General Bathymetric Chart of the Oceans

GEBCO (General Bathymetric Chart of the Oceans) is a global terrain model providing elevation and bathymetry data in metres on a 15 arc-second interval grid, covering both ocean and land. The dataset includes a Type Identifier (TID) Grid indicating source data types, and incorporates under-ice topography for Greenland and Antarctica. Data are distributed as Cloud Optimised GeoTIFFs for annual releases.

bathymetry oceanography elevation ocean topography seafloor global-coverage

General Bathymetric Chart of the Oceans (GEBCO)

Dataset files (3 x .tif)

Source Cooperative product page

## README.md
# General Bathymetric Chart of the Oceans Data

Data originall accessed from the
[GEBCO website](https://www.gebco.net/data_and_products/gridded_bathymetry_data/).

GEBCO's gridded bathymetric data set, is a global terrain model for ocean and land, providing elevation data, in meters, on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2024 Grid is based on.

This release includes a version of the grid with under-ice topography/bathymetry information for Greenland and Antarctica.

Find out information about [terms of use and attribution here](https://www.gebco.net/data_and_products/gridded_bathymetry_data/#a1).

Documentation on [data preparation is accessible here](https://github.com/AustralianAntarcticDivision/gebco).

## Access data

### Stream data

The cloud optimised geotiff can be used directly in QGIS or
other applications using this URL:
`https://data.source.coop/ausantarctic/gebco/GEBCO_<year>.tif`.

### Example to load as Xarray

```python
from odc.stac import load
from pystac import Item

# Bounding box around Tasmania
bbox = [140.0, -45.0, 150.0, -35.0]

# Get the item as a PySTAC Item
item = Item.from_file(
    "https://data.source.coop/ausantarctic/gebco/GEBCO_2024.stac-item.json"
)

# Load using odc.stac.load
data = load(
    [item],
    bbox=bbox,
    chunks={"latitude": 1024, "longitude": 1024},
)

# Plot an interactive map
data.elevation.odc.explore(vmin=-3000, vmax=3000, cmap="terrain")
```

### Downloading

While the idea of cloud native data formats is to not download, you can
download this data using the following URLs:

* 2024: `https://data.source.coop/ausantarctic/gebco/GEBCO_2024.tif`
* 2025: `https://data.source.coop/ausantarctic/gebco/GEBCO_2025.tif`
* 2026: `https://data.source.coop/ausantarctic/gebco/GEBCO_2026.tif`

providers: role [processor] for 'Australian Antarctic Division' is an AI guess — Hosts the data preparation pipeline repo (github.com/AustralianAntarcticDivision/gebco); assumed processor role based on that.. Verify before flipping to _state: reviewed.


GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)

GHRSST Level 4 MUR (Multi-scale Ultra-high Resolution) Global Foundation Sea Surface Temperature Analysis (v4.1) is a daily, 1-kilometer-resolution global ocean surface temperature dataset produced by NASA's Jet Propulsion Laboratory in collaboration with GHRSST. It fuses observations from multiple satellite-borne sensors (infrared and microwave radiometers) and in situ measurements to derive foundation SST values free of diurnal warming effects. Distributed as Cloud-Optimized GeoTIFFs with STAC metadata, it supports climate research, numerical weather prediction, and marine resource management.

oceanography remote-sensing climate weather-forecasting weather numerical-weather-prediction cloud-optimized-geotiff time-series sea-surface-temperature

NASA Jet Propulsion Laboratory (JPL) / PO.DAAC

Dataset files (37640 x .tif)

Source Cooperative product page

## README.md
# GHRSST Sea Surface Temperature Analysis

The GHRSST Level 4 MUR (Multi-scale Ultra-high Resolution) Global Foundation Sea Surface Temperature Analysis (v4.1) provides
a high-resolution, daily global assessment of ocean surface temperature. This dataset fuses observations from multiple
satellite-borne sensors (including infrared and microwave radiometers) and in situ measurements, offering a robust and
consistent picture of sea surface temperature conditions. The algorithm employs advanced interpolation and blending
techniques to fill gaps caused by cloud cover and sensor discrepancies, resulting in a seamless, 1-kilometer-resolution
global field. The “foundation” temperature represents the sea surface temperature free of diurnal warming effects,
making it particularly valuable for climate studies, numerical weather prediction, and a variety of marine applications.
The dataset is distributed by NASA’s Jet Propulsion Laboratory in collaboration with the Group for High Resolution Sea Surface
Temperature (GHRSST), and is widely used by researchers, operational agencies, and policy-makers for informed decision-making
in fields such as marine resource management, weather forecasting, and climate monitoring.

For more information, please refer to the [documentation](https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1).

## About this repository

The [code used to repackage this data is accessible here](https://github.com/auspatious/ghrsst-cogger).

Care has been taken to not change the source data, but just to repackage it into cloud optimised geotiffs with
STAC metadata. Each daily dataset is accessible in a `<year>/<month>/<day>` path, and file names are consistent with the
source data, e.g., `"{$Y%m%d}090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.stac-item.json`.

## Accessing data

### Using Python

To load a single day, you can do something like the following using Python:

```python
from pystac import Item
from odc.stac import load

path = "https://data.source.coop/ausantarctic/ghrsst-mur-v2/2025/02/09/20250209090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.stac-item.json"
item = Item.from_file(path)

data = load([item], chunks={}, anchor="center")
data
```

And then to plot it, do:

```python
sst_loaded = data["analysed_sst"].squeeze().compute()
sst_masked = sst_loaded.where(sst_loaded != -32768)

# Extract the scale and offset from the metadata
meta = item.assets["analysed_sst"].extra_fields["raster:bands"][0]
scale = meta["scale"]
offset = meta["offset"]
k_to_c = -273.15

sst_scaled = sst_masked * scale + offset + k_to_c
sst_scaled.plot.imshow(size=8, robust=True, cmap="inferno")
```

### Reading from STAC Parquet file

Alternately, you can use the STAC Parquet file as an index to all the STAC docs.

```python
import stacrs
import pystac
from odc.stac import load

url = "https://data.source.coop/ausantarctic/ghrsst-mur-v2/ghrsst-mur-v2.parquet"

center = 13, -61
buffer = 5

bbox = (
    center[1] - buffer,
    center[0] - buffer,
    center[1] + buffer,
    center[0] + buffer,
)

items = await stacrs.read(url)

# Or use .search to filter by time
# year = 2024
# items = stacrs.search(
#     url,
#     bbox=bbox,
#     datetime=f"{year}-01-01T00:00:00.000Z/{year}-12-31T23:59:59.999Z",
# )

items = [pystac.Item.from_dict(i) for i in items["features"]]

data = load(items, bbox=bbox, chunks={})
data
```

### Using R

The 'analysed_sst' files are templated by:

``` r
template <- "https://data.source.coop/ausantarctic/ghrsst-mur-v2/{format(date, '%Y/%m/%d')}/{format(date, '%Y%m%d')}090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1_analysed_sst.tif"
```

Example for a specific date:

``` r
date <- as.Date("2025-02-14")
(url <- glue::glue(template))
```

Strictly, we should check the catalogue first:

``` r
catalog <- arrow::read_parquet("https://data.source.coop/ausantarctic/ghrsst-mur-v2/ghrsst-mur-v2.parquet")

if (!url %in% catalog$assets$analysed_sst$href) {
  message("file does not exist, check available dates")
} else {
  dsn <- glue::glue("/vsicurl/{url}")
}
```

Read using the terra package:

``` r
library(terra)
(sst <- rast(dsn))
```

Or alternatively, using gdalraster:

``` r
library(gdalraster)
(ds <- new(GDALRaster, dsn))
```

Or with stars:

``` r
library(stars)
(sstars <- read_stars(dsn, proxy = TRUE))
```

To crop with the terra package:

``` r
crop(sst, ext(130, 150, -55, -42))
```

Or, more generally, project (by_util is very important for COG efficiency):

``` r
project(sst, rast(ext(130, 150, -55, -42), res = res(sst)), by_util = TRUE)
```

We can also change CRS and resolution as desired:

``` r
target <- rast(
  ext(c(-1, 1, -1, 1) * 6e5),
  res = 1000,
  crs = "+proj=laea +lon_0=147 +lat_0=-45"
)
project(sst, target, by_util = TRUE)
```

## License

See: [https://podaac.jpl.nasa.gov/CitingPODAAC](https://podaac.jpl.nasa.gov/CitingPODAAC)

> Data hosted by the PO.DAAC is openly shared, without restriction, in accordance with NASA's Earth Science program Data and Information Policy.

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


Annual cloud-free 30 m Landsat mosaics covering 60 Small Island Developing States (SIDS) and Pacific Community countries and territories for 2000–2025, generated using geometric median compositing and median absolute deviation (MAD) methods. Products include multi-spectral surface reflectance composites and temporal variability indices designed for land cover mapping, coastal change detection, long-term environmental monitoring, and machine learning applications.

landsat satellite-imagery remote-sensing cloud-optimized-geotiff land-cover time-series environmental-monitoring small-island-developing-states

Dataset files (212130 x .tif)

Source Cooperative product page

## README.md
# Geometric Median & Median Absolute Deviation Mosaics for Small Island Developing States

Annual cloud-free 30 m Landsat mosaics for Small Island Developing States (SIDS) and Pacific Community (SPC)'s countries/territories for 2000-2025, generated using robust pixel compositing methods for long-term environmental monitoring, land cover mapping, coastal change detection, and machine learning applications.

**Created by:** [Auspatious](https://auspatious.com/)

**Code:** https://github.com/auspatious/ldn-lulc

**License:** [Creative Commons BY](https://creativecommons.org/licenses/by/4.0/)

**Visualisation app:** https://mmufb4pjqf.execute-api.us-west-2.amazonaws.com/

---

## Dataset Summary

| Property | Value |
| - | - |
| Spatial resolution  | 30 m |
| Temporal coverage   | 2000–2025 |
| Temporal resolution | Annual |
| Sensors             | Landsat 5, 7, 8, 9 |
| Products            | Geometric Median, Median Absolute Deviation (MAD) |
| Format              | Cloud-Optimized GeoTIFF |
| Metadata            | Spatio-Temporal Asset Catalog (STAC) |
| Countries/territories | 60 |
| Regions             | 2 |
| Grid tiles processed    | 817 |

---

## Area of Interest

Coverage includes:

- [Small Island Developing States](https://www.un.org/ohrlls/content/about-small-island-developing-states)
- [Pacific Community (SPC)'s countries/territories](https://en.wikipedia.org/wiki/Pacific_Community#Membership)

Many countries/territories belong to both of these groups. In that case, they are placed in the Pacific region.

