[GRASS-dev] point cloud analysis: new features

[Paulo van Breugel]

Great! The spatial cluster analysis option would be really good, e.g. for model evaluation using spatially segregated k-fold cross validation methods.



On 1 January 2015 22:18:39 CET, Markus Metz <markus.metz.giswork at gmail.com> wrote:
>Hi all,
>
>a new spatial index for point data is available in lib/btree2: a
>multidimensional search tree, also known as k-d tree.
>
>What is it good for:
>
>- nearest neighbor statistics: test if points are randomly
>distributed. The current GRASS addon v.nnstat uses an external k-d
>tree from PCL (which in turn uses flann) which finds the approximate,
>not the exact nearest neighbor. The new GRASS-native k-d tree always
>finds the real nearest neighbor.
>
>- spatial cluster analysis: a point cloud can be partitioned into
>separate clusters where points within each cluster are closer to each
>other than to points of another cluster. To be implemented.
>
>- point cloud thinning: a sample can be generated from a large point
>cloud by specifying a minimum distance between sample points. To be
>implemented.
>
>The new k-d tree is now used by v.clean tool=snap (Vect_snap_lines()),
>reducing both memory consumption and processing time.
>
>
>More technical:
>the new k-d tree finds the exact nearest neighbor(s), not some
>approximation. It supports up to 255 dimensions. It is dynamic, i.e.
>points can be inserted and removed at any time. It is balanced to
>improve search performance. It provides k nearest neighbor search
>(find k neighbors to a given coordinate) as well as radius or distance
>search (find all neighbors within radius, i.e. not farther away than
>radius to a given coordinate).
>
>Markus M
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