We are leading large-scale, drone-powered mangrove restoration to support the UAE’s 100 Million Mangrove Commitment, working with key partners to restore ecosystems efficiently. Our projects include 500,000 mangroves for ENGIE, 700,000 for Mubadala Energy by 2030, and 2.5 million for ADNOC. Using self-engineered seed dispersal drones, our nature based methodology, and AI-driven monitoring, we ensure high survival rates, cost-effective reforestation, and scalable impact at a fraction of traditional costs.

This project, in partnership with GRID-Arendal and WWF Madagascar, integrates drone-based aerial surveillancewith Earth Observation data to detect and deter illegal, unreported, and unregulated (IUU) fishing in Madagascar’s Exclusive Economic Zone (EEZ). The pilot phase will use self-engineered delta-wing drones capable of long-range flights to identify and track illegal vessels, gathering real-time intelligence. The goal is to demonstrate how UAVs can be integrated into existing surveillance frameworks to enhance enforcement capabilities while minimizing operational costs.

In partnership with the Environment Agency – Abu Dhabi (EAD), we are developing a standalone AI-powered platform for automated species recognition and population monitoring, starting with dugongs. The system will use machine learning to analyze aerial and underwater imagery, detect dugongs, and generate species counts, while allowing for future expansion to other marine taxonomies. The platform will feature an intuitive interface, streamlined workflow, and cloud-based processing to ensure efficient model training, easy data integration, and scalable deployment for conservation efforts across diverse marine ecosystems.

In collaboration with the Environment Agency – Abu Dhabi (EAD), this project advances drone-based NDVI and multispectral imaging technology for both marine and terrestrial ecosystem monitoring. The initiative integrates high-resolution aerial imagery with AI-driven vegetation analysis to assess the health and spatial dynamics of mangroves, terrestrial plant species, and arid ecosystems. Covering sites such as Rahman Island, Jubail Island, Umm Yifeena, and Jebel Hafeet Desert Park, this work refines region-specific methodologies for arid environments,improving long-term ecosystem monitoring, biodiversity tracking, and sustainable land management strategies.

In partnership with ENGIE Solutions, this project provides long-term monitoring of mangrove ecosystems to assess changes in health, waste accumulation, soil accretion, and key ecological parameters. Using drone-based multispectral imaging and AI-driven data analysis, the initiative tracks vegetation health, sediment deposition, and environmental stressors over time. This approach ensures high-resolution, cost-effective ecosystem monitoring to support nature-based climate strategies and sustainable coastal management.

We are developing a self-funded AI platform that analyzes aerial imagery to measure the success rates of restoration projects. Using high-resolution drone data, the system tracks seedling survival, vegetation growth, and environmental factors that influence long-term ecosystem recovery. By integrating machine learning with real-world restoration data, this platform will provide automated, scalable assessments that reduce monitoring costs while improving decision-making for conservation planning and ecosystem management.