Without any parameter tweaks, ODM chooses a good compromise between quality, speed and memory usage. If you want to get higher quality results, you need to tweak some parameters:
*``--orthophoto-resolution`` is the resolution of the orthophoto in cm/pixel. Decrease this value for a higher resolution result.
*``--ignore-gsd`` is a flag that instructs ODM to skip certain memory and speed optimizations that directly affect the orthophoto. Using this flag will increase runtime and memory usage, but may produce sharper results.
*``--texturing-nadir-weight`` should be increased to ``29-32`` in urban areas to reconstruct better edges of roofs. It should be decreased to ``0-6`` in grassy / flat areas.
*``--texturing-data-term`` should be set to `area` in forest areas.
*``--mesh-size`` should be increased to ``300000-600000`` and ``--mesh-octree-depth`` should be increased to ``10-11`` in urban areas to recreate better buildings / roofs.
Camera calibration is a special challenge with commodity cameras. Temperature changes, vibrations, focus, and other factors can affect the derived parameters with substantial effects on resulting data. Automatic or self calibration is possible and desirable with drone flights, but depending on the flight pattern, automatic calibration may not remove all distortion from the resulting products. James and Robson (2014) in their paper `Mitigating systematic error in topographic models derived from UAV and ground‐based image networks <https://onlinelibrary.wiley.com/doi/full/10.1002/esp.3609>`_ address how to minimize the distortion from self-calibration.
..figure:: images/msimbasi_bowling.png
:alt:image of lens distortion effect on bowling of data
*Bowling effect on point cloud over 13,000+ image dataset collected by World Bank Tanzania over the flood prone Msimbasi Basin, Dar es Salaam, Tanzania.*
To mitigate this effect, there are a few options but the simplest are as follows: fly two patterns separated by 20°, and rather than having a nadir (straight down pointing) camera, use one that tilts forward by 5°.
As this approach to flying can be take longer than typical flights, a pilot or team can fly a small area using the above approach. OpenDroneMap will generate a calibration file called cameras.json that then can be imported to be used to calibrate another flight that is more efficiently flown.
Alternatively, the following experimental method can be applied: fly with much lower overlap, but two *crossgrid* flights (sometimes called crosshatch) separated by 20° with a 5° forward facing camera.
* Crossgrid overlap percentages can be lower than parallel flights. To get good 3D results, you will require 68% overlap and sidelap for an equivalent 83% overlap and sidelap.
* To get good 2D and 2.5D (digital elevation model) results, you will require 42% overlap and sidelap for an equivalent 70% overlap and sidelap.
By default ODM does not create DEMs. To create a digital terrain model, make sure to pass the ``--dtm`` flag. To create a digital surface model, be sure to pass the ``--dsm`` flag.
..figure:: images/digitalsurfacemodel.png
:alt:image of OpenDroneMap derived digital surface model
:align:center
For DTM generation, a Simple Morphological Filter (smrf) is used to classify points in ground vs. non-ground and only the ground points are used. The ``smrf`` filter can be controlled via several parameters:
*``--smrf-scalar`` scaling value. Increase this parameter for terrains with lots of height variation.
*``--smrf-slope`` slope parameter, which is a measure of "slope tolerance". Increase this parameter for terrains with lots of height variation. Should be set to something higher than 0.1 and not higher than 1.2.
*``--smrf-threshold`` elevation threshold. Set this parameter to the minimum height (in meters) that you expect non-ground objects to be.
*``--smrf-window`` window radius parameter (in meters) that corresponds to the size of the largest feature (building, trees, etc.) to be removed. Should be set to a value higher than 10.
Changing these options can affect the result of DTMs significantly. The best source to read to understand how the parameters affect the output is to read the original paper `An improved simple morphological filter for the terrain classification of airborne LIDAR data <https://www.researchgate.net/publication/258333806_An_Improved_Simple_Morphological_Filter_for_the_Terrain_Classification_of_Airborne_LIDAR_Data>`_ (PDF freely available).
Overall the ``--smrf-threshold`` option has the biggest impact on results.
SMRF is good at avoiding Type I errors (small number of ground points mistakenly classified as non-ground) but only "acceptable" at avoiding Type II errors (large number non-ground points mistakenly classified as ground). This needs to be taken in consideration when generating DTMs that are meant to be used visually, since objects mistaken for ground look like artifacts in the final DTM.
..figure:: images/smrf.png
:alt:image of lens distortion effect on bowling of data
:align:center
Two other important parameters affect DEM generation:
*``--dem-resolution`` which sets the output resolution of the DEM raster (cm/pixel)
*``--dem-gapfill-steps`` which determines the number of progressive DEM layers to use. For urban scenes increasing this value to `4-5` can help produce better interpolation results in the areas that are left empty by the SMRF filter.
Weather conditions modify illumination and thus impact the photography results. Best results are obtained with evenly overcast or clear skies. Also look for low wind speeds that allow the camera to remain stable during the data collection process.
In order to avoid shadows which on one side of the stockpile can obstruct feature detection and lessen the number of resulting points, always prefer the flights during the midday, when the sun is at the nadir so everything is consistently illuminated.
Also ensure that your naked eye horizontal visibility distance is congruent with the planned flight distances for the specific project, so image quality is not adversely impacted by dust, fog, smoke, volcanic ash or pollution.
