Overview

In Step 4: Select Input Features, DORA 2.0 automatically evaluates raster inputs before modeling.

Each raster receives a composite score from 0–100 based on five equally weighted criteria. The final score is the average of these five scores.

Rasters are ranked by their composite score, and the top layers (up to 64) are automatically included in the Recommended Feature selection. You can review and adjust this selection before running the model.

Because the evaluation considers multiple factors, a raster’s score reflects its geological relevance, spatial coverage, redundancy with other inputs, and compatibility with the modelling configuration.

How DORA Evaluates Input Features

DORA evaluates each raster using five criteria that measure coverage, geological relevance, redundancy, and spatial compatibility with the modelling configuration.

1. AOI Coverage

What it measures:
- How completely the raster covers the selected AOI. (Step 1: Select AOI)
How rasters are scored:
- Strong coverage across the AOI = higher scores.
- Significant gaps, missing data, or limited spatial extent = lower scores.
Why it matters:
- Incomplete coverage can reduce model reliability and introduce bias.

2. Model Alignment

What it measures:
- How consistent the raster is with the type of data used to train the selected Deposit Type (Step 3: Select Deposit Type).
How rasters are scored:
- Similar to data the deposit type model was trained on = higher scores.
- Entirely new or unfamiliar to the model = lower scores.
Why it matters:
- Models perform best when applied to data types similar to their training data.

3. Mineralization Relationship

What it measures:
- The statistical relationship between raster values and mineralized versus unmineralized Learning Data. (Step 2: Set Up Learning Data)
How rasters are scored:
- Stronger relationships with mineralization = higher scores.
- Little or no relationship to the target mineral = lower scores.
Why it matters:
- Features that are strongly associated with mineralization contribute more meaningful predictive signals.

4. Raster Redundancy (Collinearity)

What it measures:
- The degree of similarity between a raster and other selected rasters.
How rasters are scored:
- If two rasters contain highly similar information, one will be penalized.
- DORA prioritizes the higher-scoring raster to reduce duplication.
Why it matters:
- Highly correlated layers do not add new information and can reduce model efficiency.

5. Frequency and Sampling Compatibility

What it measures:
- Whether the raster’s spatial resolution and sampling characteristics are appropriate for the AOI and modeling scale.
- This criterion combines three related checks that evaluate how well a raster’s spatial resolution and sampling align with your AOI and modeling scale.
How rasters are scored:
- Resolution Compatibility
  - Too coarse relative to the AOI = lower scores (including oversampled rasters that have been over-averaged and lose important signal)
  - Too fine relative to the AOI = lower scores if it introduces noise or does not add meaningful detail (including undersampled rasters that have been over-interpolated without adding new information)
- Spatial Scale Alignment
  - Spatial patterns are misaligned with the scale of the AOI = lower scores
  - If the AOI is too small to capture the raster’s dominant spatial patterns = lower scores
- Sampling Consistency
  - Under- or over-sampled relative to the modeling resolution = lower scores
  - Inconsistent sampling across the raster = lower scores
Why it matters:
- Rasters must be spatially compatible with the modeling scale to provide reliable predictive signals. Poor alignment in resolution, sampling, or spatial scale can distort or obscure meaningful geological patterns.