#### Pacific (22)

- American Samoa `ASM`
- Cook Islands `COK`
- Fiji `FJI`
- French Polynesia `PYF`
- Guam `GUM`
- Kiribati `KIR`
- Marshall Islands `MHL`
- Micronesia `FSM`
- Nauru `NRU`
- New Caledonia `NCL`
- Niue `NIU`
- Northern Mariana Islands `MNP`
- Palau `PLW`
- Papua New Guinea `PNG`
- Pitcairn Islands `PCN`
- Samoa `WSM`
- Solomon Islands `SLB`
- Tokelau `TKL`
- Tonga `TON`
- Tuvalu `TUV`
- Vanuatu `VUT`
- Wallis and Futuna `WLF`

#### Non-Pacific (38)

- Anguilla `AIA`
- Antigua and Barbuda `ATG`
- Aruba `ABW`
- Bahamas `BHS`
- Barbados `BRB`
- Belize `BLZ`
- Bermuda `BMU`
- British Virgin Islands `VGB`
- Cabo Verde `CPV`
- Cayman Islands `CYM`
- Comoros `COM`
- Cuba `CUB`
- Curaçao `CUW`
- Dominica `DMA`
- Dominican Republic `DOM`
- Grenada `GRD`
- Guadeloupe `GLP`
- Guinea-Bissau `GNB`
- Guyana `GUY`
- Haiti `HTI`
- Jamaica `JAM`
- Maldives `MDV`
- Martinique `MTQ`
- Mauritius `MUS`
- Montserrat `MSR`
- Puerto Rico `PRI`
- Saint Kitts and Nevis `KNA`
- Saint Lucia `LCA`
- Saint Vincent and the Grenadines `VCT`
- Seychelles `SYC`
- Singapore `SGP`
- Sint Maarten `SXM`
- Suriname `SUR`
- São Tomé and Príncipe `STP`
- Timor-Leste `TLS`
- Trinidad and Tobago `TTO`
- Turks and Caicos Islands `TCA`
- Virgin Islands, U.S. `VIR`

---

## Regions & Grids

Tiling is done using [ODC Gridspec](https://odc-geo.readthedocs.io/en/latest/_api/odc.geo.gridspec.GridSpec.html) to partition the data into spatial chunks for processing. Two gridspecs are used because the Pacific region straddles the antimeridian, which would cause a single grid in EPSG:6933 to break at that boundary.

| Region | Countries/Territories | Grid tiles | Grid GeoJSON | Output | CRS |
| --- | --- | --- | --- | --- | --- |
| Pacific | 22 | 517 | [sids_pacific_tiles.geojson](https://github.com/auspatious/ldn-lulc/blob/main/ldn/sids_pacific_tiles.geojson) | [dep_ls_geomad](https://source.coop/auspatious/geomad-sids/dep_ls_geomad) | EPSG:3832 |
| Non-Pacific | 38 | 300 | [sids_non_pacific_tiles.geojson](https://github.com/auspatious/ldn-lulc/blob/main/ldn/sids_non_pacific_tiles.geojson) | [ci_ls_geomad](https://source.coop/auspatious/geomad-sids/ci_ls_geomad) | EPSG:6933 |

---

## Products

Output files are Cloud-Optimized GeoTIFFs structured by grid index and year (`x/y/year`). Example:

```
https://source.coop/auspatious/geomad-sids/ci_ls_geomad/0-2-1/118/125/2000/ci_ls_geomad_118_125_2000_bcmad.tif
```

All outputs for a tile/year are included in a STAC item JSON e.g.
```https://data.source.coop/auspatious/geomad-sids/ci_ls_geomad/0-2-1/118/125/2000/ci_ls_geomad_118_125_2000.stac-item.json```

### Geometric Median Mosaic

A cloud-free annual surface reflectance composite generated using the geometric median of all valid observations. Unlike independent per-band median compositing, the geometric median preserves spectral relationships between bands, producing more physically realistic reflectance values.

[See more technical details here.](https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/?tab=description#geometric-median)

| Band   | Description              |
| ------ | ------------------------ |
| blue   | Blue                     |
| green  | Green                    |
| red    | Red                      |
| nir08  | Near Infrared 0.8μm      |
| swir16 | Short-wave Infrared 1.6μm |
| swir22 | Short-wave Infrared 2.2μm |

### Median Absolute Deviation (MAD)

A robust measure of temporal variability useful for quantifying uncertainty, identifying unstable surfaces, detecting environmental change, and supporting machine learning workflows.

[See more technical details here.](https://knowledge.dea.ga.gov.au/data/product/dea-geometric-median-and-median-absolute-deviation-landsat/?tab=description#median-absolute-deviation)

| Band   | Description                  |
| ------ | ---------------------------- |
| emad   | Euclidean distance           |
| smad   | Cosine (spectral) distance   |
| bcmad  | Bray–Curtis dissimilarity    |

### Other bands

A `count` band is also included, recording the number of valid observations used per pixel.

---

## Applications

These products are designed for change detection and land surface analysis. Auspatious uses them to classify land use/land cover across SIDS and Pacific countries/territories, with annual outputs feeding into Land Degradation Neutrality calculations for [UN SDG Indicator 15.3.1](https://sdgs.unep.org/article/sdg-indicator-1531) (proportion of land that is degraded over total land area).

---

## Method

### Input Data

Landsat Collection 2 Tier 1 data is preferred. Where insufficient timesteps are available, Tier 2 data is included, and if still insufficient, data from ±1 year is incorporated. A small number of tiles remain incalculable even after these relaxations.

### Cloud Masking

Cloud masking is aggressive - missing pixels are preferred over cloudy outputs. Masked layers include cloud, dilated cloud, cirrus, cloud shadow, and snow (flagged from `qa_pixel`). Additional custom whiteness and blueness indices catch residual unmasked cloud. Morphological filters clean up the mask and remove small isolated pixel groups surrounded by cloud. Saturated pixels are masked using `qa_radsat` (although saturation is much less impactful than cloud).

Snow is included as a mask because of its unlikelihood to be present in this AOI, and its common presence in the input data.

---

## Quality

Some areas remain partially incomplete. Cloud-persistent regions such as Fiji can lack sufficient cloud-free observations in certain years even after broadening the input data window.

---

## Data Access

Data is distributed through Source Cooperative: https://source.coop/auspatious/geomad-sids. The 2 regions are subfolders.

### STAC GeoParquet Index

A combined index for both regions is available at:

```
https://data.source.coop/auspatious/geomad-sids/ls_geomad/0-2-1/ls_geomad.parquet
```

Queryable directly - no need to download the full catalog. Within seconds you can search (spatially and temporally), load, and visualise the data using the following Python code.

### Search the Index

```python
from odc.stac import load
from rustac import search_sync
from pystac import Item

url = "https://data.source.coop/auspatious/geomad-sids/ls_geomad/0-2-1/ls_geomad.parquet"
bbox = [166.0, -22.5, 167.0, -21.5]  # Subset of New Caledonia example

search_items = search_sync(url, bbox=bbox, datetime="2023")
items = [Item.from_dict(doc) for doc in search_items]

print(f"Found {len(items)} items for the AOI in New Caledonia in 2023")
```

### Load with odc-stac

```python
ds = load(items, chunks={}, bbox=bbox)  # Lazy load with chunks
print(ds)
```

### Visualise on an interactive map

