GDAL (or other open-source) equivalent of Arc's Point To Raster Tool

I'm trying to convert a python script that relies on arcpy into one that only relies on open source modules, but I'm having some trouble. The bulk of my processing lies in one Arc tool "Point to Raster." The goal is to take a csv file with lat/long coords and turn it into a raster, where each cell value is the number of points contained within that cell. When I'm able to use arcpy, the workflow looks like this:

Make XY event layer from csv of points --> Use Event layer as input in Point To Raster tool, with cell assignment = COUNT, specifying spatial extent and cellsize --> Mask out NoData values --> Write output to .tif

This works fine, and gives me my desired result, but now I need to write the same script without arcpy. I've found gdal tools that work for everything except the main process: Point to Raster. Do you know of any gdal utility (or reluctantly, some other open source tool) that could do what Arc's Point to Raster tool is doing? The 2 gdal tools that look like they could possibly accomplish this are:

  1. gdal_grid ( -This tool isn't meant to do exactly what I want it to do, although it does take a set of points (could be a shapefile, or a csv written as a VRT**) and turn it into a raster grid. I can specify the extent of the gridd along with its resolution, which is important. There is also an option to set grid cell values based on data metrics, with 'count' being one of them. However, under the count metric it states "A number of data points found in grid node search ellipse." What does 'grid node search ellipse' mean? Can I not assign values based on a number of data points found in each grid cell'? Is there a difference?

  2. gdal_rasterize ( -This one looks a little less promising, or maybe that's just because I don't entirely understand it. In summary, the tool "burns vector geometries (points, lines and polygons) into the raster band(s) of a raster image," which sounds like what I need, although there is no keyword to specify point count as the value to be burned, so I don't know.

A few more things:

  • **I was going to test using gdal_grid, but can't find anywhere how to convert a CSV to a VRT in python. I know it's possible by hand, every tutorial/help page I can find says something like: "Say you have a CSV with lat/long, you can write a VRT file for that CSV" then jumps right into "Here's what the VRT file looks like for the CSV." But HOW do I write this VRT file, in python? Several pages say to download an FW toolkit (or something) to create the CSV, but I need to do this within python as I'm automating the process for hundreds of CSVs. I tried ogr2ogr but got the error: "VRT driver does not support data source creation," so I'm at a loss. I could convert the csv to shapefile or some other OGR-recognized format, but the VRT is appealing because the shapefile/whatever that would be created would be very, very large and probably take a while to create. Which leads me to…

  • Each csv has an obsenely large amount of points: ~78 million. So python/numpy won't even read them without crashing. Additionally, the resulting raster will be global with fine resolution (1km), so a hack that manipulates the coordinates or loops through the points/cells is out of the question.

I'm not sure if I should put my CSV to VRT question in a separate thread, so my apologies if this is too much for one question.


  • Answer to how to turn the csv into a vrt file

For a CSV files with the x and y coordinates in the columns "x" and "y", the resulting VRT file is:

   /path/your.csv wkbPoint EPSG:…  

So in Python with the standard module xml.etree.ElementTree, for example (there are many other modules):

import xml.etree.ElementTree as ET root = ET.Element("OGRVRTDataSource") layer = ET.SubElement(root, "OGRVRTLayer") layer.attrib['name'] = "points" source = ET.SubElement(root, "SrcDataSource") source.text = "/path/your.csv" type = ET.SubElement(root, "GeometryType") type.text = "wkbPoint" srs = ET.SubElement(root, "LayerSRS") srs.text = "EPSG:… " geom_fld = ET.SubElement(root, "GeometryField") geom_fld.attrib["encoding"]= 'PointFromColumns' geom_fld.attrib["x"]= 'x' geom_fld.attrib["y"]= 'y' # save the file file = ET.ElementTree(root) file.write("test.vrt")

For part 1 of your question: The gdal_rasterize function is the one you want - you can specify the field to burn in using the -a option or the -3d option depending on how your file is structured. And, the -te, -ts and -tr options allow you to specify the grid extent and resolution (just pull these values directly from your original grid to keep everything aligned).

For part 2: I would subset the global grid into x lat/long box extents, and pull out the points that fall within that extent from the csv file over each loop (instead of converting the entire csv to points all at once, read line by line and pull only the points you need for that subset into a data structure, then convert lat/long to points and burn these into a raster). Once you've finished processing, mosaic the individual rasters produced back into a single file.


I have a DEM made of .adf files witch are ESRI Arc/info Grid. I can load it
easily with arcgis but it seems to be more complicated with SAGA.

