rsegm
The rsegm package provides scalable, high-performance tools for geospatial image segmentation built on top of terra, Rcpp, and modern block-wise / tiled processing strategies. It is designed for object-based image analysis (OBIA) workflows on large raster datasets, including satellite and aerial imagery. The core focus is on producing spatially coherent, integer-labeled segment rasters that integrate cleanly with downstream raster and vector analysis in R.
Repository
⚠️ Disclaimer
This package is currently in a beta / experimental stage.
It may contain bugs, incomplete features, or breaking changes.
Use it at your own risk, and please verify results carefully before relying on them in production or critical workflows.
Feedback, bug reports, and contributions are highly appreciated.
Key Features
- Graph-based and hybrid segmentation algorithms: Includes Fast Felzenszwalb-Huttenlocher (FH) graph-based segmentation, Hybrid FH + region-level Mean Shift segmentation, Baatz-Schäpe multi-resolution, Fast Baatz-Schäpe multi-resolution, and SEEDS.
- Large-raster support: Robust tiled processing for rasters that do not fit comfortably in memory, and seam-aware merging to avoid boundary artifacts.
- Efficient C++ backends: Computationally intensive steps implemented in Rcpp for speed and scalability.
- terra-native design: Uses
SpatRasterthroughout and produces standard GeoTIFF outputs with integer segment IDs.
Segmentation Methods
- FH graph-based segmentation: A fast, noise-robust method often used to generate superpixels or initial regions, suitable for large multi-band rasters.
- FH + mean shift hybrid segmentation: A two-stage approach combining FH over-segmentation with region-level Mean Shift refinement to merge spectrally similar regions efficiently.
- Mean Shift segmentation: A pixel-domain mean-shift filtering and clustering method that produces smooth, spectrally homogeneous segments.
- Baatz–Schäpe multiresolution segmentation: A classical OBIA algorithm that iteratively merges adjacent regions based on spectral heterogeneity and shape criteria.
- Fast Baatz–Schäpe multiresolution segmentation: An optimized variant of the Baatz–Schäpe algorithm using efficient region bookkeeping and priority-queue merging.
- SEEDS superpixel segmentation: A hierarchical, histogram-based superpixel method that starts from a regular grid and refines boundaries via local block moves.