Experiment 01 · ReLeaf AI
Tree detection
Mask R-CNN, YOLOv8 and DINOv2 vision transformers find individual crowns in satellite and drone imagery, and multispectral CNNs with transfer learning classify species.
Innovation
ReLeaf AI: AI tree monitoring and carbon intelligence at country scale
ReLeaf AI is a platform concept from Next Orbit’s R&D pipeline for counting, measuring and tracking trees across whole regions. It combines high-resolution satellite imagery, drone surveys and IoT field sensors so that reforestation programmes, and the carbon-intensive industries that fund them, can show exactly what has been planted, what is thriving and how much CO₂ it is absorbing. The concept deck is complete, and the platform is planned to start with regional pilots.
- Architecture-led
- R&D-backed
- QC before every release
- UAE, GCC & global
- Individual trees detected in satellite and drone imagery
- CO₂ sequestration estimated per tree and per region
- Health, species diversity and growth tracked over time
- Public dashboards and IPCC-aligned annual reports
Quick answer
What is ReLeaf AI and who is it for?
ReLeaf AI is a climate intelligence platform in Next Orbit’s R&D pipeline. Its first use is country-scale tree monitoring: AI fuses satellite imagery, drone surveys and IoT sensor data to detect individual trees, classify species, model growth and estimate CO₂ sequestration, then publishes dashboards and IPCC-aligned reports for sustainability teams, governments and the public. It is at concept-deck stage, with regional pilots planned before any country-wide rollout.
Every engagement is architecture-led, delivered by an innovative, R&D-backed team of security-first engineers and quality-controlled before each release, for clients across Sharjah, Dubai, Abu Dhabi and the entire UAE, the GCC and globally.
- Individual trees detected in satellite and drone imagery
- CO₂ sequestration estimated per tree and per region
- Health, species diversity and growth tracked over time
- Public dashboards and IPCC-aligned annual reports
- 6things you receive
- 5stages, each signed off
- 18tools and standards
- 4SaaS platforms we deliver and support
What you get
ReLeaf AI: what we deliver
Country-scale tree monitoring that fuses satellite, drone and IoT data with AI to measure CO₂ sequestration, health and biodiversity tree by tree.
6 experiments, all running in production
Experiment 02 · ReLeaf AI
Tree-by-tree tracking
Health, growth and biomass are followed for each tree across a region or a country, with LSTM and transformer models learning seasonal growth patterns.
Experiment 03 · ReLeaf AI
Carbon analytics
Sequestration is estimated in kilograms per tree and tonnes per region, using NDVI, EVI and PRI vegetation indices, leaf area index, and canopy height and volume from LiDAR or photogrammetry.
Experiment 04 · ReLeaf AI
Biodiversity and health
Species counts, diversity indices, health heatmaps, pest and disease alerts and seasonal events such as flowering are reported for each area.
Experiment 05 · ReLeaf AI
Map-based dashboards
A live map with layers for tree health, carbon stock by district, species richness, sensor readings and change detection, with separate access for the public, internal teams and policymakers.
Experiment 06 · ReLeaf AI
Sustainability reporting
Annual sequestration reports aligned with IPCC guidance, carbon-offset estimates for climate disclosures, ESG indicators and monitoring of protected areas.
Need ReLeaf AI for a project that does not fit a template? Tell us the problem and one of our expert engineers will map it to an architecture.
Discovery Call 01 / 03
What problem is ReLeaf AI designed to solve?
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Reforestation is easy to announce and hard to verify.
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Satellite images, drone surveys and field inspections are usually held by different teams in different formats, so nobody can see the health, species or carbon contribution of individual trees. Without that detail, governments and companies struggle to prove ESG claims or biodiversity gains.
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Carbon-offset figures are often compiled by hand, months late and with little evidence, and the public rarely sees any of it.
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ReLeaf AI is designed to replace that with one platform that takes in every data source, measures each tree on a regular cycle and publishes the results to the people who need them.
- Satellite, drone and field data in one pipeline
- Health, species and carbon visible per tree
- Offset figures that are evidenced and kept current
- Open dashboards for stakeholders and the public
02 / 03
How would the ReLeaf AI platform work?
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Four kinds of data feed the platform.
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Satellite imagery from PlanetScope, Maxar, Sentinel-2 and Landsat-8 gives the national picture. On-demand drone surveys with RGB and multispectral cameras add detail where it matters. LoRaWAN environmental sensors report soil moisture, light, temperature and humidity, and existing IoT sensors connect through APIs. Field teams and citizen scientists add tagged photos from a mobile app for ground truth.
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Raw imagery lands in cloud storage and structured results in a geospatial database.
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A containerised pipeline on cloud GPU instances runs ingestion, pre-processing, model inference, aggregation and visualisation. Models are trained on combined satellite and drone data with field validation, served as batch jobs and real-time endpoints, and updated through CI/CD with a versioned model registry and dataset tracking.
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The platform is designed to be open: REST APIs for image upload, tree queries and data export, integration with IoT platforms such as AWS IoT and ThingsBoard, and SDKs for third-party dashboards.
