Planet makes available select imagery for major disaster events, including major earthquakes, floods, storms, wildfires, and human-made disasters. These are available here in a STAC Catalog under a CC-BY-NC license. See the Planet Disaster Data page for more information.
# Planet Crisis Response — Hurricane Melissa, Jamaica (2025) (llms.txt)
> High-resolution Planet SkySat imagery of Hurricane Melissa's impact on Jamaica. Melissa made
> landfall in southwestern Jamaica on 28 October 2025 as a catastrophic Category 5 hurricane.
> Post-event ~0.8 m SkySat collects (3–4 November 2025) over the landfall zone are paired with
> an October 2023 SkySat pre-event baseline (the most recent cloud-free SkySat of these towns
> before the storm) for before/after comparison, organised by affected location
> (Savanna-la-Mar, Black River). Part of the Planet Crisis Response Program
> (https://www.planet.com/disasterdata/). Imagery © Planet Labs PBC, CC-BY-NC-4.0
> (non-commercial).
This file describes the event for machines and AI assistants. It is one event in the
Disaster Data umbrella catalog (https://data.source.coop/planet/disasterdata). Built with
Portolan (https://portolan-sdi.org).
Base URL: https://data.source.coop/planet/disasterdata/hurricane-melissa-2025
Root catalog: https://data.source.coop/planet/disasterdata/hurricane-melissa-2025/catalog.json
Browse: https://browser.portolan-sdi.org/#/external/data.source.coop/planet/disasterdata/hurricane-melissa-2025/catalog.json
Human landing page: https://source.coop/planet/disasterdata/hurricane-melissa-2025
## Structure
- `catalog.json` → two child sub-catalogs, each split by affected location:
- **post-event/** (21 scenes, 3–4 Nov 2025) — Collections `savanna-la-mar` (14) and
`black-river` (7).
- **pre-event/** (41 scenes, Oct 2023 baseline) — Collections `savanna-la-mar` (22,
2023-10-21) and `black-river` (19, 2023-10-11). A ~2-year baseline: the most recent clear
SkySat of these towns before the storm.
- Items live at `<phase>/<location>/items/<scene>/<scene>.json` with their COGs beside them.
- STAC-GeoParquet indexes (one row per scene): each Collection has `items.parquet`, and
`post-event/items.parquet` / `pre-event/items.parquet` aggregate all scenes for that phase.
## Items & assets
- One Item per scene; Item id = the Planet SkySat scene id (e.g. `20251103_132926_ssc1d3_0024`).
- Asset keys per Item:
- `visual` — true-colour RGB ortho COG (uint8, ~0.8 m), display-ready
- `pansharpened` — pansharpened BGRN ortho COG, for analysis
- `udm2` — Usable Data Mask 2 (clear / cloud / shadow / haze + confidence)
- `udm` — Unusable Data Mask (single band)
- Asset hrefs are absolute public https URLs to the COGs (no account or API key required).
## Data access
Least-cloudy post-event scenes over a town, with their COG URLs (only the parquet is read):
```sql
INSTALL spatial; LOAD spatial; INSTALL httpfs; LOAD httpfs;
SELECT id, datetime, assets['visual']['href'] AS visual_cog
FROM read_parquet(
'https://data.source.coop/planet/disasterdata/hurricane-melissa-2025/post-event/savanna-la-mar/items.parquet')
ORDER BY properties['eo:cloud_cover'];
```
Open a COG window without downloading the whole file:
```python
import rasterio
from rasterio.windows import from_bounds
with rasterio.open(visual_cog) as ds: # /vsicurl streams only the needed bytes
rgb = ds.read(window=from_bounds(*bbox, ds.transform))
```
Before/after: compare a `post-event` scene against the `pre-event` (Oct 2023) scene over the
same town — the pre/post Collections share the `savanna-la-mar` / `black-river` structure.
## Explore with AI
Point Claude Code (https://claude.com/claude-code) or another agent at this llms.txt and ask
it to, e.g., "build a before/after swipe map of Savanna-la-Mar" or "find the clearest
post-event SkySat scene over Black River."
## Notes & license
- Non-commercial license: CC-BY-NC-4.0. Attribution: © Planet Labs PBC — Crisis Response Program.
- Contact: crisis-response@planet.com
- COGs are cloud-optimized: read overviews for quick previews rather than downloading whole files.
- Coordinates are lon/lat WGS84 (EPSG:4326) in STAC metadata; each COG carries its own
projected CRS (`proj:code`).