How Viga ET Is Rewriting the Rules of Satellite Intelligence

In the world of Geospatial Intelligence, the hardest problem is no longer collecting data. Satellites already generate an overwhelming flood of imagery every single day.The real challenge lies elsewhere: making sense of this data fast enough to matter.

At Viga ET, we decided to confront one of the deepest bottlenecks in the industry—the dependence on slow, expensive, and rigid manual labeling. That journey has now reached an important milestone.

Out of 900+ teams nationwide, Viga ET has been selected as a Top 6 Finalist in the prestigious NCIIPC AI Grand Challenge—a recognition that validates not just our solution, but a fundamentally different way of thinking about satellite intelligence.

The Challenge We Chose: Problem Statement 3 (PS-3)

Visual Search, Retrieval, and Detection in Satellite Imagery

At its core, PS-3 addresses a classic needle-in-a-haystack problem. Analysts are expected to find rare, evolving, and sometimes unknown objects hidden inside massive volumes of satellite data—often under extreme time pressure.

The official challenge description sounds simple:

“Develop a system to automatically search, identify, and detect objects in satellite images, producing labelled datasets that can be further trained and fine-tuned.”

But behind this simplicity lie three deeply intertwined challenges:

• Visual Search at planetary scale

• Object Detection without prior assumptions

• Automated Annotation without human bottlenecks

Solving all three together is what separates incremental improvement from a true breakthrough.

The Industry Standard vs. The Viga Way

The Conventional Approach

Traditionally, teaching an AI system to recognize an object in satellite imagery looks like this:

• Collect thousands of images

• Manually draw bounding boxes

• Train and fine-tune models

• Wait weeks or months

• Repeat for every new object or threat

This approach is slow, expensive, and fundamentally reactive.

The Question We Asked

What if finding objects didn’t require thousands of labels?What if one image was enough?

That single question changed everything.

The Breakthrough: From Labeling to Instant Search in Satellite Intelligence

Instead of building yet another object detector, we engineered a Generic Visual Search Engine.

Old Way➡️ Annotate 5,000 images➡️ Train model➡️ Deploy⏳ Weeks to months

The Viga Way➡️ Upload 1–5 reference images (“chips”)➡️ Instant detection across large satellite datasets⚡ Real-time

This leap was made possible by combining Few-Shot Learning with Deep Metric Learning—a paradigm shift away from classification and toward similarity.

Viga’s Core Idea: Deep Metric Learning

Rather than asking:

“Is this a tank?”

Our system asks:

“How similar is this object to the image you just showed me?”

1. Thinking in Embeddings, Not Labels

Imagine a library where books aren’t arranged by title, but by meaning.Mysteries cluster together. Cookbooks live elsewhere.

Viga’s AI does something similar.

Every object in satellite imagery is converted into a mathematical signature—an embedding. Objects that look alike (in shape, texture, scale, and semantics) are placed close together in this space. Dissimilar ones are pushed far apart.

The system doesn’t memorize object names. It learns visual relationships.

2. Few-Shot Visual Search in Action

• An analyst uploads a single reference image

• The system scans massive satellite archives

• Objects that are visually close are retrieved

• Bounding boxes are generated with high precision

No retraining. No waiting. No massive datasets.

Why Metric Learning Beats Data Augmentation

Many solutions attempt to solve data scarcity by creating more data—synthetic images, augmentations, simulations. While useful, this approach is still slow and computationally heavy.

Viga’s approach is different:

• ❌ No synthetic data generation

• ❌ No retraining cycles

• ✅ Smarter comparison, not more data

• ✅ Instant adaptability to unseen objects

Instead of expanding datasets, we compress intelligence into similarity itself.

Why This Matters (Beyond the Top 6)

This achievement isn’t just about recognition in a national challenge. It represents a structural shift in how Geospatial Intelligence systems can be built:

• Speed: From months of preparation to seconds of search

• Agility: New threats can be detected instantly

• Scalability: Works across object types and domains

• Efficiency: Eliminates the biggest bottleneck—manual labeling

Perhaps most importantly, it proves that deep-tech innovation is no longer confined to traditional defense giants.

Viga ET’s roots lie in media, VFX, simulation, and digital twins—technologies originally built for cinema and interactive worlds. Today, those same tools are helping solve some of the hardest problems in national security and intelligence.

We’re Just Getting Started 🚀

This milestone is a validation of our belief:If you reimagine the problem, the solution changes entirely.

To our engineering team—this achievement belongs to you. To the ecosystem—this is a signal of what’s possible when disciplines converge.

The future of satellite intelligence will not be slower and heavier. It will be faster, adaptive, and fundamentally smarter.

And we’re excited to help build it.