I would like to import those files in SAGA. IT will be very nice if you can
help me.

You can just use the import/export - GDAL/OGR module/GDAL:Import raster and
open the w001001.adf file.

It works (as you expected I gess). Thanks a lot!

i cannot get the .adf file to actually show up and do anything with it? Any
suggestions. I downloaded it from USGS.

Could you please post the link to such a file which causes problems? And more
information about your system and SAGA version?

ok so i got the file to come in and i see my pretty little dem in there. Now i
can't seem to figure out exactly how to change my coordinate system? its in
state plane - washington - north - HARN - meters. I need it to be in the same
but in feet. And i need to know how to export it as a xyz file as well. Thanks

In case you just need to convert the unit from meters to feet you can use the
Grid Calculator module.

Have a look at the Import/Export-Grids - Export Grid to XYZ module for export.

I do need to convert meters to feet as well as Geographic to state plane
Missouri Central. Can you tell me the steps of the grid calculator module?
Thanks Volker.

This involves more than just a z-unit conversion and can not be done with the
Grid Calculator. Have a look at the Projection-Proj.4 module library. I posted
some instructions on reprojection here:

If you have many adf files, what do you do? Looking at my files and checking out the forum it seems I need to open the w001001.adf and w001001x.adf files. But I also have w001000.adf, w001000x.adf, z001001.adf, and z001001x.adf files. What to do with all of them? w001001.adf and w001000.adf are both 1.28 GB.

I am trying to import "SRTM-derived 1 Second Digital Elevation Models Version 1.0"
the DEM-S dataset.

"Data Format
Data is stored as continuous 32 bit Floating Point ESRI Grids (tiles and mosaic) and
ESRI shapefiles for some reference data. One second (

30 m) Grid tiles are
named per the latitude and longitude of the south west corner. A suffix of
‘dem1_0’, ‘dems1_0’ or ‘demh1_0’ has been applied to the tile names to
differentiate between the elevation models and the version number (in this case
version 1.0)."

I tried to import some of the files in SAGA - both simple drag and drop and also the suggested GDAL/OGR module/GDAL:Import raster - first does not do anything and second one fails.

there are several file pairs like:

w001000.adf (about 1 GB)
w001000x.adf (about 1 MB)

I had luck with loading it in QGIS but it automatically loads the whole Australia which is pretty huge and thus extremely slow. I need only a small part and wanted to perform some processing in SAGA.

7 Answers 7

You need to tell the compiler that you also want a setter. A common way is to put it in a class extension in the .m file:

Eiko and others gave correct answers.

Here's a simpler way: Directly access the private member variable.

@property (strong, nonatomic, readonly) NSString* foo

In the implementation .m file:

That’s it, that’s all you need. No muss, no fuss.

As of Xcode 4.4 and LLVM Compiler 4.0 (New Features in Xcode 4.4), you need not mess with the chores discussed in the other answers:

  • The synthesize keyword
  • Declaring a variable
  • Re-declaring the property in the implementation .m file.

After declaring a property foo , you can assume Xcode has added a private member variable named with a prefix of underscore: _foo .

If the property was declared readwrite , Xcode generates a getter method named foo and a setter named setFoo . These methods are implicitly called when you use the dot notation (my Object.myMethod). If the property was declared readonly , no setter is generated. That means the backing variable, named with the underscore, is not itself readonly. The readonly means simply that no setter method was synthesized, and therefore using the dot notation to set a value fails with a compiler error. The dot notation fails because the compiler stops you from calling a method (the setter) that does not exist.

The simplest way around this is to directly access the member variable, named with the underscore. You can do so even without declaring that underscore-named variable! Xcode is inserting that declaration as part of the build/compile process, so your compiled code will indeed have the variable declaration. But you never see that declaration in your original source code file. Not magic, just syntactic sugar.

Using self-> is a way to access a member variable of the object/instance. You may be able to omit that, and just use the var name. But I prefer using the self+arrow because it makes my code self-documenting. When you see the self->_foo you know without ambiguity that _foo is a member variable on this instance.

By the way, discussion of pros and cons of property accessors versus direct ivar access is exactly the kind of thoughtful treatment you'll read in Dr. Matt Neuberg's Programming iOS book. I found it very helpful to read and re-read.

2 Answers 2

Found the book (=Succesful drawing by A.Loomis) where the questioner's image was taken from.

This image shows a vertically standing rectangular box, which is divided to equal smaller boxes. The box has square bottom. The box is seen diagonally, the nearest and the most distant corners are on the same sight line. It's drawn with 2-point perspective - the image plane is vertical, there's no top nor bottom vanishing point. Left and right vanishing points are out of the image.

