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Task Options

When creating a task, press the Edit button next to the Options field:

Accessing task options from the cloud interface

In general, it's a good idea to begin with the default settings, which usually work well for most datasets, and make adjustments as necessary.


3D Tiles are a format specification for visualizing and interacting with 3D geospatial content. You can view these files using software like Cesium. WebODM Lightning can generate point clouds and textured 3D models in 3D Tiles format. Turn on this option to generate them.


Automatically calculates a 2D polygon that encloses the camera pose locations. This polygon is subsequently employed as an input for the boundary option. The polygon is generated using a convex hull and it's adjusted with a distance buffer that scales with the flight altitude, with higher altitudes leading to larger buffers.

Boundary computed from camera poses (dots)


Manually adjust the distance buffer value (in meters) for auto-boundary.


Utilizes artificial intelligence techniques to automatically create image masks for background removal. This is particularly valuable for generating 3D models of individual objects. However, it may not work well in aerial scenes.

See also sky-removal.


Specify a single polygon boundary in GeoJSON format, which is used to define the reconstruction area.

GeoJSON polygons can be created using software like QGIS or online tools like Additionally, you can automatically generate them using the auto-boundary option.

If the crop option is set to zero, the boundary polygon can also serve as the crop area for DEMs and orthophotos.


Digital camera sensors quantify the incoming light. Prior to reaching the sensor, light traverses through a camera lens. Lenses introduce different degrees of distortion into photos, with the specific type of distortion determined by the lens shape. This distortion can range from pronounced, such as in fisheye or wide-angle lenses, to more subtle in the case of perspective lenses. It's essential to recognize that some level of distortion is invariably present.

WebODM Lightning offers support for multiple lens models and automatically selects the most suitable one by considering the information available in the EXIF1 and XMP2 tags of the images. Nevertheless, there are instances where such information is absent. In these cases, if you encounter difficulties when processing images captured with a fisheye lens, it's advisable to manually designate either fisheye or fisheye_opencv as the camera-lens option. As a general guideline, when your input images exhibit noticeable distortion, it's a prudent approach to manually configure this option with the appropriate value.

autoNormalDefaults to brown, unless the XMP tag GPano:ProjectionType or Camera:ModelType contains a value from this table
perspectiveNormalHandles radial distortion
brownNormalHandles radial, tangential, and principal point distortions
fisheyeUltra wide-angleHandles radial distortion
fisheye_opencvUltra wide-angleHandles radial distortion, tangential, and principal point distortions
spherical360Handles spherical projection for 360 images
equirectangular360Same as spherical (legacy name)
dualUltra wide-angle / NormalHandles radial distortion from sensors that can capture both fisheye and perspective images, transitioning from one to the other.

Please be aware that utilizing this option applies the same camera lens model to all images, even if they originate from different cameras. To apply distinct models for different cameras, it's necessary to ensure that the images have the appropriate XMP tags set.


By default WebODM Lightning estimates the camera model's distortion parameters from the input images. This option allows you to choose a precomputed set of parameters instead from another task. You can do this by providing a cameras.json file, which is generated after processing a dataset and can be downloaded from the cloud interface by clicking Download AssetsCamera Parameters. This feature can be helpful in improving the accuracy of certain datasets, especially those that didn't follow good image capture guidelines.


The crop area for orthophotos and DEMs is calculated from the point cloud, first by defining a convex hull around the points and then shrinking it by crop amount (in meters).

Point cloud (left) and cropped bounds (right)

This option can be set to zero to skip cropping.

One can also set the boundary option and set this option to zero to manually define the crop area.


DEMs are computed from the point cloud. To speed up the process, you can use this option to reduce the number of points used. Setting it to 3 keeps every third point, discarding the rest to speed up computation.

The default value of 1 includes all points. Setting it to 50 keeps approximately 2% of the original points and