- Satellite, drone, IoT and field inputs
- Geospatial database and GPU processing pipeline
- Batch and real-time inference
- REST APIs, IoT integration and SDKs
03 / 03
Who is ReLeaf AI for, and how would it be rolled out?
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Inside an organisation, the platform serves sustainability teams, field operators and analysts.
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Outside it, the users are governments, NGOs and environmental agencies running reforestation programmes, and mining, energy and infrastructure companies that need to evidence offsets. A public tier gives schools, universities, citizens and journalists transparent impact data.
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Rollout follows the same engineering discipline Next Orbit applies to its live platforms: CI/CD for incremental releases, regional pilots before country-wide scale, auto-scaling compute, partitioned geospatial data for fast queries and retraining pipelines so accuracy improves over time.
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The roadmap adds crowd-sourced species tagging, autonomous drone mapping with AI route planning, soil-health monitoring and regional climate-prediction modules.
- Regional pilots, then country-wide scale
- Auto-scaling compute and partitioned geospatial data
- Continuous model retraining
- Roadmap: citizen tagging, autonomous drones, soil health, climate prediction
How we work
How we deliver ReLeaf AI
5 stages, each with a deliverable you can review and sign off before the next begins.
Readiness level 1 of 5 · Idea
Territory and goals
Agree the region, the species of interest, the reporting standard and the stakeholders who will use the results.
Maturity
Readiness level 2 of 5 · Proven
Data onboarding
Connect satellite feeds, plan drone surveys, deploy or link IoT sensors and set up the field validation app.
Maturity
Readiness level 3 of 5 · Piloted
Model calibration
Train and validate detection, species and growth models on the region’s own imagery and field data.
Maturity
Readiness level 4 of 5 · Deployed
Regional pilot
Run the platform on a limited area with live dashboards and a first sequestration report, then review accuracy.
Maturity
Readiness level 5 of 5 · Operating
Scale and publish
Expand with auto-scaling compute, open the public dashboards and issue annual IPCC-aligned reports.
Maturity
Tools and standards
Tools, methods and standards we use for ReLeaf AI
Tools are chosen to suit the project, not the other way round. These are the ones we reach for most often; the final selection is made during solution architecture and explained in your tailored proposal.
- Sentinel-2
- Landsat-8
- PlanetScope
- Maxar
- DJI Matrice / Mavic drones
- Multispectral sensors
- LoRaWAN
- AWS IoT
- ThingsBoard
- YOLOv8
- Mask R-CNN
- DINOv2
- LSTM / transformers
- LiDAR / photogrammetry
- Geospatial database
- AWS / GCP GPU compute
- REST APIs
- CI/CD
Questions, answered
ReLeaf AI in the UAE: frequently asked questions
01 What is ReLeaf AI?
ReLeaf AI is a country-scale tree monitoring and carbon intelligence platform in Next Orbit’s R&D pipeline. It fuses satellite, drone and IoT data with AI to detect individual trees, classify species, track health and growth and estimate CO₂ sequestration, with dashboards and reports for sustainability teams, governments and the public.
02 Can we use ReLeaf AI today?
Not yet as a live service. ReLeaf AI is at concept-deck stage: the architecture, data sources, AI pipeline and dashboards are defined, and rollout starts with regional pilots. Contact us if you would like to discuss becoming a pilot partner.
03 Which data sources does it use?
Satellite imagery from PlanetScope, Maxar, Sentinel-2 and Landsat-8; drone imagery with RGB and multispectral sensors; LoRaWAN environmental sensors or existing IoT sensors through APIs; and tagged photos from field and citizen-science apps.
04 How is CO₂ sequestration estimated?
Trees are detected and measured from imagery, canopy height and volume come from LiDAR or photogrammetry, growth is modelled over time, and sequestration is estimated per tree and summed per region, with annual reports aligned to IPCC guidance.
05 Who would use ReLeaf AI?
Mining, energy and infrastructure companies proving offsets; governments, NGOs and environmental agencies running reforestation; and schools, universities, citizens and journalists who want transparent impact data.
06 Will it connect to our existing systems?
Yes, by design. It exposes REST APIs for image upload, tree queries and data export, integrates with IoT platforms such as AWS IoT and ThingsBoard, and offers SDKs for third-party dashboards. Related work: climate and ESG software.
Discuss ReLeaf AI for your project
Pick what you need and the right person on our team replies.
- 1What you need
- 2About you
- 3Your request
- 4Confirm & send
Sent. Thank you.
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An engineer will reply to you. Quote the reference if you follow up.
What happens next
From first message to a tailored proposal
- Discovery call with an expert engineer
- Solution architecture drawn around your operation
- Tailored proposal with scope and milestones
More innovations
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- Digital twins Live digital models of assets, buildings and sites, fed by IoT data and explored in 2D, 3D or mixed reality.
- Predictive analytics Machine-learning forecasts of demand, maintenance, delay and risk, trained on your own operational data.
- Climate & ESG intelligence Metering, data platforms and reporting software for energy, water, emissions and sustainability programmes.
Tell us what you are building
Share the problem, the systems involved and your timeline. You will hear back from an engineer, not a sales script.