The idea of the image is to show how to get apparently right horizontal division to equal parts. The bottom measuring line, the top vanishing point (must be on the horizon line. ) and the dashed measuring rays divide the visible sides of the bottom square. The horizontal division is extended vertically from the bottom to the top with vertical lines. It's OK because all vertical lines of the target stay vertical in a 2-point perspective drawing.

The vertical division to equal parts can be drawn in 2-point perspective by using the same unit along a vertical line, there's no shortening upwards nor downwards. This image shows an example of it. The vertical scale is drawn on the nearest vertical edge. More distant vertical lines, of course have shortened scale, but the division is also there uniform due the 2-pt perspective.

But one complex thing - how to get the same length unit for horizontal and vertical divisions of the box is coldly skipped. The vertical division in this image is eyeballed i.e. drawn as the artist happens to feel right. Or it's got with some method from elsewhere. There's absolutely no mention in the image nor in the accompanying text of does the used vertical division make the box divided to equal cubes or not.

Eyeballing some perspective aspects doesn't make the book crap. The book is for persons who have the ability to make successful eyeballing. Otherwise the wouldn't learn to draw. More rigorous on math based perspective drawing methods can be found. I guess the questioner knows their existence, because his question seems to be based on an apparent contradiction between the shown image and the common 2 measuring point method.

The 2 measuring point method to get consistent horizontal and vertical lengths and distances in images looks simple enough to be practiced at least in 2-pt perspective, but the math behind it is tricky. For me the easiest method is to make a 3D model, but that's useless for those who want paint or draw manually.

If I was forced to draw perspective images manually I would construct the image with sight lines in 2 perpendicular projections. That's the old engineering way. It also would probably be useless for an artist because the needed top and side view engineering drawings do not exist. The artist creates something new as he draws, so the perspective drawing tricks must work on his drawing paper.

One example which uses a rigorous method to get the dimensions right:

The next image shows a diagonal view perspective image of a square. It's constructed artistically - not a slightest idea how long is the side of the square. The vanishing points are both placed on the horizon line as far (=S) from the center to make it present a horizontal square seen diagonally.

The next goal is to continue and extrude this square to cube. The image should use only 2-pt perspective. That means both the image plane and the extrusion direction are vertical. For simplicity let the nearest corner of the square be on the image plane.

Now the problem is how high should the nearest vertical edge be in the image if it's a cube? Someone may think that the drawn horizontal projection X of the edge of the square could be used somehow, maybe cleverly scaled. It can work as is if the viewing direction is right, but for this exaggerated perspective it looks too short.

Tiresome triangle proportion calculations in the 3D scene of the perspective image construction with sight lines show, that the length of the edges of the actual square can be calculated from this perspective drawing. Assuming that the nearest corner is on the image plane (+tricky triangle math) give the next formula for the square edge length A:

A = X * S * sqrt(2)/(S-X) (see NOTE1)

In this drawing X = 32,0 millimeters and S = 68,5 millimeters. The calculated square side length A = 85,4 millimeters. That A should be the height of the cube and the nearest edge which is on the image plane should be drawn 85,4 mm high.

The image looks very distorted, but a quick check in a 3D CAD program gave the same result. The blue cube has edge length = 85,4 mm. The 2-point perspective drawing is made with sight lines. The drawing session image:

The straight on the face image with no extras:

NOTE1: the formula looks not at all demonstrative of what it presents. But it's got with proportionality theorems of triangles and contains only things which exist in the drawing. So a line pattern which fulfills the same proportionality must very likely be possible to insert with a ruler and compass directly to the perspective drawing. The established simple to use drawing-only way to find the edge length of the already in 2-pt perspective drawn square is known as the measurement (or measure-) point method. It covers also other cases than the symmetric diagonal view. The method is thoroughly described here:

BTW. The method is described thoroughly but the explanation is complex. It becomes nearly trivial when one watches a 3D image of the perspective imaging session.

The square is blue and its image is orange. When the violet sight line turns around one corner of the image to the image plane (=to the yellow line) so that the end on the original square corner moves along circular arc finally showing the edge length A, the top end draws on the horizontal plane an arc from the station point to the image plane. The end of the arc is the measure point. The placement of the station point can be constructed in the image plane and the equivalent arc is easy to draw.

Software similar to or like ArcMap

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How to fill no value holes in DEM?

Assuming you're using ArcGIS, raster calculator or a raster math script is going to be your friend here. Looking at the image, you're going to have to average the values around the holes to be able to have something usable for whatever you're doing with the DEM later.

