RESIDUALS: Multi-Method Differential Feature Detection
A framework for feature detection in Digital Elevation Models using systematic decomposition and differential analysis.
What Is This?
RESIDUALS systematically tests combinations of signal decomposition and upsampling methods to identify which combinations best reveal features in elevation data. The core insight:
Different method combinations have characteristic behaviors that selectively preserve or eliminate different feature types. By computing differentials between outputs, we create feature-specific extraction filters.
The 4-Level Differential Hierarchy
Level What It Shows Column 0 Ground truth (hillshade) DEM 1 Decomposition residuals bicubic, lanczos, bspline, fft 2 Residual vs ground truth Δbic, Δlan, Δbsp, Δfft 3 Divergence across methods Div 4 Meta-divergence (uncertainty of uncertainty) ΔDiv
Quick Start
# Clone and install git clone https://github.com/bshepp/RESIDUALS.git cd RESIDUALS pip install -r requirements.txt # Run with included test DEM python run_experiment.py # Or generate your own from LiDAR python generate_test_dem.py --lidar-dir /path/to/las/files --grid-rows 4 --grid-cols 4 python run_experiment.py --dem data/test_dems/your_dem.npy
Decomposition Methods
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