README.md

---
pretty_name: NAIP 16-Day City Cubes (materialized tiles)
tags:
- geospatial
- remote-sensing
- earth-observation
- wildfire
- segmentation
task_categories:
- image-segmentation
- image-classification
license: cc-by-4.0
---

# NAIP 16-Day City Cubes (materialized tiles)

![Panel preview](assets/panels/santa_maria_3x3.png)

Each row is a 512×512 chip with **16 layers** (composites, single-band indices, and masks).

### What’s included (no pseudoRGB)
- **RGB composites**: `naip_rgb`, `s2_rgb`, `dem_rgb`
- **Mono S2 layers** (published as single-channel images): `s2_B08`, `s2_MSAVI`, `s2_NDVI`, `s2_NDWI`, `s2_SCL`
- **Other monos**: `naip_ndvi`
- **Semantic masks**: `labels` (task labels), `landfire_family`, `cdl`
- **Metadata**: `tile_id`, `city`, `bbox` (`west,south,east,north`), `chip_px`, `split`, `meta_json`

> **Note:** The dataset intentionally omits `s2_pseudo_rgb`. If you need alternate composites,
> you can compose them on the fly from the published mono layers.

## Loading
```python
from datasets import load_dataset
ds = load_dataset("gdurkin/naip-16d-city-cubes", split="train")
ds[0].keys()
```

To load a single city’s parquet shards directly:
```python
from datasets import load_dataset
city = "Arcadia__California__USA"
pattern = f"hf://datasets/gdurkin/naip-16d-city-cubes/data/city={city}/*.parquet"
ds_city = load_dataset("parquet", data_files=pattern, split="train")
```

## Columns & shapes
- `naip_rgb` _(512×512×3 u8)_: NAIP true color
- `s2_rgb` _(512×512×3 u8)_: Sentinel-2 true color (B04,B03,B02)
- `dem_rgb` _(512×512×3 u8)_: elevation/gradient/aspect visualization
- `naip_ndvi` _(512×512 u8)_: NDVI from NAIP
- `s2_*` _(512×512 u8)_: mono S2 layers (`B08`, `MSAVI`, `NDVI`, `NDWI`, `SCL`)
- `labels`, `landfire_family`, `cdl` _(512×512 u8)_: categorical masks

## Legends

### Labels (task)
| id | name | color |
|--:|:-----|:------|
| 0 | `background` | `#000000` |
| 1 | `road_concrete` | `#C8C8C8` |
| 2 | `pavement` | `#A0A0A0` |
| 3 | `dirt_gravel` | `#96643C` |
| 4 | `grass_dry` | `#E6DC78` |
| 5 | `grass_healthy` | `#50C878` |
| 6 | `vegetation` | `#147814` |
| 7 | `building` | `#FF5050` |
| 8 | `water` | `#4682B4` |
| 11 | `building_res` | `#FF8C8C` |
| 12 | `building_com` | `#FF5A1E` |

### LANDFIRE family
| id | family | color |
|--:|:-------|:------|
| 0 | `background` | `#000000` |
| 1 | `grass` | `#F7E68C` |
| 2 | `shrub` | `#C69842` |
| 3 | `timber` | `#1E781E` |
| 4 | `slash` | `#CE5A32` |
| 9 | `urban` | `#969696` |
| 10 | `snow_ice` | `#C8E6FF` |
| 11 | `agriculture` | `#FFCC66` |
| 12 | `water` | `#4682B4` |
| 13 | `barren` | `#C2B280` |

### CDL (full palette)
The table below lists CDL codes and colors (urban classes are overridden for better visual separation).