```python
ds.odc.explore(vmin=7500, vmax=12000)
```

### Visualise using Xarray

```python
ds[["red", "green", "blue"]].isel(time=0).to_array().squeeze().plot.imshow(vmin=7500, vmax=12000)
```

extent.temporal: None (probe sample) widened to ['2000-01-01T00:00:00Z', '2025-01-01T00:00:00Z'] using README-stated coverage ['2000', '2025'].


Land Use/Land Cover for Small Island Developing States


USGS Watershed Boundary Dataset

The USGS Watershed Boundary Dataset (WBD) is a nationally consistent geospatial dataset delineating hierarchical hydrologic unit boundaries across the United States and its territories. Published by the U.S. Geological Survey's National Geospatial Technical Operations Center, it defines watershed boundaries at multiple scales (HUC-2 through HUC-12) for use in water resource management, hydrologic modeling, and land stewardship.

watershed-boundaries hydrologic-units hydrology nhd water-resources stream-networks

Dataset files (1 x .filegdb)

Source Cooperative product page

providers: role [processor] for 'National Geospatial Technical Operations Center (NGTOC)' is an AI guess — NGTOC is the USGS division credited in the citation as compiling and distributing the WBD; assumed processor role.. Verify before flipping to _state: reviewed.


National Hydrology Dataset Plus High Resolution (NHDPlus HR)

NHDPlus HR is a national geospatial dataset depicting water flow across US landscapes and through the stream network. It integrates 1:24,000-scale National Hydrography Dataset High Resolution data, 10-meter 3D Elevation Program data, and the nationally complete Watershed Boundary Dataset. Coverage spans the United States and territories, providing hydrological features including streams, waterbodies, and drainage basins in File Geodatabase format.

hydrology watershed streams water-resources national-hydrography drainage-networks elevation

Dataset files (1 x .filegdb)

Source Cooperative product page

providers: role [producer, licensor] for 'U.S. Geological Survey (USGS)' is an AI guess — USGS is the known producer of NHDPlus HR (confirmed by API tags); licensor inferred from producer status as the originating federal agency, not explicitly stated.. Verify before flipping to _state: reviewed.


Failed

Flowlines from the NHDPlus High Resolution (HR) dataset covering the South Fork Eel River watershed in northern California. Contains 5,966 stream and river flowline features spanning roughly 39.6°N–40.4°N, 123.4°W–124.1°W. Distributed as FlatGeobuf and MBTiles formats.

flowlines hydrology hydrography nhd rivers streams watersheds south-fork-eel-river

Dataset files (1 x .fgb)

Source Cooperative product page


South Eel River PM Tiles and .fgb Layers

Flowline data for the South Fork Eel River in Northern California, derived from the National Hydrography Dataset Plus High Resolution (NHDPlus HR). Distributed as PMTiles for web visualization and FlatGeobuf for analysis, covering stream network topology, routing attributes, and elevation profiles across the watershed.

hydrology flowlines river-network nhdplus watershed stream-topology

Eel_flow.fgb

[admin_code] Geographic Names Information System identifier for the named feature

[placename] GNIS-assigned name for the hydrographic feature (e.g. stream or river name)

ReachCode

[admin_code] NHD reach code uniquely identifying each hydrographic reach

Source Cooperative product page

providers: role [producer, licensor] for 'U.S. Geological Survey (USGS)' is an AI guess — NHDPlus HR is a USGS product; licensor assumed from standard USGS public-domain data policy, not stated in this dataset's metadata.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [Eel_flow.GNIS_Name (placename), Eel_flow.GNIS_ID (admin_code), Eel_flow.ReachCode (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


England Trees Outside Woodland

The National Trees Outside Woodland (TOW) V1 map is a vector product funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme produced under Forest Research’s Earth Observation for Trees and Woodlands (EOTW) project. The TOW map identifies canopy cover over 3m tall and 5m2 area which exists outside the National Forest Inventory (National Forest Inventory - Forest Research). Canopy cover is categorised into the following woodland types - lone trees, groups of trees and small woodlands. The data set was derived from the Vegetation Object Model (VOM) (Environment Agency, EA), the National Lidar Survey (EA), and Sentinel-2 (European Space Agency) imagery using spatial algorithms. The method is fully automated with no manual manipulation or editing. The map and its production method has been quality assured by DEFRA science assurance protocols and assessed for accuracy using ground truth data. Because the process classifies objects based on proximity to features within OS mapping, there could be some misclassifications of those objects not included in the OS (specifically: static caravans, shipping containers, large tents, marquees, coastal cliffs and solar farms). This is a first release of this dataset, the quality of the production methods will be reviewed over the next year, and improvements will be made where possible. The TOW map is available under open government licence and free to download from the Forestry Commission open data download website (Forestry Commission) and view online on the NCEA ArcGIS Online web portal (Trees Outside Woodland). A full report containing details on methodology, accuracy and user guide is available. TOW map web portal link : Trees Outside Woodland Public Map FR TOW map web page : Trees Outside Woodland Map - Forest Research


Cloud-Optimized derivatives of the California Department of Fish and Wildlife Areas of Conservation

Berkeley DSE optimization of California Areas of Conservation Concern (ACE). ACE was a CDFW Project providing an easily-accessible and standardized way to view a mapped summary of the best available statewide data on California's biological richness and biodiversity for use in conservation planning. (Public Domain)

biodiversity california conservation public-data


Multi-Resolution Land Characteristics (MRCL)

The Multi-Resolution Land Characteristics (MRLC) consortium is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management and modeling applications. The creation of this consortium has resulted in the mapping of the lower 48 United States, Hawaii, Alaska and Puerto Rico into a comprehensive land cover product termed, the National Land Cover Database (NLCD), from decadal Landsat satellite imagery and other supplementary datasets.

The primary objective of the MRLC NLCD is to provide the Nation with nationally complete, current, consistent and public domain information on the Nation's land cover. Land cover information is critical for local, state and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics and developing land management policies.

All MRLC NLCD data products are available at no charge to the user.

Cloud-optimized translations provided via DSE team, Work in Progress.  see official data at https://www.mrlc.gov/data


AOML Ocean GTS Observations

All available ocean observations from NOAA's Atlantic Oceanographic and Meteorological Laboratory (AOML) sourced from the WMO Global Telecommunication System (GTS) flattened stream, spanning 2012-01-01 to 2025-06-30. The dataset has been converted to Icechunk format as a single large Zarr-compatible chunked store with global spatial coverage.

ocean-observations oceanography gts climate weather icechunk zarr noaa meteorology

Dataset files (26 x .icechunk)

Source Cooperative product page

providers: role [producer] for 'NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML)' is an AI guess — Identified as producer from 'AOML' in dataset title and description; licensor role not stated. Verify before flipping to _state: reviewed.


CAMS Aerosol Analysis

CAMS Aerosol Analysis containing many wavelength and aerosol properties from the Copernicus CAMS operational archive, in Icechunk format. The goal of this is to give quicker access to some of the slow-access data, and focus on variables important for solar energy forecasting and related tasks.


Nordic Regional NWP Models

Hourly atmospheric analysis and forecast archive derived from two regional numerical weather prediction models: the Danish HARMONIE model at 2 km resolution covering Greenland and Iceland, and the MetCoOp MEPS model at 2.5 km resolution covering the broader Nordic region. Constructed by archiving the first three forecast steps of each model run to produce an effective hourly analysis product. Data is stored in Icechunk format for cloud-native multidimensional array access.

numerical-weather-prediction meteorology weather-forecasting weather atmospheric-modeling icechunk zarr arctic

Dataset files (16 x .icechunk)

Source Cooperative product page


Global Hydrology Estimate and Enterprise Rain Rate

Global Hydrology Estimate (GHE) and Enterprise Rain Rate (ERR) products from NOAA, stored in Icechunk/Zarr format. GHE provides satellite-derived global precipitation estimates; the Enterprise Rain Rate is its operational successor. Data spans June 2019 through mid-2025 at global coverage.

precipitation climate weather noaa meteorology remote-sensing global-coverage

Dataset files (3 x .icechunk)

Source Cooperative product page


Geostationary Full-Disk Imagery

Full-disk geostationary satellite imagery from GOES, Himawari, and G2-KA satellites, stored in Icechunk format and converted from public AWS archives. Data consists of Level 1b radiance/reflectance imagery processed by Satpy. Coverage spans historical and near-real-time imagery and is being actively expanded.

remote-sensing satellite-imagery weather climate earth-observation icechunk zarr noaa meteorology geostationary

Dataset files (81 x .icechunk)

Source Cooperative product page


GEOS Atmosphere Composition Analysis

GEOS-CF (Goddard Earth Observing System Composition Forecast) is a high-temporal-resolution global atmospheric composition analysis product from NASA's Global Modeling and Assimilation Office (GMAO). It provides gridded aerosol and atmospheric trace gas data at 0.25-degree spatial resolution and 15-minute temporal intervals covering the entire globe. Data are stored in Icechunk/Zarr format and updated with approximately one-day latency.

atmospheric-data meteorology atmospheric-modeling climate zarr icechunk global-coverage time-series

NASA Goddard Modeling and Assimilation Office (GMAO)

Dataset files (2 x .icechunk)

Source Cooperative product page


GFS Analysis and Forecast

Global Forecast System (GFS) analysis and forecast data stored in Icechunk (Zarr-based) format, covering global extent at 0.25-degree resolution. Includes both operational GFS analysis fields and a pseudo-reanalysis dataset constructed from the most recent available real-time forecast step. Temporal coverage spans 2016-01-01 through at least October 2025.

numerical-weather-prediction weather meteorology reanalysis gfs icechunk zarr global-coverage

Dataset files (4 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA National Centers for Environmental Prediction (NCEP)' is an AI guess — GFS is operationally produced by NOAA/NCEP; assumed producer and licensor as US government data, but not stated in README. Verify before flipping to _state: reviewed.


Global Mosaic of Geostationary Images

A global mosaic of imagery assembled from multiple geostationary weather satellites at hourly cadence, covering latitudes from approximately -73° to +73°. Data is stored in Zarr v3 / IceChunk format and spans from July 2021 onward. Extended from a Hugging Face dataset originally produced by jacobbieker.

remote-sensing satellite-imagery meteorology weather zarr icechunk global-coverage geostationary

Dataset files (3 x .icechunk)

Source Cooperative product page


DWD ICON

Archive of global numerical weather prediction (NWP) model output from Deutscher Wetterdienst (DWD), Germany's national meteorological service. Covers the ICON Global and ICON ART Global (Aerosol and Reactive Trace gases) models, including ensemble runs and historical forecast data stored in GRIB2 format. Data is accessible via Icechunk/Zarr with near-global spatial coverage.

numerical-weather-prediction meteorology weather-forecasting weather ensemble-forecast atmospheric-modeling zarr icechunk

Dataset files (6 x .icechunk)

Source Cooperative product page


IFS Analysis

Global atmospheric analysis fields from the ECMWF Integrated Forecasting System (IFS), with a subset sourced from the NCAR/UCAR GDEX archive. Coverage spans January 2016 through June 2026 at global extent, comprising approximately 33 TB across nearly one million files. Data are stored in icechunk/Zarr format for cloud-native access.

meteorology numerical-weather-prediction global-coverage atmospheric-data icechunk zarr time-series atmospheric-modeling

European Centre for Medium-Range Weather Forecasts (ECMWF)

Dataset files (4 x .icechunk)

Source Cooperative product page


GPM IMERG Early and Final Half-Hourly Precipitation Datasets

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) Early and Final Half-Hourly precipitation products, stored as Zarr arrays with global coverage. The dataset spans from 2000-01-01 to present at 30-minute temporal resolution. Early run provides near-real-time estimates; Final run is a research-quality, latency-adjusted product.

precipitation gpm imerg remote-sensing zarr climate satellite-rainfall

Dataset files (3 x .zarr)

Source Cooperative product page

providers: role [producer] for 'JAXA' is an AI guess — JAXA is the co-lead agency on the GPM mission and contributes the core satellite; producer role inferred from mission structure, not stated in context.. Verify before flipping to _state: reviewed.


Copernicus Marine Wave and Ocean Products

A global collection of Copernicus Marine Service wave and ocean reanalysis and near-real-time analysis products at native model resolution, stored in Icechunk format. Temporal coverage spans 1980 to 2026 with near-global spatial extent. Select variables were rescaled to float16 to reduce storage footprint with minimal loss of precision.

oceanography reanalysis meteorology wave-modeling copernicus-marine sea-state ocean-analysis

Dataset files (3 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'Copernicus Marine Service (CMEMS)' is an AI guess — CMEMS is the named source of these products; licensor role inferred from standard Copernicus Marine Service open-data terms, not explicitly stated here.. Verify before flipping to _state: reviewed.


MetOffice Global 10km and UK 2km Deterministic Analysis

A 2-year rolling archive of the UK Met Office's global 10 km and UK 2 km deterministic model analyses, providing hourly atmospheric data from 2024-01-01 to near-present. The global model additionally includes 5 hours of forecast outputs. Data is stored in Icechunk format for cloud-native multidimensional array access.

numerical-weather-prediction meteorology weather atmospheric-modeling weather-forecasting icechunk zarr global-coverage

Dataset files (7 x .icechunk)

Source Cooperative product page


MRMS Rainfall Measurements

Multi-Radar Multi-Sensor (MRMS) hourly precipitation accumulations for the United States, converted to Zarr format. Includes gauge-only measurements, MultiSensor MultiPass estimates, and derived precipitation rate products. Data is sourced from a network of NOAA weather radars and ground-based rain gauges.

mrms precipitation rainfall radar earth-observation weather remote-sensing hourly

NOAA National Severe Storms Laboratory (NSSL)

Dataset files (10 x .zarr)

Source Cooperative product page


Cloud-Optimized NREL National Solar Radiation Database

International solar radiation data from the NREL National Solar Radiation Database (NSRDB), reformatted into Icechunk/Zarr format and optimized for reading full spatial extents at single timesteps. The initial release covers India; the full dataset spans hourly and sub-hourly satellite-derived solar resource variables globally. Intended for photovoltaic modeling and climate/weather applications.

solar-radiation climate weather remote-sensing zarr icechunk

National Renewable Energy Laboratory (NREL)

Dataset files (2 x .icechunk)

Source Cooperative product page

providers: role [host] for 'OpenEI' is an AI guess — OpenEI S3 bucket (nrel-pds-nsrdb) cited as upstream source; assumed host/distributor of original files. Verify before flipping to _state: reviewed.


Weather Observations

A collection of weather observations in Icechunk format. Data sources vary, but mostly from NOAA/EUMETSAT/ECMWF/MetOffice. Data includes radiosonodes, weather stations, and ocean observations.

icechunk zarr weather climate observation forecasting


Polar-Orbiting Satellite Imagery

Polar-orbiting satellite imagery from the JPSS (Joint Polar Satellite System) and MetOp satellite series, stored in Icechunk format. Data was converted from original instrument output using Satpy and covers weather, climate, and environmental observation use cases. The archive is continually expanding but is noted as incomplete.

satellite-imagery polar-orbiting jpss metop weather climate

Dataset files (6 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA / NASA (JPSS Program)' is an AI guess — JPSS is a joint NOAA/NASA program; producer and licensor roles assumed as the originating government agencies for the raw instrument data. Verify before flipping to _state: reviewed.

providers: role [producer, licensor] for 'EUMETSAT (MetOp)' is an AI guess — MetOp is an EUMETSAT/ESA series; producer and licensor roles assumed as the originating agency for raw data. Verify before flipping to _state: reviewed.


International Precipitation Radar

Precipitation radar estimates from national weather services in the United Kingdom and Finland, comprising Finnish hourly 1-hour, 12-hour, and 24-hour precipitation accumulations alongside UK composite rain rate radar output. Data is stored in icechunk-optimized Zarr format and covers August 2023 through April 2026.

precipitation radar earth-observation meteorology weather zarr icechunk time-series

Dataset files (3 x .icechunk)

Source Cooperative product page


SILAM Global Dust Model Forecasts

Global atmospheric dust model forecasts produced by the Finnish Meteorological Institute (FMI) using the SILAM (System for Integrated modeLling of Atmospheric coMposition) model, stored in Zarr format at 0.1-degree spatial resolution with global coverage. Also includes FMI aerosol AWS forecast data in Icechunk format.

aerosols dust forecast numerical-weather-prediction atmospheric-modeling meteorology

Dataset files (3 x .zarr)

Source Cooperative product page


WeatherReal-filtered NOAA Integrated Surface Dataset

NOAA Integrated Surface Dataset (ISD) weather station observations filtered according to the WeatherReal dataset methodology and converted to Zarr format. Covers global surface meteorological station data spanning 1901 to 2025, including temperature, precipitation, and other surface weather variables.

Dataset files (11 x .zarr)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA National Centers for Environmental Information' is an AI guess — NOAA is the originating agency for ISD; licensor role assumed from standard NOAA open data policy. Verify before flipping to _state: reviewed.

providers: role [processor] for 'WeatherReal' is an AI guess — Assumed processor role based on the filtering methodology described as 'filtered as in the WeatherReal dataset paper'. Verify before flipping to _state: reviewed.


GLanCE is a global land cover training dataset containing nearly 2 million samples spanning 1984–2020 at 30 m spatial resolution derived from the Landsat archive, covering seven primary and twelve secondary land cover classes across all global ecoregions. Each sample includes up to 23 land cover characteristics and supports mapping of both abrupt and gradual land cover change over nearly four decades. The classification scheme is compatible with IPCC land use categories and the FAO Land Cover Classification System (LCCS).

land-use remote-sensing agriculture land-cover landsat change-detection time-series

Dataset files (1 x .geojson)

Source Cooperative product page

## README.md
# GLanCE: A Global Land Cover Training Dataset from 1984 to 2020
The Global Land Cover Estimation (GLanCE) project provides high-quality long-term records of land cover and land cover change at 30 m spatial resolution for the 21st century using the Landsat archive. The GLanCE land cover classification scheme is designed to be compatible with common land cover classification systems such as the IPCC land use categories for greenhouse gas inventory reporting and the FAO Land Cover Classification System (LCCS). As part of the GLanCE project, we present a new land cover training database that is designed to provide global coverage, ensure accuracy of land cover labels at 30 m spatial resolution, cover nearly four decades, and produce a geographically dense dataset.

## Download Dataset
This repository contains a [Cloud-Native Geospatial](https://cloudnativegeo.org/) [GeoParquet](https://geoparquet.org/) version as well as a [GeoJSON](https://geojson.org/) version of the data. You can use the [BROWSE link](https://beta.source.coop/boston-university/bu-glance) on the left hand side to navigate to the data and download it. You can also access the S3 bucket directly with S3 tools (aws cli, boto3, etc); the S3 URI is s3://us-west-2.opendata.source.coop/boston-university/bu-glance/

## Creator & Contact
* [Boston University Global Land Cover Estimation (GLanCE)](https://sites.bu.edu/measures/)
* rkstan@bu.edu

## Data License
The data is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation & DOI
Stanimirova R., Tarrio K., Turlej K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) "A Global Land Cover Training Dataset from 1984 to 2020", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.x4xfh3

## Publication
Stanimirova, R., Tarrio, K., Turlej, K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) A global land cover training dataset from 1984 to 2020. Sci Data 10, 879 https://doi.org/10.1038/s41597-023-02798-5

| Column Name | Description |
| ------------- | ------------- |
| Lat  | Latitude  |
| Lon  | Longitude  |
| Start_Year  | Start year of segment, ranging from 1984 to 2020 (integer) |
| End_Year  | End year of segment, ranging from 1984 to 2020 (integer) |
| Glance_Class_ID_level1  | Level 1 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Barren/sparsely vegetated), 5 (Trees), 6 (Shrub), and 7 (Herbaceous). |
| Glance_Class_ID_level2  | Level 2 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Soil), 5 (Rock), 6 (Beach/sand), 7 (Deciduous), 8 (Evergreen), 9 (Mixed), 10 (Shrub), 11 (Grassland), 12 (Agriculture), and 13 (Moss/lichen). NaN values present. |
| Leaf_Type  | Tree leaf type: broadleaf (1), needleleaf (2), and mixed (3). NaN values present.  |
| Impervious_Percent  | Impervious percent for developed samples: low 0%-30% (1), medium 30%-60% (2), and high 60%-100% (3). NaN values present.  |
| Tree_Location  | Binary integer indicating whether trees are on the interior (0) or edge (1) of a forest. NaN values present.   |
| Veg_Density  | Vegetation density for trees and shrubs: sparse 0%-30% (1), open 30%-60% (2), and closed 60%-100% (3). NaN values present.   |
| Veg_Modifier | Vegetation modifiers, which can include one or more of the following: Cropland, Plantation, Wetland, Riparian/Flood, Mangrove, Greenhouse, and Trees/Shrub Present. NaN values present.    |
| Segment_Type  | Indicates whether a segment is stable (0) or transitional (1). See Section 1 for a detailed description. Land cover for transitional segments is recorded at both the beginning and end of the time segment - typically the first and last three years. NaN values present.    |
| Change  | Indicates presence (1) or absence (0) of land cover change for Level 1 land cover labels. Includes both abrupt change and gradual change (transitional segments (1) from the Segment_Type attribute) if it happened at any time for that training unit.   |
| LC_Confidence  | Interpreter confidence in the Level 1 land cover label from 1 (lowest) to 3 (highest). NaN values present.  |
| Level1_Ecoregion  | Ecoregion Level 1 number based on World Wildlife Fund definitions. For North America we used ecoregions based on the Environmental Protection Agency’s Ecoregions of North America product. |
| Level2_Ecoregion  | Ecoregion Level 2 number based on the Environmental Protection Agency’s Ecoregions of North America product. This field is available only for North America and is assigned a value of 0 for all other continents.   |
| Continent_Code  | Assigned continent number: North America (1), South America (2), Africa (3), Europe (4), Asia (5), and Oceania (6). |
| Dataset_Code |  Assigned dataset number: 1, 2, 3, 4, 5, 902, 999, 700, 701, 702, 703, 704, 705, 706, and 707. Numbers correspond to each Dataset as follows: STEP, CLUSTERING, LCMAP, ABoVE, MapBiomas, Feedback, Training_augment, MODIS_algo, GeoWiki, RadEarth, Collaborator_data, BU_team_collected, GLC30, LUCAS, ASB_crop.  For details see Scientific Data publication.    |
| Glance_ID  |  Unique ID for each sample.  |
| ID  |  ID for each unique combination of latitude and longitude. Change units have the same ID but different Glance_ID.   |

providers: role [host] for 'Radiant MLHub' is an AI guess — Original host inferred from dataset DOI domain (rdnt.x4xfh3) and citation referencing Radiant MLHub. Verify before flipping to _state: reviewed.


Clay Model v0 Embeddings

We provide here the embeddings for the 1M chips used to train Clay Model v0.
See documentation for details: https://clay-foundation.github.io/model/specification.html

remote sensing
earth
ai
foundational model


Plateau Tokyo Buildings in FlatCityBuf

Plateau Tokyo Buildings in FlatCityBuf (Updated 29 Jan 2026)


Plateau Tokyo Buildings in FlatCityBuf

Plateau Tokyo Buildings in FlatCityBuf


Tokyo 13999 district in FlatCityBuf

Tokyo 13999 district in FlatCityBuf


Geospatial ML

Visualizer components for geospatialml.com


Open Climate Risk

Building-level wildfire risk assessments across the continental United States, produced by CarbonPlan's Open Climate Risk project. The pipeline integrates USFS wildfire hazard potential raster inputs with Overture Maps building footprints and U.S. Census TIGER/Line administrative boundaries to compute risk metrics at building, Census block, tract, and county scales. Outputs are provided as Hive-partitioned Geoparquet, PMTiles, Icechunk tensor stores, GPKG, and CSV formats.

building-footprints climate census icechunk zarr geoparquet pmtiles wildfire-risk

Dataset files (10 x .icechunk)

Source Cooperative product page

## README.md
# Open Climate Risk

This Source Cooperative repository contains data products for the [Open Climate Risk](https://carbonplan.org/research/climate-risk) project. These data were used to build a scalable pipeline for calculating climate risk assessments at building-level resolution across the continental United States. Wildfire risk was calculated through a distributed processing system that can run locally or on cloud infrastructure. Technical documentation can be found [here](https://docs.carbonplan.org/ocr).

## Working with the data
Documentation for working with the raster and vector data can be found [here](https://docs.carbonplan.org/ocr/en/latest/how-to/work-with-data.html).

## Data Formats

**Tensor**
- Our raw input data consisted of either Zarr or TIFF files. We processed it into rechunked Icechunk stores.
- Our n-dimensional output data is stored as a single Icechunk store.

**Vector**
- Our raw input data was either GPKG or Geoparquet. All the vector pipeline inputs were converted to Geoparquet.
- Our output vector data is stored in multiple formats. See [our documentation on data access](https://docs.carbonplan.org/ocr/en/latest/access-data.html) for more details.
    - Hive-partitioned (state/county) Geoparquet file for risk sampled buildings across CONUS.
    - PMTiles layers for buildings, Census blocks, Census Tracts and counties.
    - Summary statistics files in GPKG and CSV

## Data structure

The data is split into `inputs` and `outputs,` where `inputs` are raw and processed data for the pipeline, and `outputs` are versioned dataset outputs.

