Applied AI for Earth observation

We turn Earth observation research into working software.

Hyperalis Labs develops multimodal and generative geospatial systems, from aligned optical, radar, terrain, and map data to validated raster outputs. We turn research methods into reproducible pipelines ready for integration and handover.

Discuss a project Explore our work
Multisensor data pipelines
Generative geospatial models
Reproducible training and delivery
Aligned multimodal Earth observation inputs Aligned multimodal inputs
Cross-modal generation workflow Cross-modal generation
Canopy and forest structure modeling from optical imagery Canopy structure sampling
Validation and export workflow for geospatial AI Validation and export

What we build

The work spans geospatial data engineering, model development, and the tools needed to reproduce, deploy, and maintain the result.

Multimodal EO generation

We develop pipelines that combine aligned optical, radar, terrain, and land-cover data, including workflows that reconstruct or generate missing modalities.

Generative geospatial models

We build conditional models for canopy and related structure rasters. Repeated sampling and uncertainty analysis show where the data supports more than one plausible output.

Research-to-production engineering

We package data loaders, configuration, training, checkpoints, validation, and inference or export tools into workflows that another engineering team can maintain.

How the work is delivered

Each project starts by defining the sensor inputs, target output, spatial alignment rules, and evaluation plan. That shared specification keeps model work focused on the operational goal.

1
Data definition and spatial QA Agree on data sources, output specifications, tiling, no-data behavior, coordinate systems, and spatially sound training and validation splits.
2
Model build and validation Train suitable model families, compare relevant baselines, and inspect failure modes on representative geospatial cases.
3
Export, integration, and handover Package inference or sampling, document input and output contracts, and provide a workflow your team can rerun without relying on ad hoc notebooks.
Input stack
Optical imagery, radar, terrain products, and categorical geospatial context selected for the use case.
Output types
Reconstructed modalities, structure rasters, and uncertainty-aware samples that make ambiguity visible.
Operational controls
Versioned configuration, model checkpoints, validation logs, and export paths designed for review, reruns, and handover.

Ways to work with us

Engagements range from a short feasibility study to the handover of an operational workflow.

Feasibility study

Assess the available data, establish a baseline, and test the central technical assumption before committing to a larger build.

Focused pilot

Implement and validate a scoped workflow on representative data, with clear success criteria and documented limitations.

Production handover

Harden the pipeline, connect it to the intended environment, and transfer the code, configuration, and operating knowledge to your team.

Have an Earth observation problem to solve?

Book a meeting to discuss the goal, available data, operating constraints, and a sensible first step.

Book a meeting