<details><summary><strong>Click to expand full CDL legend</strong></summary>

| id | class | color |
|--:|:------|:------|
| 0 | `Background` | `000000` |
| 1 | `Corn` | `ffd400` |
| 2 | `Cotton` | `ff2626` |
| 3 | `Rice` | `00a9e6` |
| 4 | `Sorghum` | `ff9e0f` |
| 5 | `Soybeans` | `267300` |
| 6 | `Sunflower` | `ffff00` |
| 10 | `Peanuts` | `70a800` |
| 11 | `Tobacco` | `00af4d` |
| 12 | `Sweet Corn` | `e0a60f` |
| 13 | `Pop or Orn Corn` | `e0a60f` |
| 14 | `Mint` | `80d4ff` |
| 21 | `Barley` | `e2007f` |
| 22 | `Durum Wheat` | `8a6453` |
| 23 | `Spring Wheat` | `d9b56c` |
| 24 | `Winter Wheat` | `a87000` |
| 25 | `Other Small Grains` | `d69dbc` |
| 26 | `Dbl Crop WinWht/Soybeans` | `737300` |
| 27 | `Rye` | `ae017e` |
| 28 | `Oats` | `a15889` |
| 29 | `Millet` | `73004c` |
| 30 | `Speltz` | `d69dbc` |
| 31 | `Canola` | `d1ff00` |
| 32 | `Flaxseed` | `8099ff` |
| 33 | `Safflower` | `d6d600` |
| 34 | `Rape Seed` | `d1ff00` |
| 35 | `Mustard` | `00af4d` |
| 36 | `Alfalfa` | `ffa8e3` |
| 37 | `Other Hay/Non Alfalfa` | `a5f58d` |
| 38 | `Camelina` | `00af4d` |
| 39 | `Buckwheat` | `d69dbc` |
| 41 | `Sugarbeets` | `a900e6` |
| 42 | `Dry Beans` | `a80000` |
| 43 | `Potatoes` | `732600` |
| 44 | `Other Crops` | `00af4d` |
| 45 | `Sugarcane` | `b380ff` |
| 46 | `Sweet Potatoes` | `732600` |
| 47 | `Misc Vegs & Fruits` | `ff6666` |
| 48 | `Watermelons` | `ff6666` |
| 49 | `Onions` | `ffcc66` |
| 50 | `Cucumbers` | `ff6666` |
| 51 | `Chick Peas` | `00af4d` |
| 52 | `Lentils` | `00deb0` |
| 53 | `Peas` | `55ff00` |
| 54 | `Tomatoes` | `f5a27a` |
| 55 | `Caneberries` | `ff6666` |
| 56 | `Hops` | `00af4d` |
| 57 | `Herbs` | `80d4ff` |
| 58 | `Clover/Wildflowers` | `e8beff` |
| 59 | `Sod/Grass Seed` | `b2ffde` |
| 60 | `Switchgrass` | `00af4d` |
| 61 | `Fallow/Idle Cropland` | `bfbf7a` |
| 63 | `Forest` | `95ce93` |
| 64 | `Shrubland` | `c7d79e` |
| 65 | `Barren` | `ccbfa3` |
| 66 | `Cherries` | `ff00ff` |
| 67 | `Peaches` | `ff91ab` |
| 68 | `Apples` | `b90050` |
| 69 | `Grapes` | `704489` |
| 70 | `Christmas Trees` | `007878` |
| 71 | `Other Tree Crops` | `b39c70` |
| 72 | `Citrus` | `ffff80` |
| 74 | `Pecans` | `b6705c` |
| 75 | `Almonds` | `00a884` |
| 76 | `Walnuts` | `ebd6b0` |
| 77 | `Pears` | `b39c70` |
| 81 | `Clouds/No Data` | `f7f7f7` |
| 82 | `Developed` | `9c9c9c` |
| 83 | `Water` | `4d70a3` |
| 87 | `Wetlands` | `80b3b3` |
| 88 | `Nonag/Undefined` | `e9ffbe` |
| 92 | `Aquaculture` | `00ffff` |
| 111 | `Open Water` | `4d70a3` |
| 112 | `Perennial Ice/Snow` | `d4e3fc` |
| 121 | `Developed/Open Space` | `#BDBDBD` |
| 122 | `Developed/Low Intensity` | `#F4B183` |
| 123 | `Developed/Med Intensity` | `#E06666` |
