Latest Contributions by JosephMcGlinchy

‎01-07-2016

Hi Aida, If you are confident in the classification of the points in your data, then the workflow which Geoff mentioned should work for you. Feel free to send me a direct message if you are interested in the workflow when your data does not meet those requirements! Cheers, Joe

‎12-31-2015

Hi Aida, To follow up on what Geoff has provided, we also have a workflow that can help generate the building footprints if that data does not already exist by generating DSM and DTM raster surfaces from the point data. What is the classification level of your point cloud data? Do you know if you have ground and building points classified? Thanks, Joe

‎08-06-2015

A couple of thoughts here. Principal Components Analysis is very valuable for a relevant dataset. I say that because this type of analysis is typically performed on datasets with high degrees of correlation between data points, such as hyperspectral data cubes. Lower spectral resolution datasets, such as Landsat, may not have as much spectral correlation between bands and the result of a principal components analysis may not hold as much water, so to speak. That being said, it is typical that the first 3 components (bands) of a PCA will represent > 95% of the variability in the data. If the output of the PCA tools gives you a data type that is not unsigned 8bit, you can convert it to 8 bit unsigned using the Copy Raster workflow as mentioned above, or by applying a Stretch raster function via the Image Analysis Window and specifying the output to be 8 bit unsigned integer. This result can then be used as input to the Segment Mean Shift GP tool / raster function. Hope it helps!! -Joe

‎07-20-2015

Hi Rachel, There are a few things to consider here. The bands between Landsat 5 and 8 are for sure different. For L5, bands 4 and 3 are needed for NIR and Red, while for L8 it is bands 5 and 4. Also, ideally, each image you are using to produce the NDVI product should be corrected to a surface reflectance product otherwise there will be lots of variability in the result that is false. You asked about a model, how do you mean? If you have a mosaic dataset for L5 data, and a mosaic dataset for L8 data, you can construct raster functions to produce the NDVI product for each of those, and then difference those NDVI products. That can be done either in python or model builder, I believe.

‎02-11-2014

Hi Andrew, Yes, please double check the output GeoTIFF that represents your elevation data. Make sure those values are consistent with what you know to be true! Otherwise, your input in CityEngine will not look correct either. -Joe

‎02-10-2014

Hi Andrew, I think it may be a problem of vertical exaggeration. CityEngine, by default, sets the max brightness for the terrain layer to be 100.0 and I think your max elevations are larger than this. The maximums I see for Cape Town and Johannesburg are 246.0 and 196.0, respectively. If you make the TIF file the input for both parts of the terrain layer (terrain and texture), you can change the maximum value in the import wizard (under maximum brightness value) or in the Inspector window if you select the terrain layer. At the very bottom you can change the 'elev' attribute as follows: attr elevation = map_01(brightness, 0.0, 246.0) + elevationDelta Try that, and hopefully it works! When I imported those files directly with the defaults, the areas look flat. But changing this maximum 'brightness' value should give you the relief you want, no pun intended. Cheers, Joe

‎09-10-2013

bump. these would be very useful! Has anyone pursued this?

‎08-19-2013

some ideas for fixing it: running tool multiple times in same ArcMap session does not update the graph display properly. Horizontal axis labels options do not respond. Horizontal axis title is name of first object in the feature list one uses to generate the histogram. etc., etc.

‎04-08-2013

We need to be fully compliant with LAS 1.4 with all point record formats since many vendors provide their lidar data with all of the point record formats available in LAS version 1.4. Full waveform support is not required at this time (although I don't think anyone would object), but full access to the data in LAS version 1.4, e.g., full range of class codes and record formats, would benefit the community of users of lidar data and ArcGIS's LAS Dataset. Currently, from the ArcGIS resource center: "ArcGIS supports LAS versions 1.0, 1.1, 1.2, and 1.3. The waveform information that is available when using LAS version 1.3 is not supported. Additionally, LAS version 1.4 files that are 1.3 compliant, containing point record formats 0 through 5, are supported. LAS version 1.4 files containing point record formats 6 through 10 are not supported. "

‎03-13-2013

this would be extremely useful, particularly for creating a polyline representative of a continuous collection of data from a terrestrial lidar scan.

‎07-26-2012

I should also say that I am printing out the ID of the points that I am saving off since this is still in development phase. That may also come into play.

‎07-26-2012

Hello everybody, I am working with the LAS Dataset API in the ArcGIS ArcObjects for Java SDK. I am aiming to re-classify some LAS points within my LAS dataset that fall witihin a specific area, and am running into some "Out Of Memory" issues. My LAS Dataset is very dense, and currently I have thinned it so it contains 40 million points. I am looping through each point individually using the nextAttrLong method from the ILasPointEnumerator interface: nextAttrLong(int arraySize, int[] pPointCount, _WKSPointZ[] pPoints, int[] pAttribute, ILongArray pIntensity, ILongArray pFileIndices, IDoubleArray pPointIDs); What I am finding is that as I get above 20 million points the Java runtime is unable to allocate 4 bytes of memory to store the next index generated in pPointIDs, the index being of type INT. Since this is in Java, the point ID is only stored in the first element of the double array pPointIDs, so I am extracting that element and adding it to a different double array. The max allowable number by an INT in Java is 2,147,483,639 (found this through a search). This is a far cry from the number of points I am dealing with. One thing I tried is to loop through the points up to 20 million, do my reclassification, then loop through the rest of the points. This still results in the out of memory error. The full error is below: # # There is insufficient memory for the Java Runtime Environment to continue. # Native memory allocation (malloc) failed to allocate 2 bytes for GetStringUTFChars # # Out of Memory Error (memory/allocation.inline.hpp:58), pid=1208, tid=5284 # # JRE version: 7.0_05-b06 # Java VM: Java HotSpot(TM) Client VM (23.1-b03 mixed mode, sharing windows-x86 ) # Failed to write core dump. Minidumps are not enabled by default on client versions of Windows # The fact that it is failing to allocate memory for a String is confusing to me. I am not referencing any String variables in my code, so the only thing that comes to mind is something within the nextAttrLong() method. Any thoughts? Cheers, Joe McGlinchy

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Date Last Visited	‎11-11-2020 02:24 AM

My Ideas

Latest Contributions by JosephMcGlinchy

Re: Lidar point density

Re: Lidar point density

Re: Unable to run Segment Mean Shift on output from Principal Component Analysis

Re: NDVI differencing

Re: Problem Importing GeoTIFF Terrain Into CityEngine

Re: Problem Importing GeoTIFF Terrain Into CityEngine

Re: Gray level co-occurrence matrices (GLCMs) for texture analysis

Re: Fix Zonal Histogram tool in Spatial Analysis Extension

Enhanced support for LAS version 1.4 files

Re: DataManagement: 3D-Point-Features to 3D-Line-Features

Re: point ID limitation for LAS Dataset / Java SDK

point ID limitation for LAS Dataset / Java SDK

Enhanced support for LAS version 1.4 files