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. ReLeaf AI is at ideation stage and has not launched; the plan is to start with regional pilots.

Discovery Call See what you get

Ideation stage
Not yet launched
R&D-backed
Pilot partners welcome

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

Tree detectionTree-by-tree trackingCarbon analyticsBiodiversity and healthMap-based dashboardsSustainability reportingSentinel-2Landsat-8PlanetScopeMaxarDJI Matrice / Mavic dronesMultispectral sensorsLoRaWANAWS IoTTree detectionTree-by-tree trackingCarbon analyticsBiodiversity and healthMap-based dashboardsSustainability reportingSentinel-2Landsat-8PlanetScopeMaxarDJI Matrice / Mavic dronesMultispectral sensorsLoRaWANAWS IoT

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 ideation stage and not yet launched, 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 Dubai, Abu Dhabi, Sharjah 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

What you get

ReLeaf AI: what we deliver

In ideation, not yet launched: country-scale tree monitoring that would fuse satellite, drone and IoT data with AI to measure CO₂ sequestration, health and biodiversity tree by tree.

6 capabilities in design, not yet launched

Design note 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.

StatusIn design, not yet launched

Design note 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.

StatusIn design, not yet launched

Design note 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.

StatusIn design, not yet launched

Design note 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.

StatusIn design, not yet launched

Design note 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.

StatusIn design, not yet launched

Design note 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.

StatusIn design, not yet launched

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.

01 / 03

What problem is ReLeaf AI designed to solve?

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.
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?

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.
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.
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?

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.
Rollout would follow the same engineering discipline Next Orbit applies to the live platforms it delivers: 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.
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.

Step 1 of 5

Territory and goals

Agree the region, the species of interest, the reporting standard and the stakeholders who will use the results.

Progress

Step 2 of 5

Data onboarding

Connect satellite feeds, plan drone surveys, deploy or link IoT sensors and set up the field validation app.

Progress

Step 3 of 5

Model calibration

Train and validate detection, species and growth models on the region’s own imagery and field data.

Progress

Step 4 of 5

Regional pilot

Run the platform on a limited area with live dashboards and a first sequestration report, then review accuracy.

Progress

Step 5 of 5

Scale and publish

Expand with auto-scaling compute, open the public dashboards and issue annual IPCC-aligned reports.

Progress

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. ReLeaf AI is at ideation stage and has not launched: the architecture, data sources, AI pipeline and dashboards are being designed, and rollout would start 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.

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.

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