Engineering Solutions for Enhanced Earth Observation (EO)
RC Fornax provides disciplined, mission-led engineering services across the Earth Observation lifecycle: from concept development and data strategy through to algorithm development, operational workflows, geographical information system (GIS) platform integration, and end-user decision support. Our work spans satellite, airborne, drone-based, and in-situ sensing, with particular strength in challenging, low-observability environments where standard EO methods are limited by weather, darkness, clutter, or insufficient spatial resolution.
Our approach combines remote sensing science, AI-enabled analytics, radar phenomenology, and operational engineering to convert raw multi-source data into actionable intelligence. We work across optical and SAR domains, with a strong emphasis on maritime monitoring, anomaly detection, tracking, and application-specific information enhancement.
Drawing on experience from our prior work for a public agency, we develop practical EO solutions that integrate satellite data, sparse in-situ measurements, AI models, and GIS-based user interfaces into coherent, scalable workflows. This enables customers to move from fragmented data exploitation toward reliable, explainable, and operationally relevant EO services.
Our Approach
Mission-led EO engineering focused on real operational questions such as event detection, vessel monitoring, activity prediction, and hotspot characterisation rather than just model development in isolation.
Multi-source sensing strategy combining free and commercial EO, SAR and optical imagery, AIS and non-AIS vessel analysis, drone-based sensing, and in-situ validation methods.
Application-specific information enhancement using radar phenomenology, sparse measurements, and AI to improve utility of low-resolution or noisy EO data, particularly for maritime targets and difficult-weather conditions.
Operational GIS delivery through user-facing platforms designed to ingest data, visualise insights, compare detections, and support future expansion into broader EO and non-EO sources.
Evidence-driven maturation through targeted micro-pilots, validation campaigns, statistical confidence building, and TRL uplift focused on end-user usability and repeatability.
Our Core EO Capabilities
EO Systems Engineering & Solution Architecture
We apply structured engineering methods to design EO solutions that link user need, sensing architecture, algorithm performance, validation evidence, and operational deployment.
EO concept development and feasibility studies across satellite, airborne, drone, and in-situ sensing layers.
Mission architecture design for monitoring, detection, tracking, and predictive analytics use cases.
Trade-space assessment of sensor types, acquisition timing, data availability, and processing approaches.
Workflow definition from raw data ingestion to analytics, GIS presentation, and end-user action.
Scalable architectures designed for integration of EO and non-EO sources such as coastal radars and local sensing platforms.
Maritime Monitoring, Detection & Tracking
We develop EO analytics for maritime surveillance, with emphasis on detecting and characterising vessels that may not be visible through conventional cooperative systems.
Vessel detection from SAR and optical EO data, including Sentinel-1, Sentinel-2 and commercial satellites (like ICEYE and Umbra) based pipelines.
Filtering of detected vessels using AIS correlation to distinguish cooperative from non-cooperative maritime traffic.
Vessel size classification, orientation estimation, and tracking support.
Estimation of likely origin and arrival patterns using historical trajectory data and extrapolation logic.
Hotspot and event-density analysis to optimise surveillance focus, tasking windows, and data purchasing strategy.
SAR Processing, Radar Phenomenology & Information Enhancement
We specialise in extracting greater operational value from SAR by treating it as a phenomenological sensing modality rather than a conventional image source.
SAR-specific engineering for all-weather, day-night monitoring in conditions where optical imagery is unreliable.
Application-specific super-resolution and information enhancement for maritime targets.
Development of our patented algorithm (GB Patent (filed) Ref: 2522644.0) called PRISM (Precision Resolution via Intelligent Sparse Measurements) to combine RF physics, sparse drone-radar observations, and AI-based inference.
Canonical scatterer modelling and phenomenology-led interpretation of radar returns.
Detection-domain optimisation approaches that improve target detectability without relying purely on pixel-level fidelity.
Drone, In-Situ & Micro-Pilot Validation
We complement EO analytics with targeted local sensing and field validation to improve confidence, calibration, and interpretability.
Micro-pilot design using corner reflectors, fiducial markers, and controlled test environments.
Drone-based radar experimentation to support phenomenology studies and PRISM-style sparse measurement concepts.
Collection of local evidence to validate satellite interpretations and investigate small-target observability.
Resolution and registration validation through coastal and land-based pilot sites.
Progressive TRL maturation through iterative testing, evidence capture, and operational stakeholder feedback.
Programme Delivery & EO Capability Maturation
We support EO programmes from early-stage innovation through to deployable capability.
Road-mapping from low-TRL concept to usable operational demonstrator.
Structured experimentation to quantify reliability, latency, confidence levels, and practical constraints of EO-based detection workflows.
Stakeholder engagement with end users to shape algorithm priorities, data strategies, and platform usability.
Delivery planning for phased capability build-up across analytics, data integration, validation, and interface maturity.
Bridging innovation and operations by turning technically novel EO methods into decision-support tools with measurable utility.
Whether the challenge is maritime surveillance, small-target detection, radar information enhancement, or multi-source geospatial fusion, RC Fornax brings together sensing strategy, algorithm engineering, AI, validation, and user-focused delivery to create enhanced EO solutions that are scientifically credible, operationally relevant, and ready to mature toward fielded capability.
