Geospatial & Remote Sensing AI Solutions

Hyperspectral remote sensing, geospatial intelligence and edge vision for industrial operators and the public sector. Our models run on satellite data and on edge hardware at remote sites with little bandwidth.

Detection accuracy

94.2%

Single-pass coverage

50 km²

Spectral bands fused

12

Industrial operators and public agencies collect more sensor data than they can analyze. Satellite passes, drone surveys and fixed cameras produce imagery at a scale no team can review by hand, and the decisions that depend on it, from contamination monitoring to site inspection, are often high-stakes and time-sensitive. The challenge is turning that data into reliable answers in places where compute and connectivity are limited.

The problems we see in industry and the public sector

• Monitoring at scale. Field sampling is slow, expensive and spatially sparse, leaving large areas unobserved between campaigns.
• Signals that standard imagery misses. Contamination, mineralogy and material condition often show up only in narrow spectral features that RGB and broadband sensors cannot resolve.
• Limited bandwidth. Remote sites, field equipment and satellite links cannot move raw data to a central cloud fast enough to act on it.
• Sensitive programs. Defense-adjacent and public-sector work often requires that data and models stay under the client's control.

What we build for operators and agencies

• Hyperspectral and multispectral pipelines: calibration, cloud and shadow masking, co-registration and spatial-spectral deep learning from raw satellite products.
• Geospatial intelligence systems that turn imagery into maps, alerts and quantified estimates that field teams can act on.
• Edge vision for sensors and cameras, optimized to run on local hardware where bandwidth is scarce.
• Detection and segmentation models for inspection and monitoring.

The modeling capability behind this is described in Computer Vision, and novel methods are developed through our Research & Development practice.

Constraints we design for

Bandwidth and compute at the edge

We plan for the deployment target from the start: where inference runs, what hardware it has, and what must be transmitted. Models are quantized and optimized so the result, not the raw data, is what travels.

Noisy, heterogeneous data

Satellite and sensor data arrive with atmospheric effects, bad bands and misalignment. Deterministic preprocessing is as important as the network, and we treat it as part of the system.

Control and confidentiality

We deploy on your infrastructure and work under the confidentiality terms your program requires.

Relevant work

Hyperspectral Heavy Metal Detection is a complete pipeline for processing hyperspectral satellite imagery to detect and quantify heavy metal contamination in soil across mining regions. Using custom spectral unmixing, it reached 94.2% detection accuracy, fusing 12 spectral bands over 50 km² of single-pass coverage. We describe the architecture, from L1A radiance preprocessing to channel attention and cross-domain transfer learning, in Hyperspectral Heavy Metal Detection: Multiscale Spatial Deep Learning on EnMAP Satellite Imagery.

Our identity verification engine for Oneex runs in airports, defence and sovereign infrastructure, fully on-device on CPU-only edge hardware with no cloud dependency. For Tessi, we audited and benchmarked an industrial OCR engine built for high-volume document processing.

How to start

A first engagement usually pairs one monitoring or inspection question with the data you already hold, whether satellite products, survey imagery or sensor feeds, plus whatever ground truth exists. We assess feasibility, build and validate a pipeline against field data, and deploy it where it needs to run, then hand over the system and documentation to your team. Contact us to discuss your program.

Selected partners: NVIDIA · Confidential