Planet Labs' open satellite feed
summary
Planet Labs provides an open, high-resolution satellite imagery feed for disaster response that can be programmatically accessed and analyzed using modern data tools. By leveraging DuckDB, GDAL, and QGIS, users can efficiently process large-scale geospatial datasets, including basemaps and post-event imagery, to monitor natural disasters. This workflow demonstrates how Cloudflare-hosted Parquet and JSON metadata can be used to automate the retrieval and mosaicking of satellite imagery for rapid crisis assessment.
key points
- The Planet Labs disaster feed offers diverse imagery from several constellations, including the high-resolution SkySat and the hyperspectral Pelican, which captures 426 bands of light.
- Efficient processing of geospatial data is achieved by using DuckDB for metadata querying and GDAL for compressing large GeoTIFF files into cloud-optimized formats using WEBP compression.
- Usable Data Masks (UDM) are provided alongside imagery to identify obstructed areas, allowing analysts to distinguish between clear ground views and cloud-covered regions.
who should read it
Geospatial analysts, data engineers, and disaster response coordinators who want to learn how to programmatically retrieve and process open satellite imagery.
discussion positive
consensus
Commenters agree that high-resolution satellite imagery is prohibitively expensive for small nonprofits, creating a gap between available low-res free data and costly commercial options. There is a general consensus that the Earth Observation market is fragmented, with various trade-offs between cost, resolution, and revisit rates. Users suggest that negotiating with account managers, exploring alternative aerial platforms like balloons or drones, and utilizing specific archives like SkyWatch or Microsoft Planetary Computer are viable paths to reduce costs.
top insight
The most valuable perspective is the strategy of using a tiered data approach to 'remove the haystack' by using low-resolution Sentinel-1 and Nimbo data to identify specific areas of interest. Once these high-probability zones are isolated, the organization can then justify the cost of high-resolution imagery or escalate the findings to parties with larger budgets. This approach transforms the problem from a wide-area surveillance task into a targeted evidentiary task.
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