```bash
├── input
│   └── fire-risk
│       ├── tensor
│       │   ├── USFS (Icechunk & TIFF)
│       │   │   ├── dillon-et-al-2023
│       │   │   ├── riley-et-al-2025
│       │   │   └── scott-et-al-2024
│       │   └── conus404-ffwi (Icechunk)
│       └── vector
│           ├── census-tiger (Geoparquet)
│           │   ├── LICENSE.txt (CC BY 4.0)
│           │   ├── blocks
│           │   ├── counties
│           │   └── tracts
│           └── overture-maps (Geoparquet)
│                └── LICENSE.txt (ODbL)
└── output
    └── fire-risk
        ├── tensor (Icechunk)
        │   ├── LICENSE.txt (CC BY 4.0)
        │   ├── NOTICE.txt
        │   └── production
        └── vector (Geoparquet, PMTiles, GPKG, csv)
            ├── LICENSE.txt (ODbL)
            ├── NOTICE.txt
            └── production

```

## Data Sources

The tensor outputs rely on data from [K Riley et al. 2025](https://doi.org/10.2737/RDS-2025-0006), [J Scott et al. 2024](https://doi.org/10.2737/RDS-2020-0016-2), and [R Rasmussen et al. 2023](https://doi.org/10.1175/BAMS-D-21-0326.1). The vector outputs additionally incorporate data from the [Overture Maps Foundation buildings dataset](https://docs.overturemaps.org/guides/buildings/) and [Census TIGER/Line](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html). Read more about our data sources [here](https://docs.carbonplan.org/ocr/en/latest/reference/data-sources.html).

## Terms of use

By viewing Open Climate Risk data, you agree to CarbonPlan’s [Terms of Use](https://carbonplan.org/terms) and [Privacy Policy](https://carbonplan.org/privacy).

This work contains information from the Overture Maps buildings database, which is made available here under the [Open Database License](https://opendatacommons.org/licenses/odbl) (ODbL).

Licenses for tensor ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0)) and vector ([ODbL](https://opendatacommons.org/licenses/odbl/1-0)) data are packaged with the data in colocated LICENSE.txt files.


30×30 state-level conservation policy implementation and progress across the United States

This repository contains a curated dataset of U.S. state-level 30×30 policy implementation and progress. Policy records were collected, reviewed, and standardized by the author from publicly available primary sources, then compiled into a single, harmonized dataset.


The Public Utility Data Liberation Project

The PUDL Project is an open source data processing pipeline that makes US energy data easier to access and use programmatically.

Hundreds of gigabytes of valuable data are published by US government agencies, but it's often difficult to work with. PUDL takes the original spreadsheets, CSV files, and databases and turns them into a unified resource. This allows users to spend more time on novel analysis and less time on data preparation.

energy
electricity
climate change
united states
natural gas
coal
emissions
finance
eia
ferc
epa


Demo of Source Cooperative

No Description Provided


California Department of Water Resources (DWR) layers identifying Disadvantaged Communities (DAC, median household income below 80% of statewide) and Economically Distressed Areas (EDA, MHI below 85% of statewide plus additional distress factors) at Census tract, place, block group, and county geographies. Designations are derived from ACS 2019–2023 5-year estimates joined to 2020 Census boundaries and indicate eligibility for California water-related funding programs. Layers are published as GeoParquet, PMTiles, and H3-partitioned Parquet files.

demographics census administrative-boundaries geoparquet pmtiles h3

dac-2023

[admin_code] Census FIPS code (state+county+tract+block group)

[latlon] Internal point latitude (paired with INTPTLON20)

geom

[geometry] Census geography polygon in EPSG:4326

PlaceNames

[placename] Comma-separated place names for Place geography

3 files sampled for this table

dac-2023/blockgroup/hex

2 files sampled for this table

dac-2023/place/hex

2 files sampled for this table

dac-2023/tract/hex

[admin_code] Census tract FIPS code

[spatial_index] H3 cell at resolution 10 (~15,000 m² per hex)

2 files sampled for this table

eda-2023

[placename] Geography label for county or place features

4 files sampled for this table

eda-2023/blockgroup/hex

2 files sampled for this table

eda-2023/county/hex

2 files sampled for this table

eda-2023/place/hex

2 files sampled for this table

eda-2023/tract/hex

2 files sampled for this table

Source Cooperative product page

## README.md
# California Disadvantaged Communities (DAC) and Economically Distressed Areas (EDA), 2023

This dataset republishes California Department of Water Resources (DWR) layers for
**Disadvantaged Communities (DAC)** and **Economically Distressed Areas (EDA)** at
three Census geographies (Place, Tract, Block Group), with County also available for
EDA. Source data are joined from American Community Survey (ACS) 2019–2023 5-year
estimates onto 2020 Census geographies, and identify communities eligible for
California water-related funding programs.

A community is designated **Disadvantaged (DAC)** when its Median Household Income
(MHI) is less than 80% of the statewide MHI per California Water Code §79505.5(a).
The published `DAC23` field encodes this as `Y` / `N` / `Data Not Available`. The
finer **Severely Disadvantaged (SDAC)** distinction (MHI < 60% of statewide) is not
encoded as a separate flag in this dataset — derive it from `MHI23` and the
statewide MHI threshold relevant to your eligibility window.

**Economically Distressed Areas (EDA)** are defined per Water Code §79702(k) as a
subset of municipalities with MHI < 85% of statewide MHI together with at least two
of: financial hardship, unemployment ≥ 2% above statewide, or low population density.

## Layers

Files are organized by the published Census geography:

| Layer | Path prefix | Features | Use |
|-------|-------------|----------|-----|
| DAC — Census Tract | `dac-2023/tract` | 9,129 | Most common eligibility unit |
| DAC — Census Place | `dac-2023/place` | 1,618 | Cities and CDPs |
| DAC — Block Group | `dac-2023/blockgroup` | 25,607 | Finest-grain DAC layer |
| EDA — Census Tract | `eda-2023/tract` | 9,129 | EDA at tract level |
| EDA — Census Place | `eda-2023/place` | 1,618 | EDA at place level |
| EDA — Block Group | `eda-2023/blockgroup` | 25,607 | EDA at block-group level |
| EDA — County | `eda-2023/county` | 58 | EDA at county level |

Each layer is published in three formats:

- `<prefix>.parquet` — GeoParquet for analytical queries (DuckDB, Polars, geopandas)
- `<prefix>.pmtiles` — Vector tiles for web maps (MapLibre, Leaflet)
- `<prefix>/hex/h0={cell}/data_0.parquet` — H3 hexagonal tiling at resolution 10, hive-partitioned by H3 resolution-0 parent cell

## Field Reference

### DAC fields (Tract, Place, Block Group)

| Field | Type | Description |
|-------|------|-------------|
| `GEOID` / `GEOID20` | string | Census FIPS code (state+county+tract+blkgrp, or place code) |
| `STATEFP` / `STATEFP20` | string | State FIPS (`06` for California) |
| `COUNTYFP20` | string | County FIPS (Tract, Block Group only) |
| `TRACTCE20` | string | Tract code within county |
| `BLKGRPCE20` | string | Block group code within tract (Block Group only) |
| `PlaceCode` | string | Place code (Place only) |
| `PlaceNames` | string | Comma-separated place names (Place only) |
| `ALAND` / `ALAND20` | float | Land area in square meters |
| `AWATER` / `AWATER20` | float | Water area in square meters |
| `INTPTLAT*` / `INTPTLON*` | string | Internal point latitude / longitude |
| `Pop23` | float | Total population, ACS 2019–2023 |
| `HH23` | float | Households, ACS 2019–2023 |
| `MHI23` | float | Median household income, ACS 2019–2023 (USD) |
| `DAC23` | string | DAC flag: `Y` (designated DAC), `N` (not DAC), or `Data Not Available`. The Tract layer also distinguishes severely-disadvantaged designations — inspect distinct values in your layer of interest. |
| `Source` | string | Data source for the MHI estimate |
| `Comments` | string | Editor notes (often empty) |
| `Date_Recor` | date | When the row was last edited (truncated from shapefile DBF) |
| `Record_Las` | string | Editor identifier (truncated from shapefile DBF) |
| `Date_Data_` | string | Vintage of the underlying ACS estimate (truncated from shapefile DBF) |
| `_cng_fid`, `OGC_FID` | int64 | Row-unique keys added by the conversion pipeline |

### EDA-only fields (in addition to the geographic identifiers above)

| Field | Type | Description |
|-------|------|-------------|
| `LandSqMile` | float | Land area in square miles |
| `PopDensity` | int | Population density (people per land sq mile) |
| `Flag_MHI_P` | string | Flag indicating MHI is below 85% of statewide MHI |
| `Unempl_EDD` | float | Unemployment rate (Place, County) |
| `UnEmFlgEDD` | string | Flag indicating unemployment ≥ 2 points above statewide (Place, County) |
| `NAMELSAD` / `NAMELSAD20` | string | Geography name (Place, County) |

> **Note:** Source data are published as shapefiles, where DBF column names are
> capped at 10 characters. Names longer than 10 characters in the original DWR
> schema are truncated in the GeoParquet (e.g. `Date_Record_Last_Edited` →
> `Date_Recor`, `Shape.STArea()` → `ShapeSTAre`).

### H3 indexing fields (added by this pipeline)

| Field | Type | Description |
|-------|------|-------------|
| `h10` | uint64 | H3 cell at resolution 10 (~15,000 m² per hex) |
| `h9`, `h8` | uint64 | Parent H3 cells |
| `h0` | uint64 | H3 resolution-0 parent (partition key) |

## Usage

### DuckDB — analytical query

