Axine HMAS: Fast, non-invasive detection of soil contamination through intelligent spectral analysis

Heavy Metal Analysis Suite Powered by Hyperspectral Intelligence

Axine-HMAS transforms complex spectral data into precise, scalable insights about soil contamination. By combining advanced remote sensing with powerful predictive modeling, our platform enables faster, safer, and more cost-effective environmental monitoring — without the need for dense physical sampling.

Key Features

Whether you’re assessing agricultural land, monitoring mining impact, or managing environmental risk, Axine-HMAS delivers data-driven clarity where traditional methods fall short.

Hyperspectral Data Integration
Axine-HMAS is designed to ingest high-dimensional hyperspectral datasets from airborne, satellite, or proximal sensing platforms. The system supports flexible preprocessing pipelines, including spectral calibration, noise reduction, band selection, and normalization, ensuring compatibility across diverse sensors and acquisition conditions.
Machine Learning–Driven Prediction
At its core, Axine-HMAS leverages advanced machine learning models to estimate soil heavy metal concentrations directly from spectral signatures. The framework supports a multi-class model approach optimized for high-dimensional data, enabling accurate prediction of trace metal content even in spectrally complex or heterogeneous soils.
Spatially Explicit Contamination Mapping
Axine-HMAS converts spectral predictions into geospatially continuous contamination maps. This enables researchers to analyze spatial distribution patterns of heavy metals at multiple scales, supporting landscape-level assessments, hotspot identification, and longitudinal environmental monitoring.
Scalable and Non-Destructive Assessment
By reducing dependence on dense physical soil sampling, Axine-HMAS provides a scalable and non-invasive alternative for large-area environmental assessment. The approach is particularly suited for agricultural monitoring, post-industrial land evaluation, and mining-impacted regions where rapid coverage is essential.
Model Transparency and Validation
The platform incorporates cross-validation workflows, uncertainty estimation, and performance metrics to support reproducible research. Feature importance analysis and spectral band relevance tools help interpret model behavior and link predictive performance to underlying soil and mineralogical properties.
Interoperability with Research Workflows
Axine-HMAS outputs are compatible with standard geospatial and statistical analysis tools, allowing seamless integration into existing environmental modeling, GIS, and decision-support systems. Export options facilitate downstream analysis, publication, and data sharing.

Get Started with Axine HMAS

Discover how our software can drive results for your organization. Whether you're looking to request a personalized demo, explore partnership opportunities, or learn more about our solutions, our team is ready to assist you.