I am looking for any source code that does tree crown segmentation/delaunitaitoun with LiDAR point clouds or their respective greo-TIFFs. So far I can load and view both LiDAR file and its TIFF in matlab but I need the code to convert it to extract tree crowns.  Any open source library in any programming language is ok.
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I am looking for a method to process a remote sensing image and extract the crown areas of the individual trees from the image.   I have both visual wavelength areal imagery, and lidar data from the area. The location in question is a desert area, so the tree cover isn't as dense as a forest area. The resolution of the aerial imagery is 0.5 feet by 0.5 feet. The lidar resolution is approximately 1 x 1 feet. Both the visual data and the lidar come from a Pima County, Arizona dataset. A sample of the type of aerial imagery I have is at the end of this post.   This question  seems to be the same issue, but there does not seem to be a good answer there.  I can obtain a reasonable classification of the vegetation types (and information about the overall percent cover) in the area by using the Iso Cluster classification in Arcmap, but this provides little information on individual trees. The closest I have to what I want is the results of passing the output of the isocluster classification through the Raster to Polygon feature in Arcmap. The problem is that this method merges near by trees into a single polygon.   Edit: I probably should have included some more detail about what I have. The raw datasets I have are:    Full las data, and a tiff raster generated from it. Visual imagery (like the sample image shown, but covering a much wider area)  Manual direct measurements of a subset of the trees in the area.    From these I have generated:    The ground/vegetation classifications. The DEM/DSM rasters.
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I'm looking for aerial LiDAR or terrestrial laser scanner data in laz or las format preferably from an FTP site.   Does anyone know which countries have that data available to the public?   From my research it doesn't seem to be easy to come by.
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I am looking to host lidar data on my own server and give clients the ability to view that data through a web browser as a 3D point cloud (file would be in .las format or equivalent), possibly with the ability to measure features.   Does anyone know of an easy to implement piece of software that can do this?  I am not nessecarily looking for open source, and I would prefer something plug and play because I am not a programmer.
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I have lots of objects consisting of many different meshes. Some of these meshes come with transparent materials applied to them. My goal is to modify the nodes list, add new nodes and change the links for the transparent materials. Based on , I can detect the transparent materials (thanks to ) using the following Python script:  for obj in bpy.data.objects:     if obj.type == 'MESH':         for slot in obj.material_slots:             if slot.material:                 mat = slot.material                 for node in mat.node_tree.nodes:                     if node.label == "Mix Color/Alpha":                         if node.inputs[1].default_value[0] &lt; 1:                             print("Material '%s' on object '%s' seems transparent" % (mat.name, obj.name))   Also, based on  (again, thanks to ) I can manually add shader nodes to get depth maps or surface Normals while ignoring those materials as shown below:    I need to do this process for thousands of objects. I am pretty new to the concepts of nodes in Blender and do not know how modify the nodes and remove/add links to do what is being shown in the GIF image above. So I wonder, how can I add the math and mix shader nodes and add/remove the corresponding links using Python?  You can download  .obj file and load it in Blender to be able and make sure your code works. Just make sure you have selected Cycles before importing the obj file into Blender so that Blender creates the materials' nodes list automatically.  NOTE: I'm not sure how this helps but some of the meshes share the same materials. For instance, in the car example, the headlamps and the glasses (windshield, side windows) share the same material.  NOTE: Just loading the object and doing rendering will not get you the same Normal maps as shown here. The surface Normals of the object I provide here are incorrect. If you want to get the same surface Normals you need to do the following: join all the meshes. Then go edit mode and recalculate outside surface Normals of the downloaded object. Then turn off Auto Smooth.
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I'm trying to mass convert a handful of .tif files. I found phatch could look like a good candidiate but I'm running Ubuntu 11.04. Looks like they don't have a .deb for my version.  Anyone have any alternatives to phatch or any other recommendations as to quickly batch convert tif to jpeg files.  I'm looking for a non-Photoshop (ala Wine) solution.
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I have a scene with a many objects and one camera.  I would like to select the objects that the camera sees, even partially.   Is there a way to do that in Python that accounts for the objects bounds?    Note, I know that KD trees and Octrees are used for the lookup. There is  but the example only suggests how to query objects close to a point. I'm not familiar with the math for combining camera frustrum and binary trees lookup.
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I have a few LiDAR point data (.las files) with me. I would like to read the X,Y and Z from it using .Net. I found , which is in C++ and I am not sure if it can be used commercially after reading the licensing information. Has anyone found any good ones?
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Is there a function to crop sf map object, similar to maptools::pruneMap(lines, xlim= c(4, 10), ylim= c(10, 15)) used for SpatialPolygon or SpatialLine?   I am considering st_intersection() but there can be proper way.