If you are using a version of ArcMap that supports the Imagery Analysis window and functions I would recommend checking out the Elevation Void Fill function as well.

From QGIS you can use the GDAL tool ɿill nodata'. See GDAL Tools Plugin

In Arc there is a "Fill Sinks" tool for solving this issue. I have only used it while creating DEM stream hydrography.

This will also affect other areas though. OP should make sure the side effects are acceptable.

We can't help you unless you tell us what software you have on hand.

Hi guys, I would just like to clarify, I only want to fill the 2 holes inside. Do you think the fill methods would affect the area outside my DEM?

For these cases, I've always used a coarser elevation model (e.g. GMTED2010 which you can download from

Then using Raster Calculator in ArcGIS, you can easily substitute NoData areas with values from the global raster:

Con(IsNull(YourDEM.tif), GlobalDEM.tif, YourDEM.tif)

I usually multiply the dem in the Raster calculator by 1. This will help offset any odds and ends.

I'm curious. What issues would that fix?

I have arcgis but can dl other software if needed(i know piratebay)

What software would you even pirate for this if you already have ArcGIS? There are tools for this in Arc, it can be done using Python as well, or you could download open source software that will also do this like QGIS or Saga.

Don't pirate software, seriously. if you can't do it in open source then there are a ton of packages that offer free evaluation copies or cheap personal use licenses.

GRASS GIS – Geographic Resources Analysis Support System

Lots of GIS application are available in the market but still, there is a lot of demand for GRASS GIS, But what is GRASS GIS?

GRASS GIS – Geographic Resources Analysis Support System is a geographic information system (GIS) software package which is freely available and is used for database management, geospatial data analysis, analysis, image processing, spatial and temporal modelling with visualization. It can handle raster, topological vector and graphics data. GRASS GIS provides over 350 functions or modules to render maps and images monitoring with a capability to manipulate, manipulate raster and vector data including vector networks, process multispectral image data and create, manage, and store spatial data. These modules are written in C, C++, Python and other scripting languages. It supports libraries i.e. GDAL for raster and OGR for vector data analysis.

GRASS GIS application is free and is licensed as open-source software under the GNU General Public License (GPL). It can run on multiple operating systems, including OS X, Windows and Linux operating system. Users can interact with the software interface through a graphical user interface (GUI) or by plugging into GRASS via alternative software such as QGIS application. A newer version of QGIS application can be executed within the GRASS environment. It is an official project and founding member of the Open Source Geospatial Foundation (OSGeo).


There are numerous properties that the data manager should verify regarding all elevation data. The data manager must review and decide which components are important to maintain as well as which metadata fields to expose to the data users. The metadata listed below is recommended for the purposes of both quality assurance and system configuration.

Metadata the data manager should verify includes:

  • Data source or owner.
  • Copyright.
  • Horizontal coordinate system (projection, datum, and units).
  • Vertical datum (specific model, noting if it is ellipsoidal or orthometric) and units (feet or meters).
  • Horizontal accuracy (typically measured as CE90 or CE95, but also may be reported as RMS error or RMSE).
  • Vertical accuracy (typically measured as LE90).
  • Resolution (sample spacing stored in the data file and is not the same as the horizontal accuracy of the data).
  • Elevation surface type (DEM versus DSM).
  • How is NoData defined in this dataset:
    • Are there areas of NoData?
    • If yes, is it represented by a single value?
    • Is the NoData limited to the edges of the datasets or are there holes of NoData within the valid data?
    • Some products have associated feature classes that define void regions. Look to see if these areas were filled with a value and if it is the NoData value.

    For unique metadata fields, it may be necessary to manually add these to the mosaic dataset's attribute table, such as the horizontal and vertical accuracies. This way, you can easily query the mosaic dataset for this information.

    It is worth creating a list for the products (or subproducts) you will be using because you may need to modify the data within the mosaic dataset, such as using the Arithmetic function to convert from one unit to another.

    No module named gdal

    QGIS was working just fine until I installed the latest version. Now QGIS plugins needing GDAL are broken. Tried total QGIS uninstall and registry scrubbing, and modifying the QGIS path settings. Can't seem to figure it out. Any ideas? Error shown below.

