No single satellite sensor can do everything. Optical cameras go blind in clouds or at night. Some sensors can detect crop stress but can't explain why. Others see through clouds but miss important visual detail. As the idiom goes, more sensors are better than one.
Combining data from multiple satellites is technically hard and expensive. Teams spend months on custom pipeline "plumbing" just to handle different data formats, failed downloads, and scaling issues, leaving little time for real science. Many end up cutting scope entirely, limiting themselves to a single data source to avoid the headache.
Evolving the Geospatial Workflow
Given the exponential growth in satellite data and relatively low ROI for downstream geospatial applications, it's important to point out that the bottleneck is not sales. It's the pipeline. Between the total acquisition cost of data, the engineering resources it takes to build and maintain pipelines, plus growing competition - its critical to focus on differentiation and offering the best available product using the best available data, no matter the source(s).
We're working to evolve the geospatial sector by solving for traditional obstacles and drastically improving data efficiency.
Here are a few of the things we're changing:
One framework for any environment(s). If you need to process Sentinel-2 data from an Open Telekom Cloud, Landsat data from AWS US-West-2, and proprietary data in a Google Cloud for a multi-source pipeline, Tilebox enables you to execute the same workflow in each environment.
Parallel processing at scale. One job can automatically split into thousands of smaller tasks, no manual batching required.
Automatic error recovery. If a download fails, Tilebox retries it. A 10,000-scene job that breaks at scene 8,743 resumes from there, not from the beginning.
Runs on your infrastructure. You control the compute and cost; Tilebox handles the coordination.
Adding a new data source used to mean weeks of integration work. With Tilebox, it's just a new dataset name and one additional line of code.
client.dataset("<slug>")
For EO teams, that's the difference between multi-source data being a real advantage, or just a project that never gets built.
One workflow across three environments
Check out this video for a sample workflow.
