R on João Gonçalves http://joaogoncalves.cc/tags/r/ Recent content in R on João Gonçalves Hugo -- gohugo.io en-us © 2026 Sun, 01 Dec 2024 00:00:00 +0000 SegOptim http://joaogoncalves.cc/software/segoptim/ Sun, 01 Dec 2024 00:00:00 +0000 http://joaogoncalves.cc/software/segoptim/ SegOptim SegOptim is an R package for object-based image analysis (OBIA). It allows to run, compare and optimize multiple image segmentation algorithms in the context of supervised classification. It also allows to perform unsupervised classification with several different methods and compare them through internal clustering metrics. For more details about installation and how to use the package go to the tutorial here. Check also the paper describing the package functionalities here. ThermalRaster http://joaogoncalves.cc/software/thermalraster/ Sun, 01 Dec 2024 00:00:00 +0000 http://joaogoncalves.cc/software/thermalraster/ ThermalRaster ThermalRaster is an R package designed to enhance the processing and analysis of FLIR thermal and RGB images. It offers a comprehensive suite of functions for extracting, processing, and visualizing thermal data embedded within the metadata of FLIR images. Key features also include retrieving full and cropped RGB images. Imagery is returned as SpatRaster objects compatible with the terra package, enabling users to profit from this package’s features/toolkit. rsegm http://joaogoncalves.cc/software/rsegm/ Sun, 01 Dec 2024 00:00:00 +0000 http://joaogoncalves.cc/software/rsegm/ 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. 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.