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1

In GRASS GIS, you can use r.thin for this task: The code implements the thinning algorithm described in "Analysis of Thinning Algorithms Using Mathematical Morphology" by Ben-Kwei Jang and Ronlad T. Chin in Transactions on Pattern Analysis and Machine Intelligence, vol. 12, No. 6, June 1990, along with further subsequent improvements. In QGIS, you can ...


2

So you want to convert all values to the same constant value and NoData should remain NoData. Instead of Reclassify, use the Con tool with your input raster as 'Input conditional raster', and the constant value as 'Input true raster or constant value'. E.g.: import arcpy cst = 5 # your constant value outCon = Con(r"C:\data\intput.tif", ...


0

With ArcGIS Spatial analyst, you can use the "thin" tool. Their algorithm is described in Zhan, Cixiang, 1993, A Hybrid Line Thinning Approach, Proceedings Auto-Carto 11, Minneapolis , pp. 396-405 As a remark, if you are interested in the process, you can also have a look at the skeletonization process in mathematical morphology. I don't know about a ...


0

I would suggest taking a look at the ArcScan toolbar if you are using ArcGIS. It has a range of functions in addition to Raster vectorisation which I think should help. Specifically the Raster Cleanup tools such as erosion and dilation which will make your lines thinner. Also check out raster snapping as that may also be useful. The image below is from the ...


-1

I think that somewhere in the classification process you are including spatial coordinates or pixel row/column IDs of your training samples. For a purely spectral classification and classes distributed in a spatially homogeneous manner it is not required to include spatial coordinates. From a random forest perspective, this would explain the linear ...


0

There is a Curvature GP tool in Spatial Analyst that may be helpful. It can tell you how convex or concave a surface is. For example it can be used to determine ridges and valleys. You would still need to convert the output raster to vector - likely it would be lines that you convert to initially.


1

This is a bit of a late answer, but I thought it was worth contributing. As a polyline is just a series of points you should be able to obtain the Mean value you want by converting the Polyline nodes into points by going Vector > Geometry Tools > Extract Nodes... You can then extract the underlying Raster values for each of these points by using the Point ...


1

I see that the question and answers are quite old, but still will post my answer as it could help someone in the future. I get this error mostly because invalid characters in the paths and file names. Keep your whole path strictly in ASCII without spaces and the filename under 13 characters.


2

You could: Generate contours at the desired intervals from your DEM and use the lines to cut up your polygon. Clip your DEM using your polygon, then reclassify the DEM using the desired interval ranges, followed by a little math - get the number of pixels in each class times the area of a pixel. (See Measuring area of raster classes? which discusses ArcGIS ...


0

So, I solved my problem in the way that I actually converted my Raster data into Polygon, draw Buffer around it with the Buffer tool and then converted it back into Raster.


0

Step one is to read GeoSolutions excellent GeoServer on Steroids: http://demo.geo-solutions.it/share/foss4g2013/gs_steroids_sgiannec_foss4g2013_01.03.pdf - it's pretty much the definitive guide to GeoServer optimisation. You'll want pages 10-18. A simplified version of what you want to do is: Convert into GeoTiffs Use Inner Tiling Add Overviews Load them ...


5

This is a bit messy thing. You should read at least this GeoTIFF document http://www.remotesensing.org/geotiff/spec/geotiff2.5.html#2.5.2.2 and some GDAL considerations http://trac.osgeo.org/gdal/wiki/rfc33_gtiff_pixelispoint. As a rule of thumb all rasters (aerial, satellite images) use pixel-is-area and measurement data like DEMs use pixel-is-point. ...


3

Both are common, and neither can be considered entirely standard. For GeoTIFF, both are possible - see Section 2.5.2.2 for the GTRasterTypeGeoKey that describes the interpretation method. The GeoTIFF FAQ suggests using the default (PixelIsArea) value of that tag for compatibility with older versions of GDAL. The World File format uses the centre of the ...


1

I do not know if there is a convention, but coordinates on images normally refer to pixel centres. This this can differ in the case of rasters produced from, for instance, computer models. If you are not sure, you can use GMT, the Generic Mapping Tools, to test you rasters and convert them to some appropriate format, since it explicitly offers the option of ...


1

At first, you should distinguish "georeferenced rasters" from "non georeferenced raster". The second group includes all the raw image acquisitions from remote sensing (aerial photographs, satellite images, UAV images) or scanners. Those raw images will be captured according to the sensor orientation, which is rarely North up. They are affected by geometric ...


0

You should be fine if it was consistently undefined in them all but they all really had the same coordinate system (just not defined).


1

1) Does r.neighbors walk through the raster collecting cell values? The neighborhood operators determine a new value for each cell as a function of the values in its neighboring cells. All cells in a raster map, except for the cells at the map boundaries, become the center cell of a neighborhood as the neighborhood window moves from cell to cell ...


1

I've just tested the code below and it works fine. You need to be careful in your code that you don't try and apply symbology to the template raster itself otherwise you'll receive an error so I've added an if statement. import arcpy mxd = arcpy.mapping.MapDocument("CURRENT") df = arcpy.mapping.ListDataFrames(mxd)[0] rasters = arcpy.mapping.ListLayers(mxd, ...


0

Attachments may be useful here. If so then you can use the Generate Attachment Match Table to batch load your images.


1

Given a mosaic dataset, you can select the footprints of interest and then in the table of contents right-click the footprint layer > Data > Download Selected Rasters... which then allows you to choose which rasters to download.


0

The code below works for me, though I think it's a bit sloppy. I had to include make raster layer and save to layer for it to work. arcpy.MakeRasterLayer_management(in_raster, out_raster) arcpy.SaveToLayerFile_management(out_raster, display_raster, "ABSOLUTE") # Adjust symbology of Raster layer ...


2

There's a few minor hiccups in your code, I've re-written it (hopefully) better: import arcpy arcpy.env.workspace = "c:\DEM Files" # not requred mxd = arcpy.mapping.MapDocument("Current") # This MXD df = arcpy.mapping.ListDataFrames(mxd,"Georgia")[0] # the first data frame called Georgia rasters = arcpy.mapping.ListLayers(mxd,"*",df) # all the layers # ...


0

What is "Feature_El147"? The help for that function says that the second argument has to be a: Feature Layer; Raster Layer;TIN Layer; Network Analysis Layer;Geostatistical Layer. Which in this context refers to a layer currently in the map document that has the symbology you want. Is that how you've got it set up?


0

Personally I'd create a mosaic dataset (or VRT) and extract the area of interest from that. VRT is supported by Global Mapper but will be quite slow, I would go with the mosaic dataset for this operation. Both of these raster types are links only to their respective rasters so don't take very long to create as the rasters themselves aren't being copied. ...


0

You can define the labelling style using the label.style argument of lattice::contourplot. In my opinion, you should choose align. It is not a complete solution because it does not break the contour lines, but it is better than the default method. On the other hand you can overlay two different contour plots with different cuts, line widths, and labels ...


1

It's because NetCDF does not have names for each slice in the 3rd (and higher dims), but raster does. NetCDF has a name for a "variable" (which is the array), but raster has a name for every slice in the variable. (This is the standard mess where we conflate data fields/attributes with dimensions). There's no straightforward way to store these names in ...


3

This sorts itself out with the base function mean. mean(c(20,10),na.rm=TRUE) # where both values occur mean(c(20,NA),na.rm=TRUE) # where the first value occurs mean(c(NA,10),na.rm=TRUE) # where the second value occurs mean(c(NA,NA),na.rm=TRUE) # where both values are nodata If you think of raster functions in terms of vectorization then things become ...


1

The gdalbuildvrt program isn't the right tool for this task. It's for making virtual mosaics and multiband images. I suggest this approach: Define the domain in which you want results (geotransform, rows and columns). We used to call this a "canvas" back in the day. Warp (gdalwarp, nearest neighbor) your source TIFFs to that domain, producing TIFFs with ...


1

You can use Expand to buffer raster "zones" that represent specific values in the raster. Buffers value 10 by 3 cells Expand("landcover", 3, [10])


1

You can use the euclidean distance tool, see: http://resources.arcgis.com/en/help/main/10.2/index.html#//009z0000001p000000


3

I think the objection that the "Raster to Polygon" tool is making is simply that there are too many floating point values to make polygons out of. I doubt that you actually want a separate polygon for each cell. What I would do would be to generalize the raster into categories that make sense (you could use the cutoff values from the classification). I'd ...


0

this was already asked in this forum - see attached You should choose, or drag and drop, your raster to the tool's input field from the Catalog, rather than from the table of contents. Hope this solce your probelm


0

There are likely many ways to solve this, but now that ESRI provides the Multidimensional Supplemental Tools that includes the NetCDF4-Python library, one way is to load subsets of local NetCDF files or remote OPeNDAP datasets into raster using scripts like this: https://github.com/rsignell-usgs/dap2arc/blob/master/src/dap2raster.py


0

This worked for me - the solution was to use SpatialPixelsDataFrame with the suggested tolerance argument (0.916421 in your case): points <- SpatialPoints(s100_ras[,c('x','y')], s100_ras[,c('z')] pixels <- SpatialPixelsDataFrame(points, tolerance = 0.916421, points@data) raster <- raster(pixels[,'z']) though, due to the high tolerance value, the ...


1

This is at least one version of the SQL too: SELECT MIN(ST_UpperLeftX(rast)) AS min_x, MAX(ST_UpperLeftX(rast) + (ST_Width(rast) * ST_PixelWidth(rast))) AS max_x, MIN(ST_UpperLeftY(rast) - (ST_Height(rast) * ST_PixelHeight(rast))) AS min_y, MAX(ST_UpperLeftY(rast)) AS max_y, (MAX(ST_UpperLeftX(rast) + (ST_Width(rast) * ST_PixelWidth(rast))) - ...


0

Yes, FME has two DEM Generators. They are called, appropriately, DEMGenerator and RasterDEMGenerator. You'll want to use the RasterDEMGenerator. There are several options for tweaking the output: I recently used this tool to process about twice as many points and it took 2 hours and 45 minutes. There were five XYZ files writing directly to the DEM. ...


1

If you are interested in a scripted solution, try the gdal command nearblack. You can use the flag -white to retrieve data bounded by white space as well. You can polygonize the output to generate a non-rectangular footprint of the raster data. ##run nearblack shellcmds = "nearblack -white -o " + outpath + " " + inpath os.system(shellcmds) ##run ...


3

Make a big polygon around your entire study area, on and offshore Use the Symmetrical Difference tool to get the difference between your coastline polygon and your study area polygon to create an offshore polygon Extract your raster data using the offshore polygon


3

JPEG is not supported as an output format: The save method supports all raster formats supported by Spatial Analyst. In the example below the raster save method is used to save data to a file geodatabase raster and to IMAGINE format. Supported raster 'save' formats for spatial analyst: Output raster data formats The supported output raster ...


0

I'm pretty sure you need to be using this method arcpy.RasterToOtherFormat_conversion (Input_Rasters, Output_Workspace, {Raster_Format}) You can rename your raster using this method os.rename() More here: http://resources.arcgis.com/en/help/main/10.2/index.html#//001200000032000000


4

GRASS GIS has: r.fuzzy: Cluster raster maps using fuzzy logic. In addition GRASS GIS 7.x has r.fuzzy.system Full fuzzy logic standalone classification system with few fuzzy logic families implication and defuzzification and methods Look at Application of GRASS fuzzy modeling system: estimation of prone risk in Arno River Area (pdf), for example. I ...


4

The concern that I have is that lidar is not a systematic sample, as shown in your example. This data appears to have already been gridded and then thinned based on terrain or vegetation variation. Given the short-distance variation and observed spatial pattern of the clustering, my guess is higher point spacing was retained due to vegetation (e.g., trees). ...


1

All of our rasters are saved as either TIFF or Mr. Sid formats on a separate server from the rest of our ArcGIS infrastructure. These are read-only for everyone but the staff who manage the files. It also makes it possible for people who do not use ArcGIS to be able to pull these images in to their software. We do have a ArcSDE database just for the ...


1

You say you have ArcGIS, but you will need the Spatial Analyst or 3D Analyst extensions. I agree with the above comments. Use IDW or Natural Neighbor interp in SA. Set up the extent and the cell size using the environment settings. Could also build a TIN from these points then use the TIN to Raster tool.


1

We store our data in tiff (or bigtif) with compression because it can handle very large images and you can read it (+ its its internal metadata) with most softwares. I don't like to store images inside of a database because of the size (even if thesize limits have been largely improved), and use som .vrt to extract/combine the valuable information from your ...


2

If you are not in an urban area, interpolating elevation from 4 meter point density to 1 m grid is quite safe, especially if your 1 m spacing reflects a larger roughness of your terrain (e.g. river bed). Make sure that you use an exact interpolator to preserve the elevation at known points, because LIDAR is precise and accurate.


1

Okay, here is a solution that will allow you to easily import Whitebox binary raster files into Excel. First, export the raster using the Export ArcGIS ASCII Grid tool (in fact any of the ASCII raster export tools will work fine). Next, launch Excel and open the file, which will have the same name as your Whitebox raster file but with a .txt extension. A ...


2

Michael Miles-Stimson is correct in his comment above; nearest-neighbour (NN) and majority resampling methods should only be applied to categorical data, i.e. nominal and ordinal level data. Elevation, even when it is presented as integer values (which is a practice that I wish we could make illegal and punishable by lengthy jail terms), is not categorical. ...


2

First make sure that processing plugin is activated. Go to plugins/install and manage plugins and look for processing. Make sure it is activated. It should look like this: You must have a Processing menu. Choose toolbox. In the toolbox, go to the bottom and choose advanded interface. I think you are trying to use saga reclasifying tool. I personally ...


0

So over the weekend I found that my coordinate system stays constant if I output the raster to a TIF instead of GRID. I don't know if I can work all the Spatial Analyst tools with a TIF as well, so will have to try and see which format works better for me... Thank you for your help! :)



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