About Mercator Labs
We build geospatial foundation models that understand satellite imagery and location data.
Similar to models like AlphaEarth and Prithvi, but dramatically cheaper.
Our models learn from:
Satellite ImageryMulti-spectral data from Landsat, Sentinel, and other sources.
Pre-trained on terabytes of global imagery.
Location Embeddings
Geographic coordinates encoded as dense vectors.
Captures spatial relationships and context.
Self-Supervised Learning
Masked autoencoders for efficient pre-training.
No labeled data required for foundation training.
Fine-tune for your specific task with minimal labeled data.
What can you do?
Our foundation models can be adapted for:
- Land cover classification and semantic segmentation
- Change detection and temporal analysis
- Crop monitoring and yield prediction
- Disaster response (floods, fires, infrastructure damage)
- Urban planning and development tracking
- Environmental monitoring and conservation
- Geospatial similarity search
- Location-based intelligence and forecasting
Higher accuracy than ImageNet-pretrained models on geospatial tasks.
Less training data required. Faster convergence.
Pricing & Access
We're currently in private beta.
Email pinak@mercatorlabs.xyz to get access.
We offer flexible pricing based on your use case:
Research? Commercial deployment? Custom fine-tuning?
Let's talk.
FAQ
- What is a geospatial foundation model?
- A geospatial foundation model is a large neural network pre-trained on massive amounts of satellite imagery and location data. Similar to how GPT understands language, our models understand Earth observation data. We pre-train on unlabeled data from across the globe, which means you can fine-tune for your specific task with 10-100x less labeled data than training from scratch.
- How is this different from AlphaEarth or other models?
- We optimize for cost efficiency. Models like AlphaEarth demonstrate incredible capabilities, but require significant compute resources that most researchers and small companies cannot access. We focus on maximizing performance per dollar to make geospatial AI genuinely accessible.
- What kind of data do your models work with?
- Any georeferenced raster data. This includes multi-spectral satellite imagery (Landsat, Sentinel-2), SAR data, elevation models, and more. Our location embeddings can encode any latitude/longitude coordinate into a dense vector representation.
- Do I need labeled data to use your models?
- Not for the foundation model itself, which comes pre-trained. For fine-tuning to your specific application, you will need some labeled examples, but typically 10-100x fewer than training from scratch. This is the core value proposition of foundation models.
- What tasks can I fine-tune for?
- Any geospatial analysis task. Classification, semantic segmentation, object detection, temporal change detection, regression tasks like crop yield prediction. You can also use the embeddings directly for similarity search and clustering applications.
- How do I get started?
- Email pinak@mercatorlabs.xyz with a description of your use case. We will provide API access or model weights, along with documentation and example notebooks to help you get started quickly.
- Can I use this for commercial applications?
- Yes. We offer commercial licensing with pricing based on your deployment scale and requirements. Contact us to discuss your specific needs.
- Do you offer custom model training?
- Yes. If you have specific requirements like custom geographic regions, different spectral bands, or specific temporal resolutions, we can train custom models. Get in touch to discuss.
- What about inference speed and deployment?
- Our models are optimized for efficient inference on standard GPU infrastructure and major cloud platforms (AWS, GCP, Azure). If you have specific hardware constraints, we can help with optimization.
- Is this research-friendly?
- Absolutely. We offer academic pricing and actively support research projects. Many critical problems in climate science, agriculture, and disaster response require accessible tools. Enabling that research is part of our mission.
- What is your goal?
- To democratize geospatial AI. Currently, advanced Earth observation capabilities are limited to organizations with large compute budgets. But researchers working on climate change, sustainable agriculture, and disaster response need these tools too. We are working to close that gap.
Questions? Reach out: pinak@mercatorlabs.xyz