```sql
INSTALL spatial; LOAD spatial;
INSTALL httpfs;  LOAD httpfs;

-- Lowest-income disadvantaged Census tracts in 2023
SELECT GEOID20, MHI23, Pop23
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-ca-dac/dac-2023/tract.parquet')
WHERE DAC23 = 'Y'
ORDER BY MHI23 ASC
LIMIT 20;
```

### DuckDB — H3 hex spatial join

```sql
-- Anonymous-read S3 secret for the public NRP endpoint
CREATE SECRET nrp (
  TYPE s3, KEY_ID '', SECRET '',
  ENDPOINT 's3-west.nrp-nautilus.io', URL_STYLE 'path', USE_SSL 'TRUE'
);

-- Count DAC tracts overlapping each h0 partition
SELECT h0, COUNT(DISTINCT GEOID20) AS dac_tracts
FROM read_parquet(
  's3://public-ca-dac/dac-2023/tract/hex/**/*.parquet',
  hive_partitioning = true
)
WHERE DAC23 = 'Y'
GROUP BY h0
ORDER BY dac_tracts DESC;
```

### MapLibre — DAC tracts overlay

```js
map.addSource('dac-tract', {
  type: 'vector',
  url: 'pmtiles://https://s3-west.nrp-nautilus.io/public-ca-dac/dac-2023/tract.pmtiles'
});

map.addLayer({
  id: 'dac-tract-fill',
  type: 'fill',
  source: 'dac-tract',
  'source-layer': 'tract',  // last path segment of --dataset
  paint: {
    'fill-color': [
      'match', ['get', 'DAC23'],
      'SDAC', '#b30000',
      'DAC',  '#e34a33',
      '#ffffff'
    ],
    'fill-opacity': 0.5
  }
});
```

PMTiles internal `source-layer` names by file:

| File | `source-layer` |
|------|----------------|
| `dac-2023/tract.pmtiles` | `tract` |
| `dac-2023/place.pmtiles` | `place` |
| `dac-2023/blockgroup.pmtiles` | `blockgroup` |
| `eda-2023/tract.pmtiles` | `tract` |
| `eda-2023/place.pmtiles` | `place` |
| `eda-2023/blockgroup.pmtiles` | `blockgroup` |
| `eda-2023/county.pmtiles` | `county` |

## Citation

California Department of Water Resources (DWR). *Disadvantaged Communities and
Economically Distressed Areas Mapping Tool, 2023 update.*
<https://gis.water.ca.gov/app/dacs/>

Original data layers are republished from the
[California Natural Resources Agency Open Data hub](https://data.cnra.ca.gov/) under
the State of California's terms of use.

## Data Lineage

- **Source**: DWR ArcGIS hosted layers, 2023 vintage, downloaded as shapefile
  (EPSG:4326) from the CNRA Open Data hub
- **Conversion**: `cng-convert-to-parquet` produces optimized GeoParquet
- **PMTiles**: `tippecanoe` with `--drop-densest-as-needed --extend-zooms-if-still-dropping`
- **H3 hex tiling**: `cng-datasets vector` at resolution 10, parent resolutions 9, 8, 0,
  hive-partitioned on `h0`

See [boettiger-lab/datasets](https://github.com/boettiger-lab/datasets) for the
processing pipeline source.

providers: role [host] for 'California Natural Resources Agency (CNRA)' is an AI guess — CNRA Open Data hub is cited as the source of the original shapefiles; assumed distributor/host role.. Verify before flipping to _state: reviewed.

providers: role [processor] for 'boettiger-lab' is an AI guess — Processing pipeline (GeoParquet conversion, PMTiles tiling, H3 partitioning) attributed to boettiger-lab/datasets on GitHub.. Verify before flipping to _state: reviewed.

extent.temporal: None (probe sample) widened to ['2019-01-01T00:00:00Z', '2023-01-01T00:00:00Z'] using README-stated coverage ['2019', '2023'].

extent.spatial.bbox: omitted; spatial reference is categorical via [dac-2023.geom (geometry), dac-2023.INTPTLAT20 (latlon), dac-2023.GEOID20 (admin_code), dac-2023.PlaceNames (placename), eda-2023.geom (geometry), eda-2023.NAMELSAD20 (placename), dac-2023/tract/hex.h10 (spatial_index), dac-2023/tract/hex.GEOID20 (admin_code)]. Add bbox once the placename → bbox gazetteer covers it.


California 30x30 data

No Description Provided


CalEnviroScreen

CalEnviroScreen 5.0 (Draft) is a cumulative environmental impact screening tool developed by California's Office of Environmental Health Hazard Assessment (OEHHA) that assigns a composite score to every California census tract based on pollution burden and population vulnerability. It integrates over 20 indicators spanning air quality, toxic releases, contaminated water and sites, and population health and socioeconomic factors into a single CES 5.0 Score ranging from 0–100. Draft version 5.0 was released January 2026 using 2020 Decennial Census tract geography covering 9,106 tracts statewide.

environmental-monitoring air-quality census demographics environmental-justice public-health geoparquet pmtiles

ces5.parquet

tract

[admin_code] Census Tract ID from 2020 Decennial Census

ZIP

[zipcode] Postal ZIP code for the census tract

AppoxLoc

[placename] Approximate city or unincorporated area name

county

[placename] California county name

region

[placename] California region name

Shape

[geometry] Census tract polygon geometry (EPSG:4326 / OGC:CRS84)

bbox

STRUCT(xmin DOUBLE, ymin DOUBLE, xmax DOUBLE, ymax DOUBLE)

calenviroscreen-5-0/ces5/hex

[spatial_index] H3 resolution 10 hexagonal cell index

[spatial_index] H3 resolution 8 parent hexagonal cell index

[spatial_index] H3 resolution 9 parent hexagonal cell index

ZIP

[zipcode] Postal ZIP code

county

[placename] California county name

region

[placename] California region name

[spatial_index] H3 resolution 0 parent hexagonal cell index

2 files sampled for this table

Source Cooperative product page

## README.md
# CalEnviroScreen 5.0 (Draft)

## Overview

CalEnviroScreen is a mapping tool developed by California's Office of Environmental
Health Hazard Assessment (OEHHA) to identify communities disproportionately burdened
by multiple sources of pollution. It combines environmental exposures, environmental
effects, sensitive population health indicators, and socioeconomic factors into a single
cumulative impact score for every California census tract.

Draft CalEnviroScreen 5.0 was released January 2026. It uses **2020 Decennial Census**
tract geography (9,106 tracts), updates all existing indicators, and adds two new
indicators (Children's Lead Risk from Housing and Small Air Toxic Sites) compared to
version 4.1.

The overall **CES 5.0 Score** is calculated as:

> **CES Score = Pollution Burden Score × Population Characteristics Score**

Both component scores are scaled 0–10, so the overall score ranges from 0–100.

## Data Formats & Access

**Base URL**: `https://s3-west.nrp-nautilus.io/public-calenviroscreen/`

| Format | Path | Description |
|--------|------|-------------|
| GeoParquet | `calenviroscreen-5-0/ces5.parquet` | Census-tract polygons with all attributes |
| PMTiles | `calenviroscreen-5-0/ces5.pmtiles` | Vector tiles for web mapping |
| H3 Hex Parquet | `calenviroscreen-5-0/ces5/hex/h0={cell}/data_0.parquet` | H3 resolution 10 with parent resolutions 9, 8, 0 |

## Usage Examples

### MapLibre GL JS

