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2

You are looking for GDALRasterBand::RasterIO. For efficiency, if you are indexing multiple points, you will want to read data in blocks then index into the resulting array


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You can do something similar using gdal_calc.py, e.g.: gdal_calc.py -A dtm.tif --calc='((A>=100)*(A<=200))*A+((A<100)*0)+((A>200)*0)' --outfile=dtm_reclass.tif --NoDataValue=-32767 This calc expression would: Assign a value of 0 to all pixel values less than 100 ((A<100)*0) Assign a value of 0 to all pixel values greater than 200 ((A>...


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First of all, if you want to work with heights, normalize the LiDAR point cloud. You have a dense point cloud (4 pts/m²) and also high resolution aerial photos, hence, as you said getting the outside roof perimeter is not an issue. Therefore, assuming you'll manage to have a shapefile with the outer roof boundaries, use it to horizontally clip the point ...


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You can use Saga "Upslope area" geoalgorithm. You must install Saga and configure the path where you installed Saga inside QGIS in Processing->Options or Ctrl+Alt+C. Then expand Providers->Saga, set up the folder and mark Active. If you installed through osgeo4w you just need to restart qgis and you should be done. Then open up the toolbox Ctrl+Alt+...


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I used GIMP plugin wavelet noise reduction tool and I get good results and fast: GIMP Plugin Wavelet noise reduction


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For floating-point DEMs, I use -9999 because it's easy to remember, easy to type and, in terms of terrain elevations (in metres), impossible. If you can meet the latter condition, it doesn't really matter what you choose. A lot of climate-related datasets use some variation on the negative-multiple-nines theme, but it's conceivable that some other scientific ...


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I've done some environmental consultation on that area and the best available resolution for a DEM there is probably the ASTER GDEM 30m. Even then, that data isn't processed/cleaned so you might run into problems with clouds/reflective surfaces giving you false values. The thing with Lidar is that it costs quite a bit of money to produce and while some ...


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In step 4 of the tutorial it states "If desired, choose spatial and/or temporal search criteria". In essence, what you should do is draw a box on the map covering the area that you wish to search for data in (while leaving the temporal options alone). This will give you only the tiles within your search box, rather than each and every tile available in the '...


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I would try to add a low resolution DEM (e.g. SRTM 30m) to fill the gaps where your high resolution DEM is not available. The problem with a crop before orthorectification is that the RPC link image coordinates with ground, so if your image is not georeferenced and you crop it, this relationship will be detroyed.


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If you have SAGA installed, try to use Fill Sinks as an alternative, and see if this tool can solve your problem. The tool can be accessed from Processing Toolbox -> SAGA -> Terrain Analysis -> Hydrology -> Fill Sinks.


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I'm not sure if you can do this with any of the gdal cli tools, but I wrote something in python which accomplishes it: from osgeo import gdal from osgeo.gdalconst import GDT_Float32 import sys import numpy as np def fix_dem_nodata(raster_input, raster_output, nodata=0, threshold=-900): try: in_data, out_data = None, None # open ...


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So just in case someone else encounters the same problem: this seems to work ! CREATE OR REPLACE FUNCTION drape(my_wkt text) RETURNS geometry AS $$ DECLARE geom3d geometry; BEGIN WITH line AS (SELECT my_wkt::geometry as geom), linemesure AS -- Add a mesure dimension to extract ...



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