    Couldn't load plugin ɼwsi' due to an error when calling its classFactory() method

    Traceback (most recent call last):
    File "C:PROGRA

    1.3/apps/qgis/./", line 335, in _startPlugin
    plugins[packageName] = package.classFactory(iface)
    File "C:Users/beggers/AppData/Roaming/QGIS/QGIS3profilesdefault/python/pluginscwsi\", line 35, in classFactory
    from .cwsi import CWSI
    File "C:PROGRA

    1.3/apps/qgis/./", line 799, in _import
    mod = _builtin_import(name, globals, locals, fromlist, level)
    File "C:Users/beggers/AppData/Roaming/QGIS/QGIS3profilesdefault/python/", line 43, in
    import gdal
    File "C:PROGRA

    1.3/apps/qgis/./", line 799, in _import
    mod = _builtin_import(name, globals, locals, fromlist, level)
    ModuleNotFoundError: No module named 'gdal'

    Python version: 3.9.5 (tags/v3.9.5:0a7dcbd, May 3 2021, 17:27:52) [MSC v.1928 64 bit (AMD64)]
    QGIS version: 3.18.3-Zürich Zürich, 735cc85be9

    The OpenTopography Tool Registry provides a community populated clearinghouse of software, utilities, and tools oriented towards high-resolution topography data (e.g. collected with lidar technology) handling, processing, and analysis. Tools registered below range from source code to full-featured software applications. We welcome contributions to the registry via the Contribute a Tool page.

    Description: GRASS is free Geographic Information System (GIS) software used for geospatial data management and analysis, image processing, graphics/maps production, spatial model ing, and visualization. GRASS is currently used in academic and commercial settings around the world, as well as by many governmental agencies and environmental consulting companies. GRASS is an official project of the Open Source Geospatial Foundation.

    GRASS has a number of functions related to lidar and high-resolution DEM processing and analysis. lidar specific elements of GRASS are discussed here:

    Description: GDAL is a translator library for raster geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents a single abstract data model to the calling application for all supported formats. It also comes with a variety of useful commandline utilities for data translation and processing.

    GDAL is a powerful tool for converting digital elevation model (DEM) formats, performing coordinate system conversions, and the gdaldem utility provides basic processing functionality such as generation of hillshades and slope maps.

    Description: The Point Cloud Library (or PCL) is a large scale, open project for point cloud processing.

    The PCL framework contains numerous state-of-the art algorithms including filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.

    PCL is released under the terms of the BSD license and is open source software. It is free for commercial and research use. The project is financially supported by multiple companies, including: Willow Garage, NVidia, Google, and Toyota

    Description: ArcGIS (version 10.1) geoprocessing script tools for removing pits (or sinks) from Digital Elevation Model s using a combination of cut and fill. This alternative to the standard Fill tool provides more realistic flow paths with less required manual adjustment. Ideal for high-resolution datasets such as LiDAR.

    The tool also allows users to mark specific depressions to be left unmodified by setting the lowest cell to have a value of No Data. This feature can be used to establish reservoirs as well as known drainage features such as storm sewer inlets.

    Also contains a C++ executable that can be run via command line inputs independent of ArcGIS, and which operates on ASCII grid files.

    Description: The FUSION/LDV analysis and visualization system consists of two main programs, FUSION and LDV (LIDAR data viewer), and a collection of task-specific command line programs. The primary interface, provided by FUSION, consists of a graphical display window and a control window. The FUSION display presents all project data using a 2D display typical of geographic information systems. It supports a variety of data types and formats including shapefiles, images, digital terrain model s, canopy surface model s, and LIDAR return data. LDV provides the 3D visualization environment for the examination and measurement of spatially-explicit data subsets. Command line programs provide specific analysis and data processing capabilities designed to make FUSION suitable for processing large LIDAR acquisitions.

    Command line utilities and processing programs, called the FUSION LIDAR Toolkit or FUSION-LTK, provide extensive processing capabilities including bare-earth point filtering, surface fitting, data conversion, and quality assessment for large LIDAR acquisitions. These programs are designed to run from a command prompt or using batch programs.

    FUSION runs on all current versions of Windows and has been successfully used on LINUX systems using WINE. The FUSION/LDV visualization system is GUI based. The command line tools require the use of batch files to be most effective.

    Description: Landlab is a Python-based model ing environment that allows scientists and students to build numerical landscape model s. Landlab was designed for disciplines that quantify earth surface dynamics such as geomorphology, hydrology, glaciology, and stratigraphy, but can also be used in related fields.

    Landlab provides components to compute flows (such as water, sediment, glacial ice, volcanic material, or landslide debris) across a gridded terrain. With its robust, reusable components, Landlab allows scientists to quickly build landscape model experiments and compute mass balance across scales.

    Watch the video: Arc toolbox 19 - converts point features to a raster dataset (October 2021).