| 124 | `Developed/High Intensity` | `#A61C00` |
| 131 | `Barren` | `ccbfa3` |
| 141 | `Deciduous Forest` | `95ce93` |
| 142 | `Evergreen Forest` | `95ce93` |
| 143 | `Mixed Forest` | `95ce93` |
| 152 | `Shrubland` | `c7d79e` |
| 176 | `Grass/Pasture` | `e9ffbe` |
| 190 | `Woody Wetlands` | `80b3b3` |
| 195 | `Herbaceous Wetlands` | `80b3b3` |
| 204 | `Pistachios` | `00ff8c` |
| 205 | `Triticale` | `d69dbc` |
| 206 | `Carrots` | `ff6666` |
| 207 | `Asparagus` | `ff6666` |
| 208 | `Garlic` | `ff6666` |
| 209 | `Cantaloupes` | `ff6666` |
| 210 | `Prunes` | `ff91ab` |
| 211 | `Olives` | `344a34` |
| 212 | `Oranges` | `e67525` |
| 213 | `Honeydew Melons` | `ff6666` |
| 214 | `Broccoli` | `ff6666` |
| 215 | `Avocados` | `66994d` |
| 216 | `Peppers` | `ff6666` |
| 217 | `Pomegranates` | `b39c70` |
| 218 | `Nectarines` | `ff91ab` |
| 219 | `Greens` | `ff6666` |
| 220 | `Plums` | `ff91ab` |
| 221 | `Strawberries` | `ff6666` |
| 222 | `Squash` | `ff6666` |
| 223 | `Apricots` | `ff91ab` |
| 224 | `Vetch` | `00af4d` |
| 225 | `Dbl Crop WinWht/Corn` | `ffd400` |
| 226 | `Dbl Crop Oats/Corn` | `ffd400` |
| 227 | `Lettuce` | `ff6666` |
| 228 | `Dbl Crop Triticale/Corn` | `ffd400` |
| 229 | `Pumpkins` | `ff6666` |
| 230 | `Dbl Crop Lettuce/Durum Wht` | `8a6453` |
| 231 | `Dbl Crop Lettuce/Cantaloupe` | `ff6666` |
| 232 | `Dbl Crop Lettuce/Cotton` | `ff2626` |
| 233 | `Dbl Crop Lettuce/Barley` | `e2007f` |
| 234 | `Dbl Crop Durum Wht/Sorghum` | `ff9e0f` |
| 235 | `Dbl Crop Barley/Sorghum` | `ff9e0f` |
| 236 | `Dbl Crop WinWht/Sorghum` | `a87000` |
| 237 | `Dbl Crop Barley/Corn` | `ffd400` |
| 238 | `Dbl Crop WinWht/Cotton` | `a87000` |
| 239 | `Dbl Crop Soybeans/Cotton` | `267300` |
| 240 | `Dbl Crop Soybeans/Oats` | `267300` |
| 241 | `Dbl Crop Corn/Soybeans` | `ffd400` |
| 242 | `Blueberries` | `000099` |
| 243 | `Cabbage` | `ff6666` |
| 244 | `Cauliflower` | `ff6666` |
| 245 | `Celery` | `ff6666` |
| 246 | `Radishes` | `ff6666` |
| 247 | `Turnips` | `ff6666` |
| 248 | `Eggplants` | `ff6666` |
| 249 | `Gourds` | `ff6666` |
| 250 | `Cranberries` | `ff6666` |
| 254 | `Dbl Crop Barley/Soybeans` | `267300` |
</details>


## Coverage
<!-- COVERAGE-TABLE:BEGIN -->

**Coverage**
- rows: **24,792**
- S2 mono policy: `?`

| column | rows | pct |
|---|---:|---:|
| `cdl` | 24,792 | 100.0% |
| `dem_rgb` | 24,792 | 100.0% |
| `labels` | 24,792 | 100.0% |
| `landfire_family` | 24,792 | 100.0% |
| `naip_ndvi` | 24,792 | 100.0% |
| `naip_rgb` | 24,792 | 100.0% |
| `s2_B08` | 24,792 | 100.0% |
| `s2_MSAVI` | 24,792 | 100.0% |
| `s2_NDVI` | 24,792 | 100.0% |
| `s2_NDWI` | 24,792 | 100.0% |
| `s2_SCL` | 24,792 | 100.0% |
| `s2_rgb` | 24,792 | 100.0% |

<!-- COVERAGE-TABLE:END -->