stac_dem_bc

stac_dem_bc serves British Columbia’s LidarBC digital elevation model collection — nearly 100,000 GeoTIFFs on the provincial objectstore as of July 2026 — as a SpatioTemporal Asset Catalog (STAC), searchable by location and time from the rstac R package, QGIS (v3.42+), or any STAC-compliant client. The API endpoint is https://images.a11s.one.

The catalog refreshes monthly: a scheduled GitHub Actions workflow detects new tiles on the objectstore and appends them incrementally, with each month’s changes landing as a commit to data/ — a git audit trail of what was added, removed, and validated. How the automation works, its failure modes, and where the evidence lives are documented in scripts/, alongside a plain-language guide to the concepts behind the pipeline (COG, STAC, pgstac, validation caching).

Query the collection

Use bcdata to define an area of interest, then query the stac-dem-bc collection for DEM tiles intersecting it. Below: all DEMs covering the Bulkley River watershed group between 2018 and 2020.

aoi <- bcdata::bcdc_query_geodata("freshwater-atlas-watershed-groups") |>
  bcdata::filter(WATERSHED_GROUP_NAME == "Bulkley River") |>
  bcdata::collect() |>
  sf::st_transform(crs = 4326)

date_start <- "2018-01-01T00:00:00Z"
date_end <- "2020-12-31T00:00:00Z"

# use rstac to query the collection
q <- rstac::stac("https://images.a11s.one/") |>
  rstac::stac_search(
    collections = "stac-dem-bc",
    intersects = jsonlite::fromJSON(
      geojsonsf::sf_geojson(
        aoi, atomise = TRUE, simplify = FALSE
      ),
      simplifyVector = FALSE
    ) |> (\(x) x$geometry)(),
    datetime = paste0(date_start, "/", date_end)
  ) |>
  rstac::post_request()

# get details of the items
r <- q |>
  rstac::items_fetch()

# burn the results locally so we can serve it instantly on index.html builds
saveRDS(r, "data/stac_result.rds")

r <- readRDS("data/stac_result.rds")
# build the table to display the info
tab <- tibble::tibble(
  url_download = purrr::map_chr(r$features, ~ purrr::pluck(.x, "assets", "image", "href"))
)

DEM download links. NOTE: To view all columns in the table - please click on one of the sort arrows within column headers before scrolling to the right.

QGIS Data Source Manager (v3.42+)

QGIS 3.42 added native STAC support — connect directly to the catalog and filter by the current map view. See Lutra Consulting’s STAC-in-QGIS blog post for a walk-through.

Connecting to https://images.a11s.one

Using the field of view in QGIS to filter results

Sister collections on the same endpoint

The same images.a11s.one STAC API serves several complementary BC collections:

• stac_uav_bc — UAV imagery, organized by watershed
• stac_airphoto_bc — historic airphoto thumbnails (1963–2019)

Roadmap

The big one landed in 2026: the catalog is now self-updating (#23) — the goal open since the first build — and the July catch-up grew the collection from 58k to ~98k fully-validated items. Still ahead:

• Incremental registration — the post-sync pgstac registration is a full reload today (~80 minutes at current scale); upserting only each month’s new items brings it to seconds. Tracked in the infrastructure repo.
• Upstream-deletion handling (#28) — propagate objectstore removals to the catalog; on hold until the September run shows whether recent removals were renames.
• URL-encoded item hrefs (#25, fix in review as PR #26) — 90 legacy items carry literal spaces in their download links, which strict HTTP clients reject.
• True footprint geometry (#2) — recalculate per-item footprints to exclude no-data pixels rather than using bounding boxes; gives accurate spatial-overlap queries.
• Structured logging + performance benchmarking (#6) — instrument the pipeline so build performance is quantifiable across runs.
• uv-based Python dependency management (#16) — the CI workflow already installs with uv; migrate the local conda environment to match.

Browse open issues for the full backlog.

License

MIT.