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Given a list of georeferenced rasters, about a hundred, what is the simplest method to generate  for each?  In this case the source rasters are either geotiff with embedded georeferencing or .BIL with accompanying .PRJ, but the ideal solution shouldn't care what the format is. I'm hoping to avoid duplicating data.  I have at my disposal Arcgis 10 (Arcinfo &amp; Spatial Analyst), GDAL Utilities (gdal v1.8.0), and Quantum GIS 1.8.
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Is there any software that can easily create a Styled Layer Descriptor (SLD) without using a  script?
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LIDAR files were released for major part of Slovenia few months back in zlas format. Decompressed LAS format is 1.4 version. I have tried many options how to generate DEMs and had some success in programs and tools but unfortunately they were all trial versions of license pay versions.  Problem is that I can't open and generate DEMs of LAS version 1.4 in many open and free tools (such as MCC-Lidar).   Can you help me converting LAS from 1.4 to 1.2 in some free tool? Or if it is any other way for LAS 1.4.
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Is there any good, complete API for the GDAL Python bindings?  I am interpolating and rasterizing points with elevation data using the gdal.Grid() method;  output = gdal.Grid('outcome.tif','newpoints.vrt')     In its most basic sense, it's working perfectly. But now, I'd like to try the linear approach;  output = gdal.Grid('outcome.tif','newpoints.vrt', algorithm = 'linear:radius=0')     That does generate a new .tif file (512kb), but is just one big black image. Also, not all options seem to be accepted.. for the invdist:power=4 for example it also results in a useless image. What is going wrong, am I missing something? Below is the .vrt I'm using;  &lt;OGRVRTDataSource&gt; &lt;OGRVRTLayer name="newpoints"&gt;     &lt;SrcDataSource&gt;newpoints.csv&lt;/SrcDataSource&gt;     &lt;GeometryType&gt;wkbPoint&lt;/GeometryType&gt;     &lt;GeometryField encoding="PointFromColumns" x="long" y="lat" z="depth"/&gt; &lt;/OGRVRTLayer&gt;       EDIT As a next example I used the gdal.GridOptions(), putting the parameters in that one. It is resulting in a correct file, but still ignores the options. How to actually pass the GridOptions onto the gdal.Grid() ?  edit I found this question also;   Is the linear interpolation just a bug in GDAL?  See my example workfiles;
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I have created a DEM, DSM, and nDSM from a LiDAR Point Cloud using SAGA-GIS.  I extracted the appropriate returns from the point cloud, then converted to grid, and then closed gaps to fill in any nodata regions.  I'm finding that my resultant rasters are still pretty noisy. For example, a region of pixels in the DSM that is obviously a tree or cluster of trees has several pixels with a very similar value to ground instead of the surrounding higher value pixels.  I've tried doing a majority filter on the DSM, but it didn't help.  Any suggestions for other filters that might help create more contiguous regions of pixels?
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Situation:  – In a student project, we are building a cataster/registry of pollard trees. – We have geolocations of many trees and now we want to collect data about these trees in the field. – We want to use open source software and lowcost equipment: an efficient solution, basically.  What we have: – We are using QGIS for analysis – We have tablets running Windows 8.1 and QGIS  – Optionally we have external Bluetooth GPS sensors (but the tablets have internal GPS sensors as well)  What we are able to do: – Use a customizes QGIS with an entry form in the field, to gather attribute data (got that already)   Questions, what we are lacking: – Is there a good way to capture images of trees with the tablet camera in the field AND link them to tree data? Maybe a plugin or script or something? – Is there a way to get the tablet internal GPS sensor working with Windows 8.1?
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I have scene that looks like this:   And I want to add volumetric lighting to it so that light forms a rays of light when it go through the trees like on this example that I found in youtube:   But the problem is that all of these tutorials show how to make this lighting coming from windows, but when I try to replicate it in my forest scene it looks like big ugly fog with cubical shape. Also the light is coming from the back of the camera (as you can see) and there are some spots that needs this special lighting (like places where light goes through trees) and some that don't (like big bright are on the bottom left corner) so I can't just add a giant cube to all of the scene.
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I'm trying to write some code to skip over empty raster sets when converting rasters to point (the empty sets stop the conversion, leaving me with a half complete model run).  I know that I can identify empty rasters using the ALLNODATA result from "Get Raster Properties" .  I know I can delete rasters using the Delete.management tool.  I know I should be able to create a branch using calculate value.  But I'm inexperienced with python and modelbuilder, and haven't been able to put this all together. Could someone help me figure out what I am doing wrong? My code and an image of the modelbuilder setup are below.  def fn(Property):  #Import modules      import arcpy  #ifelse statement if Property == "1":             return "true" elif Property == "0":            return "false"
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I'm having an extremely difficult time finding an optics program that is easy to use and offers accurate physics simulations. I'm not asking for much, I just want to be able to simulate a laser going through a beam splitter and then be able to drag and drop mirrors and angle them to be able to see where the laser beams end up. I want to intersect two laser beams that underwent beam splitting and redirect the beams...   Does anyone know of any free software that can do this?