Raster retraining decision system

The method of claim 21, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• tiling an image into a set of image tiles comprising an image tile.

The method of claim 21, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• executing the raster segmentation model using an image tile to generate a plurality of mask tiles corresponding to the image tile, wherein the plurality of mask tiles comprises a header mask tile, a track mask tile, and a depth track mask tile.

The method of claim 21, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• executing a synthesis model to combine a plurality of header mask tiles, comprising a header mask tile, into the first header mask, combine a plurality of track mask tiles, comprising a track mask tile, into a track mask, and combine a plurality of depth track mask tiles, comprising a depth track mask tile, into a depth track mask.

The method of claim 21, wherein executing the header mask segmentation model of the second stage of the raster segmentation model comprises:

• processing the first header mask to generate a set of header image tiles centered with respect to a header identified in the first header mask.

The method of claim 21, wherein executing the header mask segmentation model of the second stage of the raster segmentation model comprises:

• executing a header raster segmentation model using a set of header image tiles to generate a set of second mask tiles corresponding to the set of header image tiles, wherein the second header mask corresponds to a second mask tile of the set of second mask tiles.

The method of claim 21, wherein executing the segment comparison model comprises:

• processing the first header mask with a set of second mask tiles to generate a set of operation values,
• wherein a second mask tile of the set of second mask tiles corresponds to the second header mask, and
• wherein an operation value of the set of operation values is one of an intersection over union value, an F1 score value, and a DICE score value.

The method of claim 21, wherein executing the segment comparison model comprises:

• combining a set of operation values to generate the comparison score, wherein combining the set of operation values comprises averaging the set of operation values.

The method of claim 21, wherein generating the raster retraining score comprises:

• combining a set of comparison scores, comprising the comparison score, for a data set to generate the raster retraining score.

The method of claim 21, wherein retraining the raster segmentation model comprises:

• retraining the raster segmentation model when the raster retraining score satisfies a raster retraining threshold, wherein the raster retraining threshold is 0.9 and the raster segmentation model is retrained when the raster retraining score is below the raster retraining threshold.

The system of claim 31, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• tiling an image into a set of image tiles comprising an image tile.

The system of claim 31, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• executing the raster segmentation model using an image tile to generate a plurality of mask tiles corresponding to the image tile, wherein the plurality of mask tiles comprises a header mask tile, a track mask tile, and a depth track mask tile.

The system of claim 31, wherein executing the raster segmentation model of the first stage of the raster segmentation model comprises:

• executing a synthesis model to combine a plurality of header mask tiles, comprising a header mask tile, into the first header mask, combine a plurality of track mask tiles, comprising a track mask tile, into a track mask, and combine a plurality of depth track mask tiles, comprising a depth track mask tile, into a depth track mask.

The system of claim 31, wherein executing the header mask segmentation model of the second stage of the raster segmentation model comprises:

• processing the first header mask to generate a set of header image tiles centered with respect to a header identified in the first header mask.

The system of claim 31, wherein executing the header mask segmentation model of the second stage of the raster segmentation model comprises:

• executing a header mask segmentation model using a set of header image tiles to generate a set of second mask tiles corresponding to the set of header image tiles, wherein the second header mask corresponds to a second mask tile of the set of second mask tiles.

The system of claim 31, wherein executing the segment comparison model comprises:

• processing the first header mask with a set of second mask tiles to generate a set of operation values,
• wherein a second mask tile of the set of second mask tiles corresponds to the second header mask, and
• wherein an operation value of the set of operation values is one of an intersection over union value, an F1 score value, and a DICE score value.

The system of claim 31, wherein executing the segment comparison model comprises:

• combining a set of operation values to generate the comparison score, wherein combining the set of operation values comprises averaging the set of operation values.

The system of claim 31, wherein generating the raster retraining score comprises:

• combining a set of comparison scores, comprising the comparison score, for a data set to generate the raster retraining score.