SERVICES
Physics-Based Synthetic EO/IR Datasets for Defense AI Training and Validation
Accelerate Algorithmic Warfare Readiness With Physics-Fidelity EO/IR Synthetic Imagery
Generate high-fidelity synthetic datasets to train your algorithms in mission-critical edge cases that are impossible, classified, or too costly to capture in the field.
Our physics-based EO/IR synthetic image sets provide the high-fidelity data you need to train, test, and validate your algorithms in a fraction of the time. By bridging the data gap between simulation and reality, we help you deploy mission-ready AI with total confidence in its performance across the entire electromagnetic spectrum.
How ThermoAnalytics Can
Help Synthetic EO/IR Datasets
Why Physics-Based Synthetic Data?
While generative AI can produce realistic looking images, our datasets are grounded in 30 years of thermal physics and heat transfer expertise. This ensures that every pixel reflects real-world thermodynamics, material properties, and environmental interactions. The result is a dataset that doesn’t just look right – it behaves right, ensuring your models transfer seamlessly from the lab to the field.
Pixel-Aligned Truth
Images can include automated ground truth, semantic segmentation, bounding boxes, and pose information, eliminating manual labeling errors.
Signature Accuracy
Unlike generative AI, our images are grounded in 30 years of thermal physics, accounting for transient heat transfer from internal heat sources, material properties, and environmental conditions.
The “Edge Case” Advantage
Simulate rare atmospheric conditions, specific times of day, or adversarial signatures that real-world testing can’t reach.
Comprehensive Multi-Spectral Libraries
True sensor fusion requires more than just high-resolution images; it requires spectral correlation. Our models and datasets span the electromagnetic spectrum. These datasets are pixel-aligned across wavebands, allowing developers to train multimodal algorithms that maintain object persistence regardless of the sensor type. Whether you are modeling solar glint in the visible spectrum or complex engine signatures in the thermal infrared, our target models provide a consistent, physics-correlated view of the battlespace.
Visible (0.4–0.7 µm)
High-fidelity textures for visual-based recognition.
SWIR (0.9–1.7 µm)
Performance through haze and low-light environments.
MWIR (3–5 µm)
Precise plume and engine signature detection.
LWIR (8–14 µm)
Diurnal thermal cycles and background clutter analysis.
Target Domains
Our synthetic image sets can be delivered in industry-standard formats including COCO, YOLO, and Pascal VOC with automated, pixel-perfect metadata for semantic segmentation and bounding boxes. Beyond the images themselves, we provide deep-level technical parameters, allowing users to inject custom sensor effects. This flexibility ensures that the synthetic data doesn’t just look like the real world; it behaves exactly like your specific hardware would in the field.
Ground Systems
Urban and rural environments, armored vehicles, and camouflaged targets.
Aviation & Space
Aerial interceptors, satellite-to-ground monitoring, and high-speed plume dynamics.
Maritime
Ship signatures against complex sea states and solar glint.
Human Activity
Realistic thermal signatures for search and rescue or security monitoring.
Technical Specs & Integration
Our synthetic image sets can be delivered in industry-standard formats including COCO, YOLO, and Pascal VOC with automated, pixel-perfect metadata for semantic segmentation and bounding boxes. Beyond the images themselves, we provide deep-level technical parameters, allowing users to inject custom sensor effects. This flexibility ensures that the synthetic data doesn’t just look like the real world; it behaves exactly like your specific hardware would in the field.
Formats
Exportable in COCO, YOLO, Pascal VOC, or custom JSON/XML.
Variable Parameters
Instantly adjust sensor noise (NETD), blur (MTF), resolution, and field of view.
Physics Grounding
Full access to underlying thermal data, including surface temperatures and heat flux.
Products to Support
Synthetic EO/IR Datasets
Target Model Database
The Foundation of Synthetic Diversity
Our extensive digital asset library provides high-fidelity building blocks for complex scene generation. This database goes beyond simple geometry to include measured surface properties, weather profiles, and geospatial data. From military and industrial vehicles to maritime craft, infrastructure, and even buried objects, our models provide the signature diversity required to train robust recognition systems across any environment or mission set.
The Industry Standard for Thermal Signature Simulation
MuSES (Multi-Service Electro-optic Signature) is the industry-standard software for predicting the thermal and multi-spectral infrared (IR) signatures of vehicles, aircraft, and buildings. Developed for both defense and commercial applications, MuSES combines high-fidelity thermal simulation with advanced electro-optical physics. It allows engineers to model how an object appears to infrared sensors by accounting for internal heat sources, complex environmental reflections, and atmospheric conditions.
Coupling and Process Automation for Mass Data Generation
CoTherm is an advanced process automation and CAE coupling software designed to automate the coupling between thermal solvers and scene rendering tools. It eliminates the manual bottlenecks of simulation, allowing you to generate hundreds of thousands of physics-aligned images with varying parameters in a fraction of the time. By orchestrating complex workflows, CoTherm makes it commercially viable to build the massive, diverse training sets required for modern deep learning and ATR development.
