Distance Maps

Shows how close each cell is to things like roads, faults, or boundaries.

Rasterizes vector features (points, lines, polygons) into distance-to-feature rasters for spatial modelling.

Input: Vector data (.SHP)

Output: Raster

Key Parameters: Grid resolution, AOI, Vector category, Desired category

Processing: Converts vector features into distance-to-feature raster. One grid raster per feature type if categories are specified.

Data Density Maps

Highlights areas where you have lots of information vs. little to none.

Creates a raster that reflects how many data layers are present per pixel, highlighting data-rich areas.

Input: Vector Data (.SHP), Raw Raster

Output: Raster

Key Parameters: Grid resolution, AOI

Processing: Counts the number of layers informing each pixel to highlight data density.

Structural Domain Maps

Creates a map of which way rock layers are tilted or folded underground.

Convert orientation measurements (strike/dip) into interpolated structural grids, vector fields.

Input: Measurement points (.SHP)

Output: Raster

Key Parameters: Grid resolution, AOI, Structure types, Strike, Dip, Right hand rule

Processing: Converts structural measurements into interpolated strike field maps using transformation rules.

Fault Disturbance Maps

Maps how the volume of rock is affected by a fault.

Model spatial influence and disturbance zones around fault lines with customizable decay.

Input: Line vectors (.SHP)

Output: Raster

Key Parameters: Grid resolution, AOI, Faults, Wavelength, Disturbance level, Decay

Processing: Models wave-based disturbance zones around faults with decay scaling away from fault trace.

Computer Vision Maps

Pulls out hidden patterns in the data using image recognition techniques.

Apply deep learning filters (feature extraction) to raster inputs to extract high-level spatial features.

Input: Raster

Output: Raster

Key Parameters: Grid resolution, AOI, Number of filters

Processing: Applies deep learning, RESNET-50, to extract spatial features from raster data.

Texture Filter Maps

Pulls out hidden patterns in the data using Haralick filters.

Apply deep learning filters (feature extraction) to raster inputs to extract high-level spatial features.

Input: Raster

Output: Raster

Key Parameters: Grid resolution, AOI, Number of filters

Processing: Applies Haralick filters to extract spatial features from raster data.

Multivariate Anomaly Maps

Flags areas where the data looks strange or unexpected when layers are compared together.

Detect anomalies based on multi-layer raster analysis using an anomaly scoring algorithm.

Input: Data Rasters

Output: Raster

Key Parameters: Grid resolution, AOI, Target grids

Processing: Analyzes multiple grids to detect anomalies across all data, using a scoring algorithm across layers.

Lineament Maps

Traces straight-line features like fractures or boundaries that show up in the data.

Identify and extract linear features (lineaments) using edge detection and line-following algorithms.

Input: Data Raster

Output: Raster

Key Parameters: Grid resolution, AOI, Edge detection sensitivity, Line length, Gaps, Orientation filters

Processing: Detects and traces linear features from raster data using edge and line detection techniques.

Gridded Maps

Links soil, till, or rock samples to auxiliary layers to predict values across the entire AOI, including areas without samples.

Uses predictive algorithms to find patterns between point-based data and auxiliary data; extrapolates those patterns across space using secondary datasets.

Input: Point-Based Data

Output: Raster

Key Parameters: AOI, data column(s), modality, smoothing kernel, selected auxiliary rasters, output resolution

Processing: Trains a machine learning model on point samples and rasters to generate stable, averaged predictions.