Taha Ghaznavi, CPO at Nabat.ai, explains how satellite intelligence, autonomous drones and field-validated AI are helping the GCC restore and manage natural ecosystems at scale
Governments and organisations across the GCC are increasingly turning to geospatial AI to transform ecosystem restoration from a largely observational exercise into a measurable, verifiable process. By integrating satellite data, autonomous drones, field-validated AI models and ecological expertise, these technologies can help identify suitable restoration sites, execute interventions and track their long-term impact.
In this interview, Taha Ghaznavi, Chief Product Officer at Nabat.ai, discusses why the region has become an important testing ground for AI-powered nature management, how geospatial intelligence is supporting large-scale mangrove restoration in Abu Dhabi, and why natural ecosystems should be managed with the same rigour as other critical national infrastructure.
Interview Excerpts
How is geospatial AI helping governments and organisations in the GCC move from environmental monitoring to measurable ecosystem restoration outcomes?
For a long time, monitoring and restoration sat in separate workflows. A government agency would commission satellite imagery to understand where degradation was happening, and a separate team would run the planting or rehabilitation program on the ground. The two rarely talked to each other in real time. What’s changed is that geospatial AI now closes that loop. At Nabat, we think about it as a single pipeline: map the ecosystem, assess its condition, plan an intervention, restore it, monitor what happens next, and verify the outcome against a baseline. Each stage feeds the next. A government program owner isn’t just getting a map anymore; they’re getting a system that tells them where to intervene, executes part of that intervention through autonomous seeding and survey drones, and then tracks survival and growth over months and years. That last part, verification, is what actually makes restoration measurable rather than aspirational.
Anyone can report a number of seeds planted. Far fewer can show, with field-validated AI models, what fraction of that effort turned into living, growing ecosystems. That’s the shift governments in this region are pushing for, and it’s the one we built the platform around.
Why is the region emerging as an important testing ground for AI-powered geospatial intelligence and nature management solutions?
A few things are converging here that don’t happen together very often. First, there’s genuine national commitment with hard targets attached – the UAE’s goal of 100 million mangroves by 2030 is a good example, and it sits inside a broader climate neutrality commitment for 2050. Targets like that create real demand for tools that can prove progress, not just describe it. Second, the environments themselves are difficult in ways that force better engineering. Coastal and marine ecosystems here deal with extreme heat, hypersaline conditions, and tidal dynamics that make naive remote-sensing approaches fall over quickly. If a model can identify a mangrove seedling reliably in this environment, it has to solve harder problems than it would almost anywhere else. Third, the region has spent the last few years building serious AI and space infrastructure – sovereign investment in compute, satellite constellations, and geospatial expertise sits right alongside the environmental ambition. That combination of capital, infrastructure, and ecological urgency is rare, and it’s exactly why we’re seeing the Gulf become a place where geospatial AI for nature gets stress-tested before it scales elsewhere.
What role do satellite data and autonomous systems play in improving the accuracy and scalability of ecosystem restoration projects?
They play different and complementary roles. Satellites give you scale and a historical archive – they can establish a baseline across thousands of hectares and let you track change over years. Drones give you resolution – multispectral and hyperspectral imagery at close range, LiDAR for canopy height and biomass, and the ability to validate what the satellite is suggesting at the ground level. Used together, you get something neither can deliver alone: a baseline you trust at scale, and a verification layer precise enough to catch what’s happening at the level of an individual seedling. Autonomous systems add a third layer – seeding drones that can deliver an intervention directly once a site has been assessed as suitable, rather than waiting for a separate ground team to mobilise. None of this works without ecologists in the loop. Our data scientists build and train the models, but ecologists ground-truth every output in the field, correcting what the model gets wrong, and those corrections feed back into retraining the system. It’s a continuous cycle between the people who know the ecosystem and the people who build the platform, not an AI model running on its own. That combination is what makes restoration scalable without becoming sloppy.
You can run interventions across a large area and still know, with confidence, what survived and what didn’t.
Can you share a GCC-based case study where geospatial intelligence has delivered verifiable environmental impact at scale?
The clearest example for us is a national-scale mangrove program in Abu Dhabi, run in partnership with the Environment Agency, covering more than 20,000 hectares through the end of the decade. We’ve supported the program with geospatial AI for site suitability, seed deployment, and post-planting monitoring, and to date our models have helped support the deployment of around two million seeds and the ongoing monitoring of roughly 600 hectares. What makes this a useful case study isn’t the scale alone, it’s the verification discipline behind it. Every site goes through a baseline assessment, a planned intervention, and a structured monitoring cycle afterward, so survival and growth can be measured against that original baseline rather than asserted after the fact. That’s the standard we think the whole industry needs to move toward, and it’s encouraging to see a government program built around exactly that level of rigor.
As sustainability becomes a strategic priority, how do you see geospatial AI shaping the future of smart infrastructure and ecosystem management across the region?
The premise we keep coming back to here at Nabat is that nature itself is critical infrastructure, not a separate category from roads, power grids, and water systems.
Once you accept that, the planning tools should look similar too: continuous monitoring instead of periodic surveys, predictive models instead of after-the-fact reporting, and systems that can act, not just observe. A few shifts are already underway that point in that direction. All-weather sensing that can see through clouds and canopy means monitoring doesn’t pause for seasons. Continuous verification means a government or investor can see a living record of an ecosystem’s condition rather than a snapshot every few years. And foundation models trained in data-rich environments are starting to transfer into regions with far less data of their own, which matters well beyond where the work started. The long-term picture is ecosystems managed with the same rigour, and the same expectation of measurable outcomes, as any other piece of national infrastructure. That’s the direction this region is already heading, and geospatial AI is what makes it operationally possible.