```javascript
map.addSource("ces5", {
  type: "vector",
  url: "pmtiles://https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.pmtiles",
});

map.addLayer({
  id: "ces5-fill",
  type: "fill",
  source: "ces5",
  "source-layer": "ces5",   // ← vector layer name inside the PMTiles
  paint: {
    "fill-color": [
      "interpolate", ["linear"],
      ["get", "CIscore_Pctl"],
      0, "#ffffcc", 50, "#fd8d3c", 100, "#800026"
    ],
    "fill-opacity": 0.7
  }
});
```

### DuckDB

```sql
-- Load spatial extension
INSTALL spatial; LOAD spatial;

-- Query top 10 most burdened tracts by CES score
SELECT tract, county, CIscore, CIscore_Pctl, Population
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
ORDER BY CIscore DESC NULLS LAST
LIMIT 10;

-- Find tracts in the top 10% of CES score (above 90th percentile)
SELECT tract, county, AppoxLoc, CIscore, CIscore_Pctl,
       Poverty, Asthma_Pctl, DieselPM_Pctl
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
WHERE CIscore_Pctl >= 90
ORDER BY CIscore_Pctl DESC;

-- Summary by county: average CES score and most burdened indicators
SELECT county,
       COUNT(*) AS num_tracts,
       ROUND(AVG(CIscore), 2) AS avg_ces_score,
       ROUND(AVG(Poverty), 2) AS avg_poverty_pct,
       ROUND(AVG(AirPM25), 4) AS avg_pm25
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
GROUP BY county
ORDER BY avg_ces_score DESC;
```

### Python (GeoPandas)

```python
import geopandas as gpd

ces = gpd.read_parquet(
    "https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet"
)

