<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>venkatasivanaga.r-universe.dev</title><link>https://venkatasivanaga.r-universe.dev</link><description>Recent package updates in venkatasivanaga</description><generator>R-universe</generator><image><url>https://github.com/venkatasivanaga.png</url><title>R packages by venkatasivanaga</title><link>https://venkatasivanaga.r-universe.dev</link></image><lastBuildDate>Wed, 04 Mar 2026 22:17:47 GMT</lastBuildDate><item><title>[venkatasivanaga] FuelDeep3D 0.1.1</title><author>venkatasivareddy003@gmail.com (Venkata Siva Reddy Naga)</author><description>Provides tools for preprocessing, feature extraction, and
segmentation of three-dimensional forest point clouds derived
from terrestrial laser scanning. Functions support creating
height-above-ground (HAG) metrics, tiling, and sampling point
clouds, generating training datasets, applying trained models
to new point clouds, and producing per-point fuel classes such
as stems, branches, foliage, and surface fuels. These tools
support workflows for forest structure analysis, wildfire
behavior modeling, and fuel complexity assessment. Deep
learning segmentation relies on the PointNeXt architecture
described by Qian et al. (2022)
&lt;doi:10.48550/arXiv.2206.04670&gt;, while ground classification
utilizes the Cloth Simulation Filter algorithm by Zhang et al.
(2016) &lt;doi:10.3390/rs8060501&gt;.</description><link>https://github.com/r-universe/venkatasivanaga/actions/runs/26810558428</link><pubDate>Wed, 04 Mar 2026 22:17:47 GMT</pubDate><r:package>FuelDeep3D</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://venkatasivanaga.r-universe.dev</r:repository><r:upstream>https://github.com/venkatasivanaga/FuelDeep3D</r:upstream></item></channel></rss>