Run-Length Stripping for River Network Skeleton Lines

The patent divides the large-scale river network raster data into multiple smaller tile units for independent processing. Each tile is processed separately to extract skeleton lines, avoiding the need to load the entire high-resolution raster into memory at once. This segmentation enables high-precision extraction while controlling memory usage within acceptable limits.

The patent transforms the problem from direct high-resolution raster processing to a multi-scale approach. By processing at different resolution levels and progressively refining results, the method achieves high measurement precision without requiring the entire high-resolution data to be held in memory simultaneously, effectively adding a temporal/dimensional dimension to the processing workflow.

The patent segments the computational task into independent tile-based units that can be processed in parallel. Each tile undergoes skeleton line extraction independently, allowing for distributed computing and parallel processing. This segmentation maintains high extraction accuracy while significantly improving computational efficiency through parallel execution and reduced data transfer overhead.

The patent performs preliminary processing steps such as rasterization and tile division before the main skeleton line extraction. By preparing data in advance and organizing it into manageable tiles with predefined processing workflows, the method reduces computational overhead during the actual extraction process, thereby improving overall computational efficiency without compromising accuracy.

The patent segments the river network into tile units and processes each independently using raster-based thinning algorithms. This segmentation allows the use of precise raster methods on smaller domains while maintaining overall computational efficiency. The local processing ensures smooth skeleton lines without the sawtooth artifacts that plague global vector methods.

The patent introduces an intermediate raster representation as a mediator between the input vector river network and the final skeleton line output. The vector data is first converted to high-resolution raster, processed through precise thinning algorithms, and then converted back to vector format. This intermediate raster step acts as a mediator that preserves both computational efficiency and manufacturing precision.

The patent segments the raster processing task into independent tile units that can be processed in parallel. By dividing the large raster dataset into smaller tiles, the method reduces memory requirements and enables parallel computation, thereby improving computational efficiency while maintaining the high manufacturing precision of raster-based methods.

The patent adds a spatial dimension to the processing by organizing raster data into a tile hierarchy. This multi-level spatial organization allows efficient memory management and parallel processing at different scales, reconciling the conflict between manufacturing precision and computational efficiency by operating at appropriate resolution levels for each processing stage.