Unmanned Aerial Vehicle Remote Sensing Tools

Support the analysis of drone derived imagery and point clouds as a cheap and easy to use alternative/complement to light detection and ranging data. It provides functionality to analyze poor quality digital aerial images as taken by low budget ready to fly drones. This includes supported machine learning based classification functions, comprehensive texture analysis, segmentation algorithms as well as forest relevant analyzes of metrics and measures on the derived products.


Reference manual

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0.5-2 by Chris Reudenbach, 2 months ago

Browse source code at https://github.com/cran/uavRst

Authors: Florian Detsch [ctb] , Hanna Meyer [aut] , Finn Möller [ctb] , Thomas Nauss [ctb] , Lars Opgenoorth [ctb] , Chris Reudenbach [cre, aut] , Environmental Informatics Marburg [ctb]

Documentation:   PDF Manual  

GPL (>= 3) | file LICENSE license

Imports raster, foreach

Suggests knitr, stringr, sp, sf, htmlwidgets, htmltools, Rcpp, rgdal, rgeos, gdalUtils, tools, caret, zoo, data.table, parallel, spatial.tools, velox, link2GI, doParallel, CAST, glcm, crayon, ForestTools, itcSegment, pROC, methods, RSAGA, reshape2, rgrass7, randomForest, rLiDAR, rlas, lidR, rmarkdown, mapview, R.utils

Linking to Rcpp

System requirements: GNU make

See at CRAN