Global Mapper Pro

Model Key Points - mass points point clouds

I build surfaces in civil3d that are delivered to civil engineers for design. I am a land surveyor. Sometimes I get all the data needed by survey field crew, other times I get it from remote sensing products such as LIDAR or photo-derived point clouds, and sometimes I mix it all.

I pretty much need to use point clouds and get surface models that I can PASS ON TO ENGINEERS.

That is the context. The specific question I have is that I can use Global Mapper to auto-classify cloud data to extract an estimated bare-earth. But in the end, it is too much data sometimes. I want to figure out how to 'thin' or 'decimate' this surface data to key points that essentially keep the same surface shape but remove redundant data.

Then classify the essential points to the 8- model key point class. So I can then extract smaller .las files for use.

or help me figure out other ways to get the bare earth data out of Global Mapper and into civil3d in a manner that does not blow up civil3d. Sometimes I have 15 square miles of data I need to model - cant have a massive tin on every point, and so on. I thought it would be good if Global Mapper could analyze the point cloud data and classify some that will basically result in about same shape and all of the 'ground'.

Or does Global Mapper already have a way to do this?

my data is typically delivered unclassified LIDAR, or auto-classified LIDAR (but they don't make key points ... I would need to do that myself.) Though I am generally interested in this kind of functionality for non-lidar data as well.

I have tried such things as generating 1ft contours in Global Mapper, then exporting as .dwg, then using those as 3d break lines to build a civil3d surface - and assume I roughly am getting the same surface shape. obviously at this level I am not building a surface for designing curb and gutter - but it is for large scale design.


  • I am very interested in the same topic.   Although I wish that I had an informed response, I am currently researching and looking for the best way to reduce a point cloud derived from drone photogrammetry into model-key ground points, for later use in modeling.

    I wrote GM a request for more training materials on the topic yesterday, but I am hoping that one their reps can follow up your post with a detailed response.  I will definitely be following the conversation.  
  • Rok
    Rok Global Mapper User Trusted User

    I'm also intrested in good model key point thinout. I know terrasolid alghorytms work comparing volume of the original and thinned surface but did't find option inside GM except with XYZ ASCII GRID export that allows to thin out points (doesnt write points if height difference is less than treshold value). ASCII file is to large so this functionality would be interesting for las/laz export.

  • I'm also pretty interested is seeing this functionality.  I'm scanning with a AL3 lidar using a Velodyne head.  A little thick and fuzzy compared to a Reigl.  I want thin...  Thin's in baby!
  • Every other lidar point classification tool I have seen provides for the ability to automatically generate a new classification (8 Model key point) from ground classified lidar points . It is very disappointing that (as far as i can tell) this is not an option in Global Mapper.

    Is there somewhere in this software that I am not looking?

    I don't think so...

  • bmg_mike
    bmg_mike Global Mapper Guru Moderator, Trusted User

    While there is not currently a simply way to do this in GM, we do have todo item GM-4493 for adding automatic classification of model key points. There are quite a few requests, so there's a good chance it will get into v24.1 (v24.0 is nearly complete already).

    I suspect it would work by creating a ground grid (after classification), then creating a simplified surface mesh from that, then identifying the Lidar ground points closest to each mesh vertex and marking those as type 8.

    You could simulate a workflow like this already by turning the mesh vertices into very short lines, then finding Lidar near loaded lines and editing them. A Python script would probably be the cleanest way.



    Global Mapper Guru

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