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Point cloud data offers non-uniform coverage via points while a raster image displays complete coverage of an area using pixels. Converting a point cloud to a gridded raster results in easy to use data with a large amount of information. Using FME, you can convert a point cloud to a raster and customize your raster image by adjusting factors like pixel resolution or which point cloud component to display. The following exercises take you through different workflows step by step and explain how FME transformers can be used in different point cloud to raster translation scenarios.
This workflow will focus on using the point cloud component called intensity to create a raster output. Intensity is a quantitative indicator of the reflectivity of an object or how “bright” the return was during the point cloud data collection. For example, vegetation has high reflectivity and would return a high-intensity value while pavement or rooftops have low reflectivity and would return low-intensity values. If we visualize a point cloud using intensity only, we can create a grayscale raster.
The result is a grayscale image. Below, from left to right are the source LAS file, rough output raster, and the smoothed output raster
An output image can often have some spots of NoData like where there is a body of water. In our case, we have spots of NoData where there is a lack of coverage due to the distance between points being larger than rasterization spacing. In the image below you can see where there are spots of NoData:
If there are many of these spots or if the output predominantly consists of NoData, consider using larger rasterization spacing like 20m instead of 10m (spacing is set in the ImageRasterizer). If there are few NoData pixels, try using smoothing algorithms such as resampling up and then down using the RasterResampler, as seen in steps 5-6 above. template workspace.
NumericRasterizer uses the Z coordinate to produce a DEM from a point cloud. The output will be effective so long as the assigned cell spacing is larger than possible irregular gaps in the point coverage. Due to the nature of point cloud data, these gaps are quite frequent and as a result, we should set a large cell spacing of 10m to avoid NoData holes in the DEM coverage. We cannot use smoothing as we did with intensity in the previous example because it will distort the surface making it appear as if it were hit with meteorites.
The images below show the original point cloud, the raster produced by the NumericRasterizer (10 meter spacing), and the raster produced by the RasterDEMGenerator respectively:
Data Attribution
i tried your "Scenario11_PC2Numeric_FME2011.fmwt" and it did not work with my .LAS file !!!
I m trying your "Scenario11_PC2Numeric_FME2011.fmwt"
what the piece of software required to properly open and view the output/writer file?
Regards
I tried your "Scenario 11. PC2Grayscale" attached
it went perfectly, the thing is, I want to display the raster in colors according to the Intensity....which the only classification I have for my point cloud ..
What should I change in the Image Rasterizer transofoemr settings to get the output in colors?
Regards,
Creating Point Clouds from 3D Models or Raster Data
How to create a hillshade image with the RasterHillshader
Converting Point Clouds to Surface Models without Classification
Comparing Rasters of Different Formats and Structures
Draping imagery textures on terrain surfaces
Setting NoData and Adding Alpha Bands to Remove Black Borders
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