Flight pattern
===============
Most stockpile measurement jobs does not require a crosshatch pattern or angled gimbal as the resting angle of stockpile materials allows the camera to capture the entire stockpile sides. Only some special cases where erosion or machinery operations causes steep angles on the faces of the stockpile would benefit of the crosshatch flight pattern and angled camera gimbal but consider that these additional recognized features come at a cost, (in field labor and processing time) and the resulting improvements are sometimes negligible.
In most of the cases a lawn mower flight pattern is capable of producing highly accurate stockpile models.
..figure:: images/lawnmower_pattern.png
:alt:a simple lawnmower flight pattern can produce accurate results
:align:center
Recommended overlap would be between 75% and 80% with a sidelap in the order of 65% to 70%. It is also recommended to slightly increase overlap and sidelap as the flight height is increased.
Flight height
==============
Flight height can be influenced by different camera models, but in a general way and in order to ensure a balance between image quality and flight optimization, it is recommended to be executed at heights 3 to 4 times the tallest stockpile height. So for a 10 meter stockpile, images can be captured at a height of 40 meters.
As the flight height is increased, it is also recommended to increase overlap, so for a 40 meter height flight you can set a 65% sidelap and 75% overlap, but for a planned height of 80 meters a 70% sidelap and 80% overlap allowing features to be recognized and properly processed.
GCPs
=====
To achieve accuracy levels better than 3%, the use of GCP’s is advised. Typically 5 distributed GCP are sufficient to ensure accurate results.
When placing or measuring GCP, equipment accuracy should be greater than the GSD. Survey grade GNSS and total stations are intended to provide the required millimetric accuracy.
For further information on the use of GCPs, please refer to the `Ground Control Points section <https://docs.opendronemap.org/gcp.html>`_.
Processing parameters
======================
A highly accurate model can be achieved using WebODM high resolution predefined settings. Then you can further adjust some parameters as necessary.
If using ODM, these this reference values can help you configure the process settings.
--dsm: true
--dem-resolution 2.0
--orthophoto-resolution 1.0
--feature-quality high
--pc-quality high
Measuring
==========
As almost 50% of the material will be found in the first 20% of the stockpile height, special care should be taken in adequately defining the base plane.
..figure:: images/stockpile.png
:alt:almost 50% of the material will be found in the first 20% of the stockpile height
:align:center
In WebODM Dashboard, clic on "view map" to start a 2D view of your project.
Once in the 2D map view, clic on the "Measure volume, area and length" button.
..figure:: images/measurement1.png
:alt:clic on the "Measure volume, area and length" button
:align:center
then clic on "Create a new measurement"
..figure:: images/measurement2.png
:alt:clic on "Create a new measurement"
:align:center
Start placing the points to define the stockpile base plane
..figure:: images/measurement3.png
:alt:Define the stockpile base plane
:align:center
Clic on "Finish measurement" to finish the process.
..figure:: images/measurement4.png
:alt:Clic on "Finish measurement" to finish the process
:align:center
Dialog box will show the message "Computing ..." for a few seconds, and after the computing is finished the volume measurement value will be displayed.
..figure:: images/measurement7.png
:alt:Clic on "Finish measurement" to finish the process
:align:center
If you are using the command line OpenDroneMap you can use the dsm files to measure the stockpile volumes using other programs.
Also consider that once the limits of the stockpile are set in software like `QGis <https://www.qgis.org>`_, you will find there are some ways to determine the base plane. So for isolated stockpiles which boundaries are mostly visible, a linear approach can be used. While for stockpiles set in slopes or in bins, the base plane is better defined by the lowest point.
Creation of a triangulated 3D surface to define the base plane is advised for large stockpiles. This is also valid for stockpiles paced on irregular surfaces.
Expected accuracy
=================
For carefully planned and executed projects, and specially when GSD is less than 1 cm, the expected accuracy should be in the range of 1% to 2%.
The resulting accuracy is comparable to the commercially available photogrammetry software and the obtained using survey grade GNSS equipment.
Since many users employ docker to deploy OpenDroneMap, it can be useful to understand some basic commands in order to interrogate the docker instances when things go wrong, or we are curious about what is happening. Docker is a containerized environment intended, among other things, to make it easier to deploy software independent of the local environment. In this way, it is similar to virtual machines.
A few simple commands can make our docker experience much better.
Using either the `CONTAINER ID` or the name, we can access any logs available on the machine as follows:
::
> docker logs 2518817537ce
This is likely to be unwieldy large, but we can use a pipe `|` character and other tools to extract just what we need from the logs. For example we can move through the log slowly using the `more` command:
::
> docker logs 2518817537ce | more
[INFO] DTM is turned on, automatically turning on point cloud classification
Pressing `Enter` or `Space`, arrow keys or `Page Up` or `Page Down` keys will now help us navigate through the logs. The lower case letter `Q` will let us escape back to the command line.
Sometimes we need to go a little deeper in our exploration of the process for OpenDroneMap. For this, we can get direct command line access to the machines. For this, we can use `docker exec` to execute a `bash` command line shell in the machine of interest as follows:
Docker has a lamentable use of space and by default does not clean up excess data and machines when processes are complete. This can be advantageous if we need to access a process that has since terminated, but carries the burden of using increasing amounts of storage over time. Maciej Łebkowski has an `excellent overview of how to manage excess disk usage in docker <https://lebkowski.name/docker-volumes/>`_.
`Learn to edit <https://github.com/opendronemap/docs#how-to-make-your-first-contribution>`_ and help improve `this page <https://github.com/OpenDroneMap/docs/blob/publish/source/tutorials.rst>`_!
