Mining footprint detection with multi-modal satellite data
Mining footprint delineation is a prerequisite for environmental compliance monitoring and land rehabilitation planning at national scale. Across Australia -- one of the world's largest mining jurisdictions -- mapping active and historical footprints previously required months of manual GIS analyst time per region, making continuous monitoring economically infeasible for regulatory bodies.
Single-modality optical imagery (Sentinel-2) is insufficient for robust footprint delineation: cloud cover interrupts temporal consistency, spectral signatures of disturbed land overlap with natural bare soil and dry vegetation, and underground or sub-canopy operations are invisible to optical sensors. Integrating Synthetic Aperture Radar (Sentinel-1 SAR) and Digital Elevation Model (DEM) data introduces a multi-modal fusion problem with heterogeneous input dimensionality, temporal misalignment across sensors, and domain shift across ore types (iron ore vs. coal vs. bauxite) and vegetation biomes (arid vs. temperate).
Built a multi-modal deep learning pipeline fusing three independent input streams: optical (Sentinel-2, 13 bands), SAR (Sentinel-1 VV/VH backscatter), and DEM (elevation + slope derivatives). Stream-specific encoders extract modality-aligned features before a late-fusion aggregation head. Fine-tuned Prithvi -- a NASA/IBM geospatial foundation model pre-trained on Sentinel-2 and Landsat -- for mining-specific segmentation, representing the first published application of a geospatial FM to this task.
Built ingestion and preprocessing pipelines for 500GB+ of labeled multispectral imagery from Google Earth Engine. Coordinated peer-reviewed annotation with geographers across University of Queensland, UCL, and University of Nottingham -- spanning multiple ore-type and biome classes -- implementing inter-annotator agreement checks to ensure label quality at scale.
Addressed temporal change detection across multi-year image stacks, distinguishing active operations from rehabilitated or historically disturbed land -- a distinction with direct legal and financial implications under Australian mining regulation.
Enables automated, scalable land-use accountability for environmental compliance and rehabilitation planning across continental-scale mining regions -- replacing months of manual GIS analysis with a satellite-speed automated pipeline. Directly informs regulatory workflows for Australian state environmental agencies and international mining company ESG reporting.
- Monash University — Primary host (A/Prof Risqi U. Saputra, Prof Alex Lechner)
- University of Queensland — Remote sensing science and annotation
- UCL — Annotation and domain expertise
- University of Nottingham — Annotation and domain expertise