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This document details Datarock’s Custom Unique Segmentation products.
Contents
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Dependent Models
The outputs of the following models are used to determine any Custom Unique Segmentation:
Model Name | Model Type |
Image Preparation | Object Detection |
Depth Registration | Semantic or Instance Segmentation |
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Unique Segmentation] e.g. Vein Segmentation | Semantic or Instance Segmentation |
Data Processing
The outputs of the Datarock Custom Unique Segmentation model when applied over depth registered rows allow the area, percentage, count and polygon axis ratio of these segmented features to be calculated.
Below is an image illustrating the difference between a classification model and its potential outputs against an object detection, semantic segmentation (single-class) and instance segmentation (multi-class) model.
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Detection of
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Unique Segmentation classes
Several segmentation classes can be detected by the Custom a Unique Segmentation model based on the training data collected during onboarding. An example of manually labelled examples of segmentation classes can be seen in the following image.
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The following images show an example of segmented outputs for the vein classes as trained using the above manually labelled image.In the first row, the raw row image has been identified and cropped, the second row contains the predicted mask polygons for each of the 5 segmentation classes, and the third row includes these masks overlain across the raw cropped row with area statistics in cm2.
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Product Configuration Options
There are no configuration aspects to this product.
Output Intervals
Default interval length: one row of core (~0.8m)
Customisable interval available: Yes, via uploading an assay or geology logging table to Datarock Customer Success Team
User Data
User data can not be uploaded to the Platform via CSV at the current time.
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Hole_ID
Depth_From
Depth_To
Data Output
Results from this class of models are delivered in a batch nature and can be obtained from the Datarock Customer Success Team. The available CSV files include the following:
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These two CSV files contain the following headers:
File Header | Description | Raw CSV | User Intervals CSV |
filename | Row image filename as defined by box and row | Yes | No |
hole_id | Customer’s Hole ID | Yes | Yes |
box_id | Platform assigned box number | Yes | No |
row_id | Core row number | Yes | No |
tray_id | Actual core tray number | Yes | No |
depth_from | Start of interval | Yes | Yes |
depth_to | End of interval | Yes | Yes |
coherent_area_cm2 | Total area of row containing coherent rock pixels | Yes | Yes |
coherent_area_% | Percentage of row containing coherent rock pixels | Yes | Yes |
incoherent_area_cm2 | Total area of row containing incoherent rock pixels | Yes | Yes |
incoherent_area_% | Percentage of row containing incoherent rock pixels | Yes | Yes |
total_rock_area_cm2 | Total area of row containing coherent and incoherent rock pixels | Yes | Yes |
total_rock_area_% | Percentage of row containing coherent and incoherent rock pixels | Yes | Yes |
[ |
unique segmentation]_area_cm2 | Total area of row containing [ |
unique segmentation class] pixels | Yes | Yes |
[ |
unique segmentation]_area_% | Percentage of row containing [ |
unique segmentation class] pixels | Yes | Yes |
[ |
unique segmentation]_count | Number count of [ |
unique segmentation class] polygons identified within interval | Yes | Yes |
[ |
unique segmentation]_avg_axis_ratio | Average ratio of [ |
unique segmentation class] polygon height to width across an interval | Yes | Yes |
Product Limitations
Limitations | Comments |
Reliance of row detection and depth registration | The |
Unique Segmentation model is based on predicting and masking geological features within a row of drill core. The dependency on the depth registered rows being identified means if a row is missed by the row model during Image Preparation, this row will not have the segmentation modelled applied. |
Training is |
dependent on what can be seen within a row image |
Datarock’s Unique Segmentation model relies on features being segmented using visually identifiable RGB features some of which are too subtle or fine to predict from a photo, in particular if resolution is poor |
. If segmentation classes are not identified, the resulting segmentation model will generally be lower than expectations based on any available site logging. | |
Training data must be representative of whole area segmentation is to be applied | If new imagery or segmentation class is introduced to the model, the performance may decline as these examples were not trained during onboarding. An initial model evaluation will need to be undertaken to see the suitability of the model in particular against any new imagery. Ideally, a new model version is trained to incorporate the new untrained drill core or segmentation class. |
Document Version
Version | Date | Author | Rationale |
1 | 29 June 2023 | N Pittaway | Initial release |