In the modern era of Geospatial Intelligence (GEOINT), the analysis of satellite and drone imagery has become a strategic cornerstone for defense, security, and intelligence operations.
The ability to detect key objects—vehicles, critical facilities, infrastructure, and logistical movements—provides a decisive operational edge where time and precision are paramount.
State-of-the-art computer vision models, such as YOLOv8, have proven to be exceptional tools for object detection in complex imagery. However, training them requires vast volumes of annotated data.
For national security agencies or international coalitions, this creates a fundamental dilemma: how do you train powerful AI models without sharing classified imagery or compromising data sovereignty?
The answer lies in Federated Learning. Our AI solution for defense, enables different entities to collaborate on building cutting-edge AI models without ever transferring a single image.
With our platform, each organization keeps its data at its source and only shares model updates, thus preserving security, confidentiality, and absolute control over sensitive information.
The power of GEOINT is measured by its ability to turn a flood of images into actionable intelligence. Faced with millions of images generated daily by satellites and drones, analysts require AI systems that can:
Identify relevant objects in seconds.
Filter out noise and prioritize critical information.
Generate early warnings of significant changes on the ground.
Provide broad, simultaneous coverage of multiple areas of interest.
From border surveillance to the protection of critical infrastructure, object detection is essential. Yet, no single organization possesses the diverse datasets needed—spanning different climates, terrains, and scenarios—to train a globally effective model. Collaboration is key, but sharing classified imagery is not a viable option.
In defense and intelligence, satellite images are highly sensitive strategic assets. Sharing this data presents unacceptable risks:
National Security: Images can reveal military capabilities, strategic locations, or operational patterns.
Legal & Regulatory Restrictions: Many countries explicitly prohibit the transfer of defense-related data to third parties.
Cyber Threats: Any data transfer process opens a window for potential attacks.
Data Sovereignty: Relinquishing classified images means losing control over a strategic asset.
The traditional approach has been to train models in isolation. This creates a clear limitation: each model is trained on smaller, homogeneous datasets, resulting in less accurate and less generalizable AI systems.
Our Sherpa.ai platform solves this dilemma using Federated Learning, a disruptive approach that changes the rules of the game.
The process is as simple as it is powerful:
Model Distribution: A baseline model (e.g., YOLOv8) is distributed to each participating entity.
Local Training: Each organization trains the model on its own classified images, within its own secure systems.
Sharing Knowledge, Not Data: Instead of sharing the images, only the model updates (weights, gradients) are shared.
Secure Aggregation: Our platform aggregates these updates into a global model that integrates the knowledge from everyone.
Redistribution: The improved global model is sent back to each participant, and the cycle continues.
This iterative process ensures that classified images never leave the secure perimeter of each organization, yet the global model is enriched by the diverse data of all allies.
Imagine a scenario with three allied nations:
Ally A has images of ground vehicles in desert environments.
Ally B has naval imagery, with ships and activity in ports.
Ally C possesses images of light aircraft in mountainous areas.
Traditionally, they could not share this imagery. With our Sherpa.ai platform:
Each ally trains the model locally on their classified dataset.
Only the model updates are shared securely.
The federated aggregation results in a global YOLOv8 model capable of recognizing vehicles across multiple environments.
The impact is immediate: the joint model achieves significantly higher accuracy levels than any single entity could obtain alone.
Adopting our AI solution for defense in the GEOINT domain offers a distinct competitive advantage:
Maximum Security: Classified images are never transferred or exposed.
Data Sovereignty: Each organization maintains absolute control over its information.
Secure Collaboration: Fosters multinational cooperation without compromising strategic secrets.
Operational Efficiency: Eliminates redundancies and optimizes training with heterogeneous data.
Scalability: Each new participant strengthens the global model.
Continuous Improvement: The model is updated and refined iteratively.
Our Federated Learning platform is engineered for critical environments like defense and intelligence. Its key features include:
Secure Central Orchestrator: Coordinates the flow of model updates.
Multi-Framework Compatibility: Supports PyTorch, TensorFlow, and detection models like YOLOv8.
Enhanced Privacy: Integrates techniques like secure aggregation and homomorphic encryption.
Monitoring and Control: A management dashboard to track the federated model's evolution in real-time.
Multi-Node Scalability: Allows for the simultaneous integration of dozens of participants.
Thanks to this architecture, our Sherpa.ai solution is unique in its ability to combine security, efficiency, and performance in the most demanding contexts.
Our technology is already being applied in multiple relevant use cases:
Border Surveillance: Detecting movements of vehicles or personnel in sensitive areas.
Critical Infrastructure Protection: Identifying changes at airports, ports, or energy facilities.
Multinational Reconnaissance: Joint training among allies without the exchange of classified images.
Disaster Analysis: Collaboration between agencies to assess damage without compromising national security data.
Federated Learning for object detection is just the first step. Our vision is to evolve towards a collaborative and autonomous geospatial intelligence that combines:
Multimodal Models: Integrating computer vision and language models to generate automatic reports.
Intelligent Agents: Systems that not only detect but also interpret and propose actions.
Continuous Training: Models that learn in real-time as new imagery arrives.
Our Sherpa.ai platform is ready to lead this transformation, offering our clients and allies a decisive strategic advantage.
The future of GEOINT does not depend on accumulating more images, but on the ability to extract value from them without compromising security or data sovereignty.
Our AI solution, Sherpa.ai, enables agencies and allies to collaboratively train object detection models like YOLOv8, preserving the confidentiality of classified images and maximizing operational effectiveness. The result is more robust, accurate, and useful models for defense and international security operations.
The key difference is data location. In a traditional centralized approach, all data must be transferred to and stored in a single location (even a secure one), creating a single point of failure and forcing organizations to relinquish direct control.
With Federated Learning, the raw, classified data never moves from its original secure environment. Only abstract mathematical model updates are shared, preserving absolute data sovereignty and minimizing security risks.
A federated model can achieve accuracy that is highly comparable to, and sometimes even better than, a centrally trained model. By learning from a more diverse and varied range of data from multiple partners—data that could never be legally or securely combined—the resulting model becomes more robust and generalizable. It performs better on unseen, real-world scenarios, which is often more valuable than performance on a single, large but homogenous dataset.
Our Sherpa.ai platform is built with a security-first approach, employing multiple privacy-enhancing technologies (PETs). We use techniques like Secure Aggregation, where the central server only sees the combined result of all updates, not the contribution from any single participant. For maximum security, we can also integrate advanced methods like Homomorphic Encryption, which allows the server to process model updates while they remain fully encrypted.
Our platform is designed for flexibility and interoperability with existing infrastructure. It supports standard machine learning frameworks like PyTorch and TensorFlow and can be deployed either on-premise or in a secure private cloud. It is compatible with a wide range of models, including state-of-the-art object detectors like YOLOv8, allowing organizations to leverage their existing tools and expertise.
The efficiency gains are substantial. It eliminates the time-consuming, risky, and often prohibited process of data de-classification, annotation, and transfer. More importantly, it allows for continuous and collaborative model improvement. Allies can work together to build a far superior AI asset that is constantly updated, ensuring that all participants have access to a state-of-the-art model without the redundant effort of each training their own limited version from scratch.
Ready to enhance your surveillance and reconnaissance capabilities without compromising your data?
Contact our experts and request a personalized demo of the Sherpa.ai platform.