Inferensys

Service

Planetary-scale Satellite Imagery AI Processing

Engineering high-throughput AI pipelines to process petabytes of satellite imagery from constellations like Sentinel and Landsat for continent-scale intelligence.
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PLANETARY-SCALE PROCESSING

The Challenge of Petabyte-Scale Geospatial Intelligence

Engineering high-throughput AI pipelines to extract intelligence from petabytes of satellite imagery for defense, climate, and urban planning.

Processing petabytes from constellations like Sentinel and Landsat requires more than cloud storage. It demands specialized AI infrastructure capable of continent-scale object detection, land cover classification, and change detection with sub-meter accuracy.

  • High-Throughput Pipelines: Architect dataflows that ingest, preprocess, and analyze multi-terabyte daily volumes without bottlenecks.
  • Scalable Model Serving: Deploy and orchestrate thousands of concurrent inference endpoints for models like SAM 2 and YOLO across hybrid cloud and edge.
  • Deterministic Analytics: Transform probabilistic AI outputs into trusted, actionable intelligence layers for integration into ArcGIS and custom geospatial intelligence platforms.

Without engineered AI pipelines, raw satellite data remains a cost center, not an intelligence asset. We build systems that deliver structured insights at planetary scale.

ENTERPRISE VALUE

Business Outcomes of Scalable Satellite AI Pipelines

Our planetary-scale AI processing pipelines deliver measurable ROI by transforming petabytes of raw satellite data into actionable intelligence, enabling faster, data-driven decisions for environmental monitoring, defense, and infrastructure planning.

From Discovery to Production

Typical Project Timeline and Deliverables

A transparent breakdown of the phased approach and key outputs for our planetary-scale satellite imagery AI processing engagements, designed for enterprise technical leaders.

Phase & Key DeliverablesTimelineCore ActivitiesOutcome

Discovery & Feasibility Assessment

1-2 weeks

Requirements gathering, data source evaluation, technical scoping

Project roadmap & architecture proposal

Data Pipeline Engineering & Model Selection

2-4 weeks

Ingestion pipeline setup, model benchmarking (e.g., SAM 2, YOLO), initial PoC

Validated data flow & model performance baseline

Custom Model Training & Fine-tuning

3-6 weeks

Dataset curation, distributed training, hyperparameter optimization

Production-ready model with >95% target accuracy

High-Throughput Inference System Deployment

2-3 weeks

Scalable API development, containerization (Docker/K8s), load testing

Deployed pipeline processing >1M km²/day

Integration & MLOps Lifecycle Setup

1-2 weeks

CI/CD pipeline, monitoring dashboards, integration with client GIS (e.g., ArcGIS)

Fully operational system with retraining triggers

Knowledge Transfer & Ongoing Support

Ongoing

Documentation, team training, optional SLA for maintenance

Autonomous internal operation capability

Planetary-scale Processing

Frequently Asked Questions on Satellite Imagery AI

Get clear answers on timelines, costs, and technical capabilities for deploying AI to analyze petabytes of satellite data.

A standard deployment for a continent-scale object detection or land cover classification pipeline takes 2-4 weeks from kickoff to production-ready inference. This includes data pipeline setup, model fine-tuning on your domain, and integration with your GIS (e.g., ArcGIS) or data lake. Complex multi-model workflows or custom foundation model training extend this to 6-8 weeks. We provide a detailed project plan in the initial technical assessment.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.