Non-profit atmospheric research

Measuring the
lower atmosphere
from above.

Low Atmospheric Data Acquisition and Research

LADAR is an open-access research initiative deploying drone-based sensing platforms to characterise temperature gradients, airflow dynamics, and pollutant dispersion across the lower atmosphere — making high-resolution atmospheric data freely available to all.

5
Sensor Stack Configurations
3D
Field Reconstruction
Open
Access Data Policy
ML
Physics-Informed Modelling

Atmospheric research, reimagined.

Traditional atmospheric monitoring relies on fixed ground stations and weather balloons. LADAR deploys modular drone platforms that can profile, map, and reconstruct atmospheric conditions in three dimensions — at a fraction of the cost.

Vertical Profiling

Continuous ascent and descent profiles capture temperature gradients, humidity layers, and pressure changes from ground level to low-altitude ceiling.

3D Field Reconstruction

Sparse in-situ measurements are fed into physics-informed machine learning models to reconstruct continuous volumetric representations of atmospheric fields.

Pollutant Mapping

Integrated air quality sensors map PM2.5, PM10, CO₂, NO₂, and O₃ dispersion across urban environments, enabling source localisation and transport modelling.

Turbulence Analysis

IMU disturbance data and direct anemometry enable turbulence intensity estimation, wind shear detection, and atmospheric stability classification.

Adaptive Sampling

Onboard edge computing enables real-time gradient detection and dynamic waypoint adjustment to efficiently capture transient or extreme atmospheric events.

Open Data Access

All collected datasets, reconstructed fields, and research outputs are made freely available. No paywalls. No embargoes. Science belongs to everyone.

Project ATLAS

ATLAS is a modular, drone-based sensing platform designed for high-resolution characterisation of the lower atmosphere. Its architecture is built around incremental capability — beginning with a lightweight core profiling configuration and expanding through validated sensor stacks as research demands grow.

By combining physics-based modelling with machine learning field reconstruction, ATLAS transforms sparse in-situ measurements into rich, continuous atmospheric datasets.

View architecture →
Stack 1 Core Atmospheric Profiling Active
Stack 2 Environmental Dispersion Planned
Stack 3 Flow Field Characterisation Planned
Stack 4 Thermal Imaging Planned
Stack 5 Adaptive Intelligence Future

Stay close to the work.

LADAR is in early development. Follow our progress, read the brief, or get in touch if you're a researcher, engineer, or institution interested in collaboration.

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