Geospatial analytics startup Descartes Labs raises $30M Series B led by March Capital; the company uses machine learning to derive data from satellite images
Descartes Labsis announcing a $30 million Series B this morning in a large round led by March Capital.
Context & Ripple Effects
Descartes Labs' $30 million Series B, led by March Capital, lands just months after rival Orbital Insight raised its $50 million Series C with Sequoia — two machine-learning shops turning satellite imagery into predictive data, funded back-to-back within the same year.
The round positions Descartes Labs to scale beyond research demos; the following year it exits beta with a platform built on NASA and other public data sources, confirming the bet that raw imagery plus models could become a product.
First-order effects
- March Capital gains an early position in geospatial analytics, while Descartes Labs gets the runway to industrialize its satellite-imagery models ahead of its beta exit.
Second-order effects
- Orbital Insight now faces a directly funded competitor chasing the same enterprise buyers, pushing both toward differentiation by vertical rather than by model quality alone.
Third-order effects
- With the analytics layer (Descartes Labs, Orbital Insight) and the visualization layer (Carto's later $61 million Series C) each raising institutional rounds, satellite imagery is hardening into a standard input for commercial predictive modeling — a stack investors are funding piece by piece.
The trend: Venture capital is assembling a geospatial analytics stack, funding machine-learning companies that convert public satellite imagery into predictive data products.