🗣️ Introducing the Duality AI + LunateAI Challenge- Geospatial Object Detection: Rural Buildings! Train a model to detect difficult detection instances, such as a low number of pixels or weak feature responses, in rural aerial imagery, to win 🏆PRIZES🏆 and 🤩RECOGNITION🤩.
This is the first competition in the 🌎Geospatial Kaggle Challenge Series🌏, which will explore how geospatial-based digital twins can train an AI model for real-world applications.
Duality is excited to be partnering with LunateAI, a high-end advisory business founded by the award-winning, industry-recognized global leader Dr. Nadine Alameh to usher in a new era of geospatial impact in conjunction with advances in computing and AI. Lunate helps government and industry leaders 🤔 rethink, 💡redesign, and 📝 execute transformative geospatial strategies using AI, cloud, and Lunate’s unparalleled global expertise. Read more about the company here: https://lunateai.com/
🏠 For this Kaggle competition, competitors will train an object detection model to identify buildings in a rural environment, using aerial imagery. 🏠
The real-world testing data focuses on difficult detection instances, such as: 🔎 Low number of pixels 🏘️ Weak feature responses 🌤️Varied lighting
Competitors must generate varied synthetic data that addresses key difficulties of the real-world dataset using a FalconCloud simulation with controllable parameters.
Competitors can control: ✅ Variety of in-class of objects ✅ Lighting and shadow conditions ✅ Environmental occlusions ✅ and more!
Join the competition and learn how digital simulation using data such as geospatial information can help tackle difficult real-world challenges.
Can AI models trained solely on 100% synthetic data achieve top-tier accuracy in real-world object detection?
👉 @sergio-sanz-rodriguez just proved it while winning Duality AI’s Synthetic-to-Real Object Detection Challenge using Falcon-generated imagery. His model achieved perfect real-world detection accuracy without a single real image in the training loop.
In this blog, Dr. Sanz walks us through his method, which includes the design and training of an advanced pipeline to achieve 100% detection accuracy. His full technical breakdown covers: 📍 Synthetic-only training 📍 Data augmentation with an ensemble learning approach for better generalization 📍 Custom occlusion generation 📍 A Faster R-CNN model fine-tuned with Falcon generated data 📍 And much more!
Excuse the lag, it's from the real-time inference from the webcam 👀 . Did you know that YOLOv11 added Streamlit for live object detection straight from your webcam?
NEW ARTICLE: "Detecting Beyond Sight: Building AI-Enabled SAR Intelligence with Synthetic Data"
Synthetic Aperture Radar (SAR) reveals what optical sensors can’t. AI can turn that information into actionable intelligence—but only with the right training data.
In our latest blog, we explore how Falcon’s new virtual SAR sensor solves the SAR data bottleneck for AI development. As the newest addition to Falcon’s sensor library, it models radar returns with precision—including azimuth, range resolution, signal intensity, and noise. This Falcon-specific, GPU-accelerated raytraced SAR model is exposed via Falcon’s Python API, giving teams precise, control over radar wave propagation and enabling physically grounded, highly customizable, and user-friendly SAR simulation.
The result? High-fidelity, automatically labeled synthetic SAR imagery from any scenario—on demand. No custom setup. No external workflows. Just mission-ready data for building AI models across defense, disaster response, agriculture, intelligence, and beyond.