# Top burdened tracts
top_burden = ces.nlargest(100, "CIscore")[
    ["tract", "county", "CIscore", "CIscore_Pctl", "Poverty", "asthma"]
]
print(top_burden)
```

## Data Dictionary

Data source: [Draft CalEnviroScreen 5.0 Data Dictionary (PDF)](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf)

### Identification & Geography

| Column | Type | Description |
|--------|------|-------------|
| `tract` | DOUBLE | Census Tract ID from 2020 Decennial Census |
| `ZIP` | INTEGER | Postal ZIP code for the census tract |
| `AppoxLoc` | VARCHAR | Approximate city or unincorporated area (Esri Demographics 2024; for reference only) |
| `county` | VARCHAR | California county |
| `region` | VARCHAR | California region |
| `Population` | DOUBLE | 2023 5-year ACS population estimate |

### CES Score

| Column | Type | Description |
|--------|------|-------------|
| `CIscore` | DOUBLE | **Draft CES 5.0 Score** — Pollution Burden Score × Population Characteristics Score (range ~0–100) |
| `CIscore_Pctl` | DOUBLE | Percentile of the CES 5.0 Score (0–100) |

### Pollution Burden — Exposure Indicators

Each indicator has a raw value column and a `_Pctl` percentile column (0–100).

| Column | Type | Description |
|--------|------|-------------|
| `AirOzone` | DOUBLE | Mean of summer months (May–Oct) daily max 8-hour ozone concentration |
| `AirOzone_Pctl` | DOUBLE | Ozone percentile |
| `AirPM25` | DOUBLE | Annual mean PM2.5 concentration (µg/m³) |
| `AirPM25_Pctl` | DOUBLE | PM2.5 percentile |
| `ChildLead` | DOUBLE | Potential risk for lead exposure in children in low-income communities with older housing |
| `ChildLead_Pctl` | DOUBLE | Children's Lead Risk from Housing percentile |
| `DieselPM` | DOUBLE | Diesel PM emissions from on-road and non-road sources within and near populated blocks |
| `DieselPM_Pctl` | DOUBLE | Diesel PM percentile |
| `DrinkingWater` | DOUBLE | Drinking water contaminant index for selected contaminants |
| `DrinkingWater_Pctl` | DOUBLE | Drinking Water Contaminants percentile |
| `Pesticides` | DOUBLE | Total pounds of selected active pesticide ingredients per square mile (production agriculture) |
| `Pesticides_Pctl` | DOUBLE | Pesticide Use percentile |
| `ToxReleases` | DOUBLE | Toxicity-weighted air concentrations from chemical releases by large facilities |
| `ToxReleases_Pctl` | DOUBLE | Toxic Releases from Facilities percentile |
| `TrafficImp` | DOUBLE | Traffic density in vehicle-km/hr per road length within 150m of census tract boundary |
| `TrafficImp_Pctl` | DOUBLE | Traffic Impacts percentile |

### Pollution Burden — Environmental Effects Indicators

| Column | Type | Description |
|--------|------|-------------|
| `CleanupSites` | DOUBLE | Sum of weighted EnviroStor cleanup sites and NPL Superfund sites within buffered distances |
| `CleanupSites_Pctl` | DOUBLE | Cleanup Sites percentile |
| `gwthreats` | DOUBLE | Sum of weighted GeoTracker cleanup, land disposal, UST, produced water pond, dairy/feedlot sites within buffered distances |
| `GWThreats_Pctl` | DOUBLE | Groundwater Threats percentile |
| `HazWaste` | DOUBLE | Sum of weighted hazardous waste facilities and large quantity generators within buffered distances |
| `HazWaste_Pctl` | DOUBLE | Hazardous Waste percentile |
| `ImpWaters` | DOUBLE | Sum of pollutants across impaired water bodies within buffered distances |
| `ImpWaters_Pctl` | DOUBLE | Impaired Waters percentile |
| `SmAirToxSites` | DOUBLE | Sum of CEIDARS emissions sites and oil/gas well sites within buffered distances (new in v5.0) |
| `SmAirToxSites_Pctl` | DOUBLE | Small Air Toxic Sites percentile |
| `SolidWaste` | DOUBLE | Sum of weighted solid waste sites and facilities (SWIS) within buffered distances |
| `SolidWaste_Pctl` | DOUBLE | Solid Waste percentile |

### Pollution Burden — Component Score

| Column | Type | Description |
|--------|------|-------------|
| `Pollution` | DOUBLE | Average of percentiles from Pollution Burden indicators (Environmental Effects weighted at 0.5×) |
| `PollutionScore` | DOUBLE | Pollution Burden variable scaled 0–10 (used to calculate CES 5.0 Score) |
| `Pollution_Pctl` | DOUBLE | Pollution Burden percentile |

### Population Characteristics — Sensitive Population Indicators

| Column | Type | Description |
|--------|------|-------------|
| `asthma` | DOUBLE | Age-adjusted rate of emergency department visits for asthma |
| `Asthma_Pctl` | DOUBLE | Asthma percentile |
| `Cardiovascular` | DOUBLE | Age-adjusted rate of ED visits for heart attacks per 10,000 |
| `Cardiovascular_Pctl` | DOUBLE | Cardiovascular Disease percentile |
| `DiabetesPrev` | DOUBLE | Model-based prevalence of diabetes in adults ≥18 years old |
| `DiabetesPrev_Pctl` | DOUBLE | Diabetes Prevalence percentile |
| `LowBirthWeight` | DOUBLE | Percent of infants weighing less than 2,500g (~5.5 lbs) |
| `LowBirthWeight_Pctl` | DOUBLE | Low-Birth-Weight Infants percentile |

### Population Characteristics — Socioeconomic Factors

| Column | Type | Description |
|--------|------|-------------|
| `Education` | DOUBLE | Percentage of population over 25 with less than a high school education |
| `Education_Pctl` | DOUBLE | Educational Attainment percentile |
| `HousingBurden` | DOUBLE | Percentage of households that are low-income AND severely burdened by housing costs |
| `HousingBurden_Pctl` | DOUBLE | Housing Burden percentile |
| `LinguisticIso` | DOUBLE | Percentage of households where no one over age 14 speaks English well |
| `LinguisticIso_Pctl` | DOUBLE | Linguistic Isolation percentile |
| `Poverty` | DOUBLE | Percentage of people living below twice the federal poverty level |
| `Poverty_Pctl` | DOUBLE | Poverty percentile |
| `Unemployment` | DOUBLE | Percentage of people aged 16+ who are unemployed and eligible for the workforce |
| `Unemployment_Pctl` | DOUBLE | Unemployment percentile |

### Population Characteristics — Component Score

| Column | Type | Description |
|--------|------|-------------|
| `PopChar` | DOUBLE | Average of percentiles from Population Characteristics indicators |
| `PopCharScore` | DOUBLE | Population Characteristics variable scaled 0–10 (used to calculate CES 5.0 Score) |
| `PopChar_Pctl` | DOUBLE | Population Characteristics percentile |

### Demographic Profile

| Column | Type | Description |
|--------|------|-------------|
| `PopUnd_10` | DOUBLE | % of population under 10 years old (2023 5-year ACS) |
| `Pop10_64` | DOUBLE | % of population aged 10–64 years (2023 5-year ACS) |
| `PopOver_65` | DOUBLE | % of population 65 years and older (2023 5-year ACS) |
| `White_Pct` | DOUBLE | % non-Hispanic White |
| `Hispanic_Pct` | DOUBLE | % Hispanic or Latino |
| `Black_Pct` | DOUBLE | % non-Hispanic African American or Black |
| `NatAmeri_Pct` | DOUBLE | % non-Hispanic Native American |
| `Asian_Pct` | DOUBLE | % non-Hispanic Asian or Pacific Islander |
| `OtherMulti_Pct` | DOUBLE | % non-Hispanic "other" or multiple races |

### Geometry

| Column | Type | Description |
|--------|------|-------------|
| `Shape` | GEOMETRY | Census tract polygon (EPSG:4326) |
| `Shape_Length` | DOUBLE | Perimeter length |
| `Shape_Area` | DOUBLE | Area |
| `bbox` | STRUCT | Bounding box (xmin, ymin, xmax, ymax) |

## Notes on Zeros and Missing Values

- **NA (missing)**: Indicator value could not be calculated — due to no monitoring, unreliable estimates, or insufficient population. Census tracts with >4 missing Population Characteristics indicators receive NA for the overall CES score.
- **Zero raw value**: Monitoring occurred but no impact detected (e.g., no hazardous facilities within buffer distance). Zeros are excluded from percentile calculations to avoid inflating ranks.
- **Zero percentile**: Assigned to tracts with a raw value of zero, meaning no measurable impact for that indicator.
- Census tracts with <50 people, or <50 people living outside group quarters, receive NA for Population Characteristics.

See the [data dictionary PDF](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf) for full description of zeros and NAs by indicator.

## Citation

California Office of Environmental Health Hazard Assessment (OEHHA). *Draft CalEnviroScreen 5.0*. Released January 2026. Available at: https://oehha.ca.gov/calenviroscreen/general-info/draft-calenviroscreen-50

Contact: calenviroscreen@oehha.ca.gov

## Resources

- [Draft CalEnviroScreen 5.0 Homepage](https://oehha.ca.gov/calenviroscreen/general-info/draft-calenviroscreen-50)
- [CalEnviroScreen FAQs](https://oehha.ca.gov/calenviroscreen/calenviroscreen-faqs)
- [Data Dictionary PDF](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf)
- [California Open Data Portal](https://data.ca.gov/dataset/draft-calenviroscreen-5-0)
- [OEHHA CalEnviroScreen](https://oehha.ca.gov/calenviroscreen)

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [calenviroscreen-5-0.tract (admin_code), calenviroscreen-5-0.ZIP (zipcode), calenviroscreen-5-0.AppoxLoc (placename), calenviroscreen-5-0.county (placename), calenviroscreen-5-0.region (placename), calenviroscreen-5-0.Shape (geometry), calenviroscreen-5-0/ces5/hex.h10 (spatial_index), calenviroscreen-5-0/ces5/hex.h9 (spatial_index), calenviroscreen-5-0/ces5/hex.h8 (spatial_index), calenviroscreen-5-0/ces5/hex.h0 (spatial_index), calenviroscreen-5-0/ces5/hex.ZIP (zipcode), calenviroscreen-5-0/ces5/hex.AppoxLoc (placename), calenviroscreen-5-0/ces5/hex.county (placename), calenviroscreen-5-0/ces5/hex.region (placename)]. Add bbox once the placename → bbox gazetteer covers it.


Irrecoverable Carbon - Conservation International

Global raster dataset mapping irrecoverable carbon — ecosystem carbon stocks that cannot be recovered by 2050 if lost — along with vulnerable and manageable carbon stocks. Produced by Conservation International, the dataset spans 2010–2024 in two versioned releases (v1: 2021, v2: 2025 with corrections), delivered as Cloud-Optimized GeoTIFFs and H3 hexagonal Parquet files at global coverage.

conservation climate remote-sensing biodiversity global-coverage time-series h3 irrecoverable-carbon

Dataset files (10 x .tif)

Source Cooperative product page

## README.md
# Irrecoverable Earth: Global Carbon Reserves

## Overview

This dataset maps global "irrecoverable carbon" — carbon stocks that, if lost, cannot be recovered by 2050, making their conservation critical for meeting climate goals. The data also includes "manageable carbon" (stocks that can be influenced by human management) and "vulnerable carbon" (total stocks vulnerable to release upon land conversion).

This collection contains **two releases**:
- **v1 (2021 release)**: 2010 and 2018 data with biomass, soil, and total components (Zenodo 4091029)
- **v2 (2025 update)**: 2010, 2018, 2022, 2023, and 2024 data (total only); includes corrections to the 2018 baseline (Zenodo 17645053)

## Source & Attribution

**Source**: Conservation International
**Project Page**: https://www.conservation.org/irrecoverable-carbon

**v1 Data**: https://doi.org/10.5281/zenodo.4091029
**v2 Data (2025 update)**: https://doi.org/10.5281/zenodo.17645053

**Citation**: Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth's ecosystems. *Nat Sustain* **5**, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6

**License**: Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0)

**Key findings from 2025 update**: 5.4% (7.4 Gt) of Earth's irrecoverable carbon has been lost between 2018–2024; global total is now 128.0 Gt.

---

## Data Formats & Access

**Base URL**: `https://s3-west.nrp-nautilus.io/public-carbon/`

---

## v2 Data (2025 Release — Zenodo 17645053)

Covers years **2010, 2018, 2022, 2023, 2024** for total carbon only. The 2018 baseline includes corrections to boreal grassland/shrubland estimates and updated coastal ecosystem extents. Note: irrecoverable carbon is available for all 5 years; vulnerable and manageable are available for 2010, 2018, and 2024 only.

### v2 Cloud-Optimized GeoTIFFs

**Path Pattern**: `v2/cogs/{category}_c_total_{year}.tif`

| File | Year | Description |
|------|------|-------------|
| `v2/cogs/irrecoverable_c_total_2024.tif` | 2024 | Irrecoverable carbon total (corrected baseline) |
| `v2/cogs/irrecoverable_c_total_2023.tif` | 2023 | Irrecoverable carbon total |
| `v2/cogs/irrecoverable_c_total_2022.tif` | 2022 | Irrecoverable carbon total |
| `v2/cogs/irrecoverable_c_total_2018.tif` | 2018 | Irrecoverable carbon total (corrected baseline) |
| `v2/cogs/irrecoverable_c_total_2010.tif` | 2010 | ⚠ **Not available** — source file has corrupt TIFF IFD (Zenodo upstream issue) |
| `v2/cogs/vulnerable_c_total_2024.tif` | 2024 | Vulnerable carbon total |
| `v2/cogs/vulnerable_c_total_2018.tif` | 2018 | Vulnerable carbon total |
| `v2/cogs/vulnerable_c_total_2010.tif` | 2010 | Vulnerable carbon total |
| `v2/cogs/manageable_c_total_2024.tif` | 2024 | Manageable carbon total |
| `v2/cogs/manageable_c_total_2018.tif` | 2018 | Manageable carbon total |
| `v2/cogs/manageable_c_total_2010.tif` | 2010 | Manageable carbon total |

All v2 COGs are compressed with ZSTD and use GoogleMapsCompatible tiling.

### v2 H3 Hexagonal Parquet

Aggregated carbon stocks at H3 resolution 8 (~0.74 km²), partitioned by H3 resolution 0 (h0) cells.

**Path Pattern**: `v2/{dataset-name}/hex/h0={cell}/data_0.parquet`

| Dataset | Path prefix |
|---------|-------------|
| Irrecoverable carbon 2024 | `v2/irrecoverable-carbon-2024/hex/` |
| Irrecoverable carbon 2023 | `v2/irrecoverable-carbon-2023/hex/` |
| Irrecoverable carbon 2022 | `v2/irrecoverable-carbon-2022/hex/` |
| Irrecoverable carbon 2018 | `v2/irrecoverable-carbon-2018/hex/` |
| Vulnerable carbon 2024    | `v2/vulnerable-carbon-2024/hex/` |
| Vulnerable carbon 2018    | `v2/vulnerable-carbon-2018/hex/` |
| Vulnerable carbon 2010    | `v2/vulnerable-carbon-2010/hex/` |
| Manageable carbon 2024    | `v2/manageable-carbon-2024/hex/` |
| Manageable carbon 2018    | `v2/manageable-carbon-2018/hex/` |
| Manageable carbon 2010    | `v2/manageable-carbon-2010/hex/` |

### v2 Usage Examples

**DuckDB — compare irrecoverable carbon across years:**
```sql
SELECT
    year,
    SUM(carbon) / 1e9 AS total_Gt_C
FROM (
    SELECT 2024 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2024/hex/**/*.parquet')
    UNION ALL
    SELECT 2023 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2023/hex/**/*.parquet')
    UNION ALL
    SELECT 2022 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2022/hex/**/*.parquet')
    UNION ALL
    SELECT 2018 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2018/hex/**/*.parquet')
)
GROUP BY year ORDER BY year;
```

---

## v1 Data (2021 Release — Zenodo 4091029)

Covers years **2010 and 2018** with biomass, soil, and total components separately.

### v1 Cloud-Optimized GeoTIFFs

**Path Pattern**: `cogs/{category}_c_{component}_{year}.tif`

**Categories**: `irrecoverable`, `manageable`, `vulnerable`
**Components**: `biomass`, `soil`, `total`
**Years**: `2010`, `2018`

Examples:
- `cogs/irrecoverable_c_total_2018.tif`
- `cogs/manageable_c_biomass_2010.tif`
- `cogs/vulnerable_c_soil_2018.tif`

### v1 H3 Hexagonal Parquet

| Dataset | Path |
|---------|------|
| Irrecoverable carbon (total 2018) | `irrecoverable-carbon/hex/h0={cell}/data_0.parquet` |
| Vulnerable carbon (total 2018)    | `vulnerable-carbon/hex/h0={cell}/data_0.parquet` |

---

## Data Dictionary

### Hexagonal Parquet Schema

| Column | Type | Description |
|--------|------|-------------|
| `carbon` | Float | Total carbon stock in the hex cell (Mg C) |
| `h8` | String | H3 hexagon cell ID at resolution 8 |
| `h0` | String | H3 resolution 0 parent cell ID (partition key) |

**Units**: Megagrams of Carbon (Mg C). One Mg = 1 metric ton.

### Terminology

- **Irrecoverable Carbon**: Ecosystem carbon that, if lost, could not be recovered by 2050 through natural regeneration or restoration.
- **Vulnerable Carbon**: Total carbon stocks (biomass + soil) that are vulnerable to release upon land conversion.
- **Manageable Carbon**: Carbon stocks in ecosystems that can be managed by human activities (forestry, agriculture, conservation).

### v2 Corrections vs v1

The v2 (2025) update includes:
1. **Bug fix**: Corrected overestimation of irrecoverable carbon in boreal grasslands and shrublands in Asia (reduces 2018 total from 139.1 Gt to 135.3 Gt)
2. **Updated coastal extents**: Global Mangrove Watch 2022 and UNEP-WCMC 2022 for seagrasses and salt marshes
3. **Annual tracking**: New years 2022, 2023, 2024 for irrecoverable; 2024 for vulnerable and manageable
4. **Updated protection status**: Uses WDPA data through January 2023–October 2025

---

## Background & Methodology

The concept of "irrecoverable carbon" was developed to identify carbon stocks where prevention of loss is more effective than restoration. These areas represent the overlap between:

1. **High carbon density** — Significant climate mitigation value
2. **Low recoverability** — Cannot regenerate by 2050
3. **High threat** — Significant risk of conversion

The dataset integrates:
- Biomass data from multiple sources (Spawn et al., Walker et al.)
- Soil carbon from SoilGrids250m
- Ecosystem vulnerability assessments
- ESA CCI Land Cover for annual conversion tracking (v2 only)

extent.temporal: None (probe sample) widened to ['2010-01-01T00:00:00Z', '2024-01-01T00:00:00Z'] using README-stated coverage ['2010', '2024'].


US Census Geographic Boundaries

Cloud-optimized Parquet files of US Census TIGER/Line geographic boundaries for multiple years (2022, 2024, 2025), covering administrative levels including tracts, counties, states, congressional districts, and state legislative districts. Each level is provided both as native geometries with FIPS identifiers and as H3 hexagonal spatial index crosswalks at resolutions 8–10, enabling spatial joins between Census tabular data and H3-indexed datasets. The collection contains geometry and administrative identifiers only; demographic attributes are not included.

census h3 administrative-boundaries crosswalk

census-2024

GEOID

[admin_code] Census GEOID (composite FIPS code) uniquely identifying the geographic unit

INTPTLAT

[latlon] Internal point latitude (centroid) of the census unit

INTPTLON

[latlon] Internal point longitude (centroid) of the census unit

geom

[geometry] WKB polygon geometry of the census geographic unit

4 files sampled for this table

census-2024/cd/hex

[spatial_index] H3 resolution 10 index intersecting this congressional district

[spatial_index] H3 resolution 8 index intersecting this congressional district

[spatial_index] H3 resolution 9 index intersecting this congressional district

GEOID

[admin_code] Census GEOID for the congressional district

INTPTLAT

[latlon] Internal point latitude of the congressional district

INTPTLON

[latlon] Internal point longitude of the congressional district

[spatial_index] H3 resolution 0 partition key for this congressional district

16 files sampled for this table

census-2024/county/hex

[spatial_index] H3 resolution 8 index intersecting this county

GEOID

[admin_code] Census GEOID (state+county FIPS) for the county

INTPTLAT

[latlon] Internal point latitude of the county

INTPTLON

[latlon] Internal point longitude of the county

[spatial_index] H3 resolution 0 partition key for this county

16 files sampled for this table

census-2024/state/hex

[spatial_index] H3 resolution 8 index intersecting this state

GEOID

[admin_code] Census GEOID (state FIPS) for the state

INTPTLAT

[latlon] Internal point latitude of the state

INTPTLON

[latlon] Internal point longitude of the state

[spatial_index] H3 resolution 0 partition key for this state

16 files sampled for this table

census-2024/tract/hex

[spatial_index] H3 resolution 10 index intersecting this census tract

[spatial_index] H3 resolution 8 index intersecting this census tract

[spatial_index] H3 resolution 9 index intersecting this census tract

GEOID

[admin_code] 11-digit FIPS code (state+county+tract) for the census tract

INTPTLAT

[latlon] Internal point latitude of the census tract

INTPTLON

[latlon] Internal point longitude of the census tract

[spatial_index] H3 resolution 0 partition key for this census tract

16 files sampled for this table

census-2025

GEOID

[admin_code] Census GEOID for the state legislative district

INTPTLAT

[latlon] Internal point latitude of the legislative district

INTPTLON

[latlon] Internal point longitude of the legislative district

geom

[geometry] WKB polygon geometry of the state legislative district

2 files sampled for this table

census-2025/sldl/hex

[spatial_index] H3 resolution 10 index intersecting this lower state legislative district

[spatial_index] H3 resolution 8 index intersecting this lower state legislative district

[spatial_index] H3 resolution 9 index intersecting this lower state legislative district

GEOID

[admin_code] Census GEOID for the lower state legislative district

INTPTLAT

[latlon] Internal point latitude of the lower legislative district

INTPTLON

[latlon] Internal point longitude of the lower legislative district

[spatial_index] H3 resolution 0 partition key for this lower legislative district

13 files sampled for this table

census-2025/sldu/hex

[spatial_index] H3 resolution 10 index intersecting this upper state legislative district

[spatial_index] H3 resolution 8 index intersecting this upper state legislative district

[spatial_index] H3 resolution 9 index intersecting this upper state legislative district

GEOID

[admin_code] Census GEOID for the upper state legislative district

INTPTLAT

[latlon] Internal point latitude of the upper legislative district

INTPTLON

[latlon] Internal point longitude of the upper legislative district

[spatial_index] H3 resolution 0 partition key for this upper legislative district

13 files sampled for this table

Source Cooperative product page

## README.md
# US Census 2022 - Spatial Crosswalk (Tracts to H3)

This dataset provides a spatial index crosswalk between US Census Tracts (2022) and the H3 geospatial indexing system. It allows for efficient integration of census data with other H3-indexed datasets.

**Note:** This dataset currently contains only the **geometry and identifiers** (FIPS codes). It does *not* contain demographic attributes (population, income, etc.), which should be joined by FIPS code from the appropriate Census API or table.

## Source

- **Producer:** US Census Bureau (TIGER/Line Shapefiles)
- **Year:** 2022
- **Level:** Census Tract
- **Processing:** Boettiger Lab (H3 Indexing)

## Data Format

The dataset is partitioned by H3 resolution 0 index for efficient global querying.

- **Parquet:** `s3://public-census/year=2022/tracts-hex-z10.parquet`
- **H3 Resolution:** 10 (Average area ~0.015 km²)

## Data Dictionary

| Column | Type | Description |
|--------|------|-------------|
| `FIPS` | String | 11-digit Federal Information Processing Standards code uniquely identifying the census tract (State+County+Tract) |
| `h10` | List(String) | List of H3 resolution 10 indices that intersect with this tract |
| `STATE` | String | FIPS State Code |
| `COUNTY` | String | FIPS County Code |
| `year` | Integer | Census year (2022) |
| `geom` | Binary | WKB Geometry of the tract |

## Usage

Use this dataset as a "spine" to map tabular Census data to H3 hexagons.

1. **Get Census Data:** Download demographic data (e.g., ACS 5-year estimates) for 2022 tracts using the Census API or libraries like `tidycensus` / `cenpy`.
2. **Join:** Join your tabular data to this dataset on the `FIPS` column (ensure FIPS codes are formatted consistently as 11-character strings).
3. **Spatial Analysis:** Use the `h10` column to aggregate or correlate with other H3 datasets.

## Citation

U.S. Census Bureau. (2022). TIGER/Line Shapefiles. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html

extent.temporal: None (probe sample) widened to ['2022-01-01T00:00:00Z', '2022-01-01T00:00:00Z'] using README-stated coverage ['2022', '2022'].

extent.spatial.bbox: omitted; spatial reference is categorical via [census-2024.geom (geometry), census-2024.INTPTLAT (latlon), census-2024.INTPTLON (latlon), census-2024.GEOID (admin_code), census-2024/cd/hex.h10 (spatial_index), census-2024/cd/hex.h9 (spatial_index), census-2024/cd/hex.h8 (spatial_index), census-2024/cd/hex.h0 (spatial_index), census-2024/cd/hex.INTPTLAT (latlon), census-2024/cd/hex.INTPTLON (latlon), census-2024/cd/hex.GEOID (admin_code), census-2024/county/hex.h8 (spatial_index), census-2024/county/hex.h0 (spatial_index), census-2024/county/hex.INTPTLAT (latlon), census-2024/county/hex.INTPTLON (latlon), census-2024/county/hex.GEOID (admin_code), census-2024/state/hex.h8 (spatial_index), census-2024/state/hex.h0 (spatial_index), census-2024/state/hex.INTPTLAT (latlon), census-2024/state/hex.INTPTLON (latlon), census-2024/state/hex.GEOID (admin_code), census-2024/tract/hex.h10 (spatial_index), census-2024/tract/hex.h9 (spatial_index), census-2024/tract/hex.h8 (spatial_index), census-2024/tract/hex.h0 (spatial_index), census-2024/tract/hex.INTPTLAT (latlon), census-2024/tract/hex.INTPTLON (latlon), census-2024/tract/hex.GEOID (admin_code), census-2025.geom (geometry), census-2025.INTPTLAT (latlon), census-2025.INTPTLON (latlon), census-2025.GEOID (admin_code), census-2025/sldl/hex.h10 (spatial_index), census-2025/sldl/hex.h9 (spatial_index), census-2025/sldl/hex.h8 (spatial_index), census-2025/sldl/hex.h0 (spatial_index), census-2025/sldl/hex.INTPTLAT (latlon), census-2025/sldl/hex.INTPTLON (latlon), census-2025/sldl/hex.GEOID (admin_code), census-2025/sldu/hex.h10 (spatial_index), census-2025/sldu/hex.h9 (spatial_index), census-2025/sldu/hex.h8 (spatial_index), census-2025/sldu/hex.h0 (spatial_index), census-2025/sldu/hex.INTPTLAT (latlon), census-2025/sldu/hex.INTPTLON (latlon), census-2025/sldu/hex.GEOID (admin_code)]. Add bbox once the placename → bbox gazetteer covers it.


California Geological Survey (CGS) Datasets

Cloud-optimized publication of California Geological Survey (CGS) Geologic Data Map (GDM) series products, co-produced with the U.S. Geological Survey. Currently includes the Sierra Nevada Earth Science Atlas (GDM 009), comprising 6,221 geologic-unit polygons across 268 distinct map units for California's Sierra Nevada region. Data are derived from the MapUnitPolys feature class of the source File Geodatabase, reprojected to WGS84, and published as GeoParquet, PMTiles, and H3-indexed Parquet.

geoparquet pmtiles h3 geology geologic-mapping sierra-nevada

Dataset files (1 x .filegdb)

Source Cooperative product page

## README.md
# California Geological Survey Datasets

Cloud-optimized publication of California Geological Survey (CGS) Geologic Data Map (GDM) series products, hosted on the NRP Nautilus S3 bucket `public-cgs`.

Public URL: <https://s3-west.nrp-nautilus.io/public-cgs/>

## Available collections

- **[Sierra Nevada Earth Science Atlas (GDM 009)](sierra-nevada-atlas/stac-collection.json)** — collaborative CGS/USGS geologic mapping of California's Sierra Nevada. Currently includes the geologic unit polygon layer; remaining layers (faults, landslides, neotectonics, geophysical anomalies, geochronology, mineralization) tracked in [data-workflows#149](https://github.com/boettiger-lab/data-workflows/issues/149).

## Sierra Nevada Earth Science Atlas — Map Unit Polygons

Geologic-unit polygons for the Sierra Nevada region. 6,221 features across 268 distinct map units, derived from the `MapUnitPolys` feature class of the source File Geodatabase. Reprojected from NAD83 / California Albers (EPSG:3310) to WGS84 (EPSG:4326).

### Assets

| Asset | URL |
|---|---|
| GeoParquet | <https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/map-unit-polys.parquet> |
| PMTiles | <https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/map-unit-polys.pmtiles> |
| H3 hex (res 10) | `https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/map-unit-polys/hex/h0=*/data_0.parquet` |
| Map-unit descriptions | <https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/description-of-map-units.parquet> |

### DuckDB example

