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When using .tif files for training, particularly with multispectral imagery, nodata values in the source metadata can be misinterpreted as valid pixel values. This is critical because we retain the .tif format rather than converting to formats like PNG that inherently bound values. Mismanagement of nodata values could degrade model performance and skew training results.
Why It Matters
Data Integrity:nodata values could lead to incorrect model learning.
Multispectral Complexity: Different bands may have varying no-data values.
Model Performance: Clean handling of nodata ensures better generalization.
Proposed Solution
Introduce explicit handling of no-data values based on .tif metadata.
Mask nodata pixels to exclude them from training.
This will improve the reliability and quality of input data for training, especially for multispectral workflows.
The text was updated successfully, but these errors were encountered:
Problem
When using
.tif
files for training, particularly with multispectral imagery,nodata
values in the source metadata can be misinterpreted as valid pixel values. This is critical because we retain the.tif
format rather than converting to formats like PNG that inherently bound values. Mismanagement ofnodata
values could degrade model performance and skew training results.Why It Matters
nodata
values could lead to incorrect model learning.nodata
ensures better generalization.Proposed Solution
.tif
metadata.nodata
pixels to exclude them from training.This will improve the reliability and quality of input data for training, especially for multispectral workflows.
The text was updated successfully, but these errors were encountered: