I have a folder with more than 500 raster (DEM) and I want to reclassify (equal interval method) each of them but number of classes in each case is determined by the number of 100m elevation bands. i.e number of classes =(max elevation- minimum elevation)/100. I have obtained max and minimum elevation of each raster from GetRasterProerties tool. How do I put this custom number of classes for each raster?
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I am using ArcMap 10.1 or 10.2. I have a folder with more than 500 raster (DEM) and i want to reclassify (equal interval method) each of them but number of classes in each case is determined by the number of 100m elevation bands. i.e number of classes =(max elevation- minimum elevation)/100. I have obtained max and minimum elevation of each raster from GetRasterProerties tool. How do I put this custom number of classes for each raster using ModelBuilder?
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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 have a single band raster file as drawn in the labeled image attached.  The size of raster is around 4k pixel in each x and y axis. Large part of this raster is empty. Only in small portions of the raster contains data as labeled. Is there any efficient way I can extract rectangular patches around the data portions of the large tif raster file? Any pythonic way using gdal or rasterio or some other package. What I have done so far: I have followed from sklearn.feature_extraction.imageway to extract 2D patches where there is data as follows. from sklearn.feature_extraction.image import extract_patches_2d im = imageio.imread(largeFilePath) # reading the large image x, y = np.nonzero(im) # taking xy locations with non zero values  # determining xleft, xright, yleft and yright of the non zero area in  raster  xl,xr = x.min(),x.max()  yl,yr = y.min(),y.max()   #Extraing the non zero area from my original raster only_desired_area = im[xl:xr+1, yl:yr+1]  # my desired window to extract  window_shape = (480, 480)  # 2D Ptaches extraction B = extract_patches_2d(only_desired_area, window_shape, max_patches=100)  Issue: This doesn't have georeference information because I haven't used gdal or rasterio
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Are there hard and fast rules about which interpolation methods are suited to each kind of raster data?
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I have a land cover classification derived from Landsat TM imagery - I created this classification using ENVI. I would like to edit individual and groups of cells in some areas of the classification in order to improve the accuracy of the output by comparing it with aerial imagery.  Is there a way, using ArcGIS Desktop, to manually edit large groups of cells by selecting them and assigning them to a new or existing class?   I am aware of merging methods based on class statistics, but the area I'm classifying has a high amount of spectral confusion due to thin cloud cover - it seems like a manual edit is the only way to go.
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I am a beginner in Machine Learning and I have a situation where an Image needs to be classified into first a super class, then a subclass. For Example, I have a set of images of fruits, containing images of Mangoes, Banana, Peaches, Apples etc. Then each fruit can either be fresh or rotten. I have training set containing images of rotten and fresh fruits for each Fruit type. So for a test image I need to classify which fruit it is (Mango, Banana, etc) and then whether it is fresh or rotten.  I want to know how to approach this kind of problem
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I am creating an exercise for remote sensing students and would like to take an existing raster and remove the spatial reference from the file so that the students can practice using another image to orthorectify the first. I am using ArcGIS 10.   Any hints?
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I have a series of landsat wanting to process in raster calculator, but after doing the tool it happened these output images only contain one single band. They used to be RGB 3-band images.  It is on arcmap 10.3. I was using raster calculator to remove a certain value in my 3-band landsat image. After I type in the conditional statement to remove all 0 value from the inpur RGB image, it only returns an image of 1band, black and white. How can I preserve the RGB value in output.
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I am using ArcGIS Desktop.  I have two DEMs in .tif format and I am looking to merge the two. The main problem with merging these are that they are different resolutions (one is 10m and one is ~20m). Also, another problem with merging the two is that they overlap and I am not sure of the most effective way of dealing with that overlap. Obviously I want to keep the values of the better resolution.   So what are the best methods here?   Is there a python script available that works best with merging rasters? I would like to automate this with a script if possible. Which process of resampling is best for DEM data? Bipolar, Cubic... ? What is the best way to merge overlapping DEMs?
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I'm new in QGIS and I'm trying to reclass a raster with reclassify grid values. I want to reclassify by table, however fixed table has only 3 rows and I need some more, but button to add is not activated.  Do you have any idea about how add more rows?
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I am using MOD09Q1 now and I would like to calculating NDVI whole year data. One year, I have 46 band 1 images and 46 band 2 images.     list.files   I creating list of band 1 and list of band 2.  Band1list  &lt;- list.files(path = ".", pattern = "*.b01.tif", all.files = FALSE,                          full.names = FALSE, recursive = FALSE,                          ignore.case = FALSE)    Band2list  &lt;- list.files(path = ".", pattern = "*.b02.tif", all.files = FALSE,                          full.names = FALSE, recursive = FALSE,                          ignore.case = FALSE)    raster::stack     Stacking all 46 band 1 (band 2) images to 'StackBand1' ('StackBand2')  StackBand1 &lt;- stack(Band1list)  StackBand2 &lt;- stack(Band2list)    NDVI calculation via for loop     I try to stack images of same date (e.g. "2012.01.01.Smb01.tif" and "2012.01.01.Smb02.tif") and calculate NDVI  for (i in 1:length(Band1list)) {   StackBand1[[i]] == i    StackBand2[[i]] == i     Stack &lt;- brick(StackBand1[[i]],StackBand2[[i]]) }   In here, I could not execute loop for all 46 images. It can be run all and result is showed for the last couple only.
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I've created a series of DEM files using the  tool from LAStools; however since I don't have a licensed version they have black streaks running across them. Does anyone know a good method for removing these streaks?
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Is it possible to create buffers for pixels of raster files? Actually I need to expand the pixels classified as 1 (in white in the figure) with a spatial range of 1 or 2 pixels, in order to perform some generalization.  I'm using QGIS, is this possible with gdal or something?
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I am using ArcGIS 10.1 to generate a DEM (bare earth) from LAS dataset. To accomplish this, I use Las Dataset to Raster tool as described in .  I have read here and there in the web that the the cell size of the output raster should be set at a value corresponding to the average LAS points spacing; somewhere else, they suggest to use a value that is three times the average point spacing.  Now, since I wish to produce a bare earth DEM (i.e., with only ground returns), I am wondering if the cell size has to be set at the average point spacing of the whole LAS dataset (i.e., comprising non-ground, ground, etc), or exclusively of the ground points?  On the basis of the ArcGIS's point file information tool (), I managed to get statistics for each LiDAR class. The average spacing (whole dataset) is 1.5 m, while for ground points only is 0.67.  Any insight into the issue?
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I have a DEM layer from ASTER satellite and I want to get the slope information from it.  I use terrain analysis > slope, but the output is so wrong. Why is that?  Here is the DEM:     And here is the "Slope" that is totally wrong. It should stretch the values from 0 - 89.9097 but it does not.
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I am looking for a way to split multiband images and extract all the bands at once- instead of subsetting one band at a time.   Is there any method in QGIS which could allow for this? I know there are split RGB band tools but I have 8 band files.
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I have two shapefiles representing grids with values that are symbolized with graduated colors, shown below:    You can see that the two shapefiles encompass similar ranges of data, but assign slightly different colors to ranges in these data.  I would like to symbolize the ranges in each of the two shapefiles using the same colors for the same ranges, enabling easier comparison between the files and allowing a single legend to be used.  However, when I attempt to classify the ranges with a manual interval, ArcGIS forces what should be the uppermost range to also include the minimum value from the dataset.  You can see this in the "-81.64 - 10.00" range of the left data frame, below.  This forces the entire layer to be symbolized with this color.  Essentially, ArcGIS is requiring that the minimum value in the dataset be used as a value in one of the ranges.    Is there a way to circumvent this feature?