| │ └── midres/ # 0.6 m/px, same layout | |
| ├── fold_1_holdout_miami/ # train: chicago + phoenix, test: miami | |
| └── fold_2_holdout_chicago/ # train: miami + phoenix, test: chicago | |
| ``` | |
| **Filename convention.** In `train/` and `val/`, files are renamed `{source_city}__{original}.png` (e.g. `chicago__img_017.png`) so the two source cities cannot collide and provenance is visible at a glance. In `test/` files keep their original names because they come from a single source city. Image and mask filenames remain matched within a split. | |
| **`metadata_{split}.json`** — same fields as the per-city metadata, plus LOCO context: | |
| ```jsonc | |
| { | |
| // ... all per-city fields preserved as-is, plus: | |
| "loco_filename": "chicago__img_017.png", | |
| "loco_split": "train", // "train" | "val" | "test" in this fold | |
| "loco_fold_id": 0, | |
| "loco_holdout_city": "phoenix", | |
| "loco_resolution": "highres", | |
| "source_city": "chicago", | |
| "source_split": "train", // which per-city split it came from | |
| "has_masks_multiclass": true | |
| } | |
| ``` | |
| **`manifest.json`** records the build parameters, per-city counts, and the full file list — enough to re-derive the fold from `data_cities/` exactly. | |
| **Counts per fold per resolution:** 450 train (225 per training city) + 150 val (75 per training city) + 150 test (held-out city's full test pool). | |
| --- | |
| ## Intended use | |
| - **Primary use**: benchmarking shadow detection methods on overhead aerial imagery, with explicit measurement of geographic transfer (`data_loco/`) or of in-domain performance per city (`data_cities/`). | |
| - **Secondary uses**: building footprint and façade extraction (binary masks act as occlusion priors); shadow-removal and de-shadowing research; domain generalization research on dense prediction tasks; pretraining for related overhead segmentation tasks; analysis of urban morphology and solar geometry from the included `center_lat` / `center_lon` and shadow type labels. | |
| For ML transfer evaluation specifically, **report results on `data_loco/` using all three folds and both resolutions** (six test cells per method) and use the within-city upper-bound numbers from the paper as the comparison baseline. Per-cell paired bootstrap is recommended for significance — see the paper for the exact protocol. | |
| --- | |
| ## Known limitations | |
| - **Three U.S. cities only.** Chicago, Miami, and Phoenix span distinct climates and morphologies but share North American grid-pattern urbanism. Generalization to dense historic European cities, informal settlements, or non-grid morphologies (e.g. Mumbai, Cairo, Marrakech) is untested. | |
| - **NAIP RGB only.** No multispectral or near-infrared bands. Sensor characteristics, color processing, and acquisition conventions are NAIP-specific. | |
| - **Single fall season.** All imagery comes from a single seasonal window; deciduous-canopy bare-vs-leaf-on variation is not represented. | |
| - **Native-resolution releases only.** The 0.3 m and 0.6 m subsets come from separate native NAIP acquisitions, not from downsampling the same source. Do not synthesize one from the other if your goal is to study resolution transfer. | |
| - **Boundary uncertainty.** Shadow edges are inherently soft; we recommend tolerant-mIoU evaluation with a ±2 px don't-care band (see paper §3.3). Strict pixel-exact metrics will systematically penalize all methods at the boundary. | |
| - **Multiclass coverage.** `masks_multiclass/` is provided where reliable typing was possible; sparse classes (vehicle-cast, thin-linear) have low per-image counts and are not recommended as primary evaluation targets. | |
| - **Annotation noise.** Even with three-phase QC and inter-annotator-agreement monitoring, a small residual disagreement rate (≈3% of segments adjudicated as borderline) is expected. | |
| --- | |
| ## License and attribution | |
| - **Source imagery (NAIP).** USDA Farm Service Agency National Agriculture Imagery Program. NAIP imagery acquired through 2019 is in the U.S. public domain; later releases are published as public-domain-with-attribution by USDA-FSA APFO. Users of the imagery in derived products are asked to credit the USDA Farm Service Agency Aerial Photography Field Office (APFO). | |
| - **Annotations and metadata.** The shadow masks (`masks/`, `masks_multiclass/`) and metadata files (`metadata_*.json`, `manifest.json`) are released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. | |
| - **Required citation when using the dataset.** | |
| ``` | |
| ShadowTransfer authors. ShadowTransfer: A Geographic Transfer Benchmark | |
| for Overhead Shadow Detection. NeurIPS 2026 Datasets & Benchmarks Track. | |
| ``` | |
| Please also cite USDA-FSA NAIP for the underlying imagery. | |
| --- | |
| ## Hosting and DOI | |
| - **Primary host**: <https://huggingface.co/datasets/shadow-transfer-bench/ShadowTransfer> | |
| - **DOI**: assigned via the Hugging Face dataset record (visible on the dataset card). | |
| - **Mirror / archival copy**: see the dataset card for the latest mirror list. | |
| --- | |
| ## Documentation | |
| Two structured-documentation artifacts accompany the release: | |
| - **`DATASHEET.md`** — a Datasheet for Datasets in the format of Gebru et al. (2021), covering motivation, composition, collection, preprocessing, uses, distribution, and maintenance. | |
| - **Croissant metadata** — machine-readable dataset description in the [MLCommons Croissant](https://mlcommons.org/working-groups/data/croissant/) format. Hugging Face auto-generates and serves this for every Hub dataset; fetch it at: | |
| ``` | |
| https://huggingface.co/api/datasets/shadow-transfer-bench/ShadowTransfer/croissant | |
| ``` | |
| This file is consumable by `mlcroissant`, TFDS, and any Croissant-aware loader. | |
| --- | |
| ## Maintenance | |
| Issues, errata, and corrections: file an issue on the Hugging Face dataset page or open a pull request on the accompanying GitHub repository linked from the dataset card. Versioned releases are tagged on Hugging Face; the version used for the published paper results is tagged `v1.0`. | |
| For questions about the LOCO protocol or the diagnostic framework, see the paper. For questions about the annotation pipeline, see Appendix A of the paper. | |