```sql
INSTALL spatial; LOAD spatial;
INSTALL httpfs; LOAD httpfs;

-- Geologic units with full names, ages, and standardized rock classes
SELECT
  p.MapUnit,
  d.Name,
  d.Age,
  d.GeoMaterial,
  COUNT(*)         AS n_polygons,
  SUM(p.Shape_Area) AS area_m2
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/map-unit-polys.parquet') p
LEFT JOIN read_parquet('https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/description-of-map-units.parquet') d
  USING (MapUnit)
GROUP BY p.MapUnit, d.Name, d.Age, d.GeoMaterial
ORDER BY area_m2 DESC
LIMIT 10;
```

### MapLibre GL JS example

PMTiles `source-layer` is **`map-unit-polys`** (last segment of `--dataset`).

```js
map.addSource('snesa-units', {
  type: 'vector',
  url: 'pmtiles://https://s3-west.nrp-nautilus.io/public-cgs/sierra-nevada-atlas/map-unit-polys.pmtiles'
});
map.addLayer({
  id: 'snesa-units-fill',
  source: 'snesa-units',
  'source-layer': 'map-unit-polys',
  type: 'fill',
  paint: {
    'fill-color': ['match', ['get', 'MapUnit'],
      'water', '#3b82f6',
      'gl', '#cbd5e1',
      'aft', '#a16207',
      /* default */ '#9ca3af'
    ],
    'fill-opacity': 0.7,
    'fill-outline-color': '#1f2937'
  }
});
```

## Source

- **Producer:** California Geological Survey + U.S. Geological Survey
- **Original release:** GDM_009-SNESA-Data.zip, 2026 (v1 data; companion report expected late 2026)
- **Source URL:** <https://www.conservation.ca.gov/cgs/sierra-nevada-atlas>
- **License:** Public domain (state/federal coproduction)

## Citation

> California Geological Survey and U.S. Geological Survey (2026). *Sierra Nevada Earth Science Atlas, v1.* Geologic Data Map No. 9. California Geological Survey, Sacramento, CA. <https://www.conservation.ca.gov/cgs/sierra-nevada-atlas>

license: Declared public domain as a state/federal co-production (California Geological Survey + U.S. Geological Survey); no formal SPDX identifier assigned.


Conservation Policy Spending in Federal Legislation

Federally funded conservation restoration and resilience projects under two major US legislative acts: the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). Sourced from conservation.gov, the dataset covers over 2,100 project locations across the United States and territories. Available in cloud-optimized formats including GeoParquet, FlatGeobuf, and PMTiles.

conservation federal-funding restoration resilience land-management land-use policy bipartisan-infrastructure-law inflation-reduction-act

Dataset files (2 x .geojson)

Source Cooperative product page

## README.md
# Federally Funded Restoration & Resilience Projects

Based on data on https://apps.conservation.gov/atbatlas/explorer/

## [Bipartisan Infrastructure Law Restoration & Resilience Projects](https://www.conservation.gov/datasets/58ccf74d49a8493a917dc1f81c2e534a_0/about)

## [Inflation Reduction Act Projects](https://www.conservation.gov/datasets/bc8b539412284f3e987cd1f0c1c71c3e_0/about)

providers: role [producer, licensor] for 'conservation.gov (USDA)' is an AI guess — Assumed USDA as producer and licensor based on the conservation.gov domain; README does not explicitly state the agency or license terms.. Verify before flipping to _state: reviewed.


California Protected Areas Database

Spatial database of California's protected areas including parks, reserves, and conservation lands. Version 2025b contains approximately 26 million polygon features representing designated protected areas across the state. Available in cloud-optimized Parquet, Shapefile, and other spatial formats.

protected-areas conservation land-use boundaries

Dataset files (9 x .parquet)

Source Cooperative product page