Federating Multiple GIS Data Sources with STAC

Federating distributed geospatial archives through the SpatioTemporal Asset Catalog (STAC) standard eliminates the need for redundant ingestion pipelines. By querying multiple cloud providers and government repositories through a unified metadata layer, analysts can retrieve, deduplicate, and stack raster data without relocating raw files. This architecture directly supports modern Remote Sensing & Raster Analysis workflows where data volume and provider fragmentation require standardized discovery protocols.

The primary friction points in multi-catalog federation are schema divergence, overlapping item IDs, and inconsistent asset naming conventions. The following workflow resolves these issues programmatically using pystac-client for discovery and stackstac for alignment and stacking.

flowchart LR
    A["Catalog 1<br/>(Planetary)"] --> C["Dedup by<br/>item ID"]
    B["Catalog 2<br/>(Earth Search)"] --> C
    C --> D["Filter items<br/>with target asset"]
    D --> E["stackstac:<br/>align CRS & stack"]
    E --> F["Unified<br/>xarray DataArray"]

Querying and Deduplicating Across Catalogs

When querying multiple endpoints, identical scenes often appear across catalogs due to shared upstream providers. Deduplication must occur at the item ID level before any raster operations begin. The pystac-client library handles pagination automatically, but explicit error handling prevents a single unreachable endpoint from halting the entire pipeline.

import pystac_client

CATALOG_URLS = [
    "https://planetarycomputer.microsoft.com/api/stac/v1",
    "https://earth-search.aws.element84.com/v1"
]

bbox = [-122.5, 37.5, -122.0, 38.0]  # [west, south, east, north]
time_range = "2023-06-01/2023-08-31"
collections = ["sentinel-2-l2a"]

federated_items = []
seen_ids = set()

for url in CATALOG_URLS:
    try:
        client = pystac_client.Client.open(url)
        search = client.search(
            bbox=bbox,
            datetime=time_range,
            collections=collections,
            max_items=10  # Limit for demonstration; increase for production
        )
        for item in search.items():
            if item.id not in seen_ids:
                seen_ids.add(item.id)
                federated_items.append(item)
    except Exception as e:
        print(f"Catalog query failed for {url}: {e}")

print(f"Retrieved {len(federated_items)} unique STAC items.")

Normalizing Asset Keys and Stacking

Provider-specific asset dictionaries rarely align. One catalog may expose Sentinel-2 red band data as red, while another uses B04. Before stacking, filter items to guarantee the target asset exists, then pass the validated list to stackstac. This library automatically aligns differing coordinate reference systems (CRS) and resolutions into a single xarray.DataArray, which is essential for building Cloud-Native Spatial Data Lakes that compute directly against remote storage.

import stackstac
import xarray as xr

TARGET_ASSET = "B04"

# Filter items that actually contain the requested asset key
valid_items = [item for item in federated_items if TARGET_ASSET in item.assets]

if not valid_items:
    raise ValueError("No returned items contain the target asset key.")

# Stack into a unified xarray DataArray
datacube = stackstac.stack(
    valid_items,
    assets=[TARGET_ASSET],
    bounds_latlon=bbox,   # bbox is in lon/lat; use bounds_latlon (bounds expects output-CRS coords)
    rescale=False,        # Keep raw digital numbers; set True for reflectance
    chunksize=1024,       # Optimize for Dask memory management
    fill_value=0          # Handle partial tile overlaps cleanly
)

print(datacube)

Debugging Federation Failures

Federation pipelines frequently break at the metadata-to-raster boundary. Use these targeted steps to isolate and resolve common issues:

  1. Missing Asset Keys: STAC catalogs evolve. If valid_items returns empty, inspect a sample item’s dictionary: print(federated_items[0].assets.keys()). Update TARGET_ASSET or implement a fallback mapping dictionary.
  2. CRS Misalignment Errors: stackstac defaults to the first item’s CRS. If you encounter RasterioIOError or projection warnings, explicitly force alignment: stackstac.stack(..., epsg=32610) (replace with your target UTM zone).
  3. Memory Overflows During Stacking: Large spatiotemporal extents exceed local RAM. Enable lazy evaluation by verifying the output is a dask.array (default in stackstac). Compute only when necessary: result = datacube.mean(dim="time").compute().
  4. Network Timeouts: Catalog APIs rate-limit or drop connections under heavy load. Wrap client.search() in a retry decorator or use requests session pooling. The official pystac-client documentation details timeout configuration and pagination limits.

For standardized metadata validation across all endpoints, reference the STAC Specification to verify required fields (datetime, geometry, assets) before ingestion. This ensures your federation logic remains resilient as providers update their schemas.