Datasets:

gdurkin
/

naip-16d-city-cubes

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)

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

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:

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).

Click to expand full CDL legend

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`

Coverage

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%