Data reaches the ground too late
Downlink, routing, and post-processing add latency when every minute affects response quality.
Orbital AI for urgent response
Process raw data in orbit before delay becomes damage
LeoTrek is building a distributed platform for AI workloads across low Earth orbit satellite clusters, so wildfire monitoring and other time-critical observation missions can move from raw data to action in minutes.
Mission focus
Fast orbital processing for wildfire monitoring and rapid alerts.
Deploy models, fuse sensing modalities, and prioritize urgent insights before full downlink.
In-orbit inference
Region-anchored monitoring
Minutes, not hours
Mission
Close the gap between sensing and action
Earth observation creates enormous value only when it arrives soon enough to change a decision. LeoTrek shifts compute closer to the sensor so satellite missions can support fast-moving events, persistent regional awareness, and new space-native software workflows.
Run urgent inference in orbit
Coordinate AI workloads directly across LEO assets and prioritize what matters most.
Actionable intelligence sooner
Deliver faster alerts, compressed insights, and better use of limited bandwidth.
Beyond wildfires
The same architecture fits disaster response, infrastructure monitoring, and other urgent EO workloads.
Why Now
The cost of waiting is measurable
Wildfires are the clearest example of why orbital compute matters. When insight arrives late, damage compounds across human safety, infrastructure, and climate.
Human and economic toll
Smoke exposure and damaged infrastructure already turn each fire season into a public-health and budget crisis.
Escalating climate events
Larger burn areas and massive CO2 release show these events are compounding, not stabilizing.
Minutes reshape outcomes
Detection latency changes containment options, dispatch quality, and how much land is ultimately lost.
More sensors, stronger onboard compute, and pressure for faster response make this the right moment to move EO intelligence from batch pipelines toward space-native execution.
Platform
Real-time intelligence in space
LeoTrek is designing a space-native orchestrator that lets EO developers deploy, schedule, and evolve EO workloads with far less operational friction.
01
Distributed EO processing in LEO with performance guarantees
Run inference, filtering, and prioritization directly across orbiting satellite clusters, so urgent compute happens before full downlink.
02
Region-anchored monitoring
Keep continuity over fixed areas of interest and surface time-critical events as new passes occur.
03
Multi-source fusion and orchestration
Coordinate tasks across the constellation and combine thermal, optical, and contextual streams into one decision layer.
04
Mission-free deployment
Deploy selected models and workflows without rebuilding the stack for each mission profile, then return prioritized alerts and outputs to the right teams.
05
Simulation and validation
Test mission logic, workload placement, and model behavior before deployment so operators can validate latency, throughput, and operational tradeoffs earlier.
06
Off-the-shelf AI services
Offer reusable optimization and inference services such as pruning, quantization, and deployment-ready packaging without requiring every team to build custom software stacks from scratch.
Scenario Preview
Local orbit simulation
A compact view of orbit nodes, assigned workflows, and computation moving inside the target area before full downlink.
Simulation clock
Current UTC
Iberian Peninsula
Business model potential:
spare onboard compute can become an application layer for satellite operators, enabling AI-as-a-service
and custom mission software beyond a single vertical use case.
Use Cases
Built for more than one emergency
Wildfire monitoring is the immediate wedge, but LeoTrek is relevant wherever Earth observation decisions are time-sensitive and bandwidth is scarce.
Wildfire response
Prioritize hotspots, fire spread indicators, and response-relevant updates without waiting for full raw-data transfer.
Disaster monitoring
Support floods, storms, and post-event damage assessment with faster orbital analytics.
Critical infrastructure
Monitor energy, transport, and industrial systems where earlier anomaly detection improves maintenance, planning, and incident response.
Continuous monitoring
Maintain regular visibility over locations of interest and detect meaningful change over time instead of treating every pass as an isolated event.
Research
Grounded in the Edge-Cloud Continuum Research
LeoTrek builds on published work in scheduling, serverless workflows, simulation, cost modeling, and hardware acceleration for AI workloads.
Hyperdrive
Scheduling serverless functions across orbital and ground-adjacent compute layers. IEEE/ACM SEC, 2024.
Read paper
IEEE EDGE 2025
Simulator demo
Stardust
A scalable simulator for multi-layer distributed computing environments. IEEE EDGE, 2025.
Read paperDatabelt
A continuous data path for serverless workflows across distributed compute tiers. Journal of Systems Architecture, 2025.
Read paperCosmos
A cost model for serverless workflows spanning multiple compute layers. IEEE SmartComp, 2025.
Read paperGaia
Hybrid hardware acceleration for serverless AI in distributed compute environments. IEEE/ACM BDCAT, 2025.
Read paperCurrus
A compound AI approach to distributed vehicle trajectory reconstruction. International Conference on the Internet of Things, 2025.
Read paper
Contact
Open to pilot programs, operator partnerships, and research collaboration
LeoTrek is interested in pilot deployments, operator partnerships, commercial validation, and research collaboration around time-critical orbital intelligence.
LEO operators
Emergency response
Research partners
Investors
Part of the European deep-tech ecosystem
