Why One AI Isn’t Enough to Detect a World Full of Species

Why One AI Isn’t Enough to Detect a World Full of Species

When you’re walking through a landscape filled with native wildflowers, towering grasses, invasive shrubs, and a range of animals, you’re seeing an ecosystem shaped by complexity and change. Each species plays a role, carries unique features, and appears differently depending on the season, lighting, or region.

Now imagine asking a single AI model to recognise all of them equally well. It wouldn’t work.

Just like ecosystems thrive on diversity, ecological monitoring at scale requires a diversity of AI approaches - each tuned to different species, datasets, and environments.

Built on 70,000+ Hectares of Experience

These AI models didn’t emerge overnight. They’ve been trained on hundreds of thousands of hectares of drone, satellite, and field imagery made possible only through the trust and collaboration of our customers and the expertise of ecologists, data scientists, and field teams on the ground

Every image flown, every field record shared, every restoration zone monitored has contributed to this intelligence. Our AI doesn’t just represent innovation, it represents collective progress across dozens of regions, use cases, and ecosystems.

So to the mining teams, restoration professionals, consultants, and government partners who’ve helped make this possible - thank you. Your local knowledge and ecological insights continue to shape the most advanced ecological AI system in the world.

Ecosystem Intelligence Stack

In ecology, no two regions or even patches of land are the same. You’ll find different weed threats in WA compared to NSW, just as species in Abu Dhabi’s mangroves differ from those in the Blue Mountains. From subtle leaf shapes to seasonal flowering, different image resolutions, the challenge is massive.

To tackle it, we use multiple AI approaches:

• Supervised AI: Trained on tens of millions of seed images by our ecologists. Great when you want high precision on target species.
• Self-supervised AI: Learns on its own from huge image sets - without needing human labels. It’s resilient, scalable, and less biased by labelling gaps.
• Binary Classifiers: Trained on a yes/no basis to detect very specific features (like “is this a kangaroo?”).
• Weakly-supervised AI: A new frontier. This model learns from partial labels, including “not this” feedback. It helps refine AI understanding across ecosystems, especially when full data is patchy.

Each method has its strength. Used together, they form a dynamic, multi-layered approach to ecological monitoring, allowing us to scale species detection across millions of hectares while adjusting for messy, real-world conditions.

Think of it Like a Team of Specialists

No one model can do everything. Some species look vastly different across geographies or stages of growth. Lighting, resolution, soil reflectance, canopy cover, or flowering stages - all affecting visibility.

In supervised AI, we need hundreds of thousands of examples to train a model to accurately identify a species. But some species are rare, or only visible from certain angles or in short seasonal windows. That’s where self-supervised models step in - uncovering structure where other models fall short.

Each AI method complements the others, forming a layered ecosystem of intelligence. But this intelligence doesn’t exist in isolation - it’s continuously shaped by human expertise. From ecologists verifying image data to field teams tagging rare sightings, people are essential to every step of the process.

This system lets us scale across millions of hectares, while still being species-specific enough to deliver results regulators, land managers, and ecologists can trust.

The Right Eye for The Right Job

Why does this matter? Because restoration projects depend on evidence that the right species are returning, that weeds are under control, and that ecosystems are self-sustaining for years to come.

Our multi-AI approach delivers:

• Better accuracy across varied environments
• More resilience in challenging conditions (e.g., shaded areas, sparse growth, overlapping canopies)
• Faster detection of threats like invasive species
• Higher confidence in audit-ready outputs

This translates into reduced compliance delays, fewer return visits to the field, and stronger narratives that support progressive certification.

In short, better AI means faster, more reliable, and more cost-effective restoration. But the real value comes when AI is paired with ecological expertise - a combination that turns insight into impact.

Diversity Needs Diversity

Nature isn’t uniform - and our AI shouldn’t be either.

From vibrant wildflowers to stubborn invasive weeds, every landscape tells a different story. That’s why we’ve built AI that doesn’t just detect diversity - it learns from it. Using a blend of self-supervised models that uncover hidden patterns, and highly targeted detectors trained for specific species, we’ve created a system as varied and adaptable as the ecosystems it serves.

And we’re doing it alongside people who know these ecosystems best - field ecologists, land stewards, and community experts whose observations guide and validate our models.

Because biodiversity doesn’t come from uniformity - it thrives in variety. Our job is to recognise it, track it, and restore it. Accurately, affordably, and at scale.

Restoration at Scale, Powered by AI That Understands the Land

Our multi-AI system doesn’t just help us detect plants. It helps restore landscapes, reduce costs, and speed up compliance. It turns complex visuals into structured, auditable insights that regulators, environmental teams, and land managers can trust.

It’s not just automation - it’s augmentation. We’re building tools that empower people to work smarter, scale their impact, and drive long-term ecological outcomes.

And the best part? These systems keep learning. From the tiniest flower to the trickiest weed, every image makes our ecosystem intelligence just a little bit smarter.

Because in a world that’s anything but uniform, we need AI that’s anything but generic.