Written by Synthetic Data in Machine Learning applications
Building highly detailed 3D assets and environments for synthetic data production. These tailored datasets empower AI models to learn, adapt, and excel in real-world scenarios:
Simulating Temperature Variations in River Flow
Digital Humans for AI-Powered Humanitarian Solutions
“Selfie” Project:
This innovative project leverages AI to analyze images taken in mirrors, with a specific focus on tracking victims of human trafficking. By creating hyper-realistic 3D environments and digital humans, we generate synthetic data that trains AI systems to recognize and interpret patterns in mirror selfies.
Large-Scale Port City Environment for Satellite Imaging AI Training
This highly detailed 3D environment of a bustling port city is meticulously crafted to train machine learning models for satellite imaging and geographic analysis. The scene combines multiple tools and data sources for optimal realism and functionality.
This model not only accelerates AI training efficiency but also provides a cost-effective and scalable solution for global-scale analysis and applications.
Procedural Tiled Terrain Generation for 2 Terapixel Satellite Imaging
Objective:
Design and simulate a massive 6×6 km tiled terrain to train AI models for censoring sensitive data captured by 2 terapixel satellite cameras.
This project sets a benchmark for high-resolution data simulation, paving the way for ethical and secure satellite imaging solutions
Automatic Safeguard System for Freight Train Wheel Defect Detection
Objective: Develop a cutting-edge automatic safeguard system to identify and prevent dangerous wear and tear on freight train wheels, minimizing the risk of catastrophic accidents like the Palestine, Ohio case.
Project Overview:
The system leverages high-resolution cameras installed on train tracks to continuously monitor freight wagon wheels during transit. Using advanced AI and machine learning algorithms, the system identifies potential defects, such as cracks, flat spots, or uneven wear, and provides early warnings to prevent accidents.
AI-Powered Weapon Detection System for Schools
Objective: Develop an AI-enhanced ultrasound imaging system to detect concealed weapons such as knives and guns in schools, ensuring student and staff safety through early threat detection.
AI-Powered Satellite Imaging for Tracking Vehicles on Terrain
Objective: Develop a terrain and built-area simulation environments for training AI models to detect and track various vehicles. This system focuses on enhancing satellite-based surveillance to address threats in volatile regions.
Synthetic Data for Vessel Recognition by Navy Drone Ships
3D ocean scenes with various ship models (military, cargo, fishing boats) to generate synthetic data for training AI to recognize and classify vessels. This data enabled Navy drone ships to distinguish between vessel types in real-time, improving navigation and safety.
Threat Recognition Training
3D models of Soviet/Russian mobile missile systems, including realistic textures and configurations, to generate synthetic data for AI training. This data enabled satellites and aerial reconnaissance systems to identify and classify potential threats with greater accuracy.
Battlefield Uniform Recognition: Generating Synthetic Data for AI Training.
3D models of soldiers wearing diverse military uniforms, representing different countries, ranks, and camouflage patterns. These scenes were used to train AI systems to identify and differentiate between combatants on the battlefield, enhancing situational awareness and decision-making.
Infrared Facial Recognition: Synthetic Data for Night-Time Security Cameras.
Infrared simulated portraits with diverse facial features, lighting conditions, and angles to create synthetic datasets for training AI. This improved the accuracy of security systems in identifying individuals in low-light and nighttime environments.
Human Channel: Synthetic Data Simulation for Skin Examination.
I developed high-fidelity 3D models of human skin with various tones, textures, and conditions to simulate dermatological examinations. This synthetic data helped train AI algorithms for advanced skin analysis, improving diagnostics and product testing in personal care applications.
Architectural Elements in Satellite Imaging and Texture Indexing for Recognition
Satellite imaging and AI-driven recognition of architectural elements rely on advanced texture indexing and spatial analysis to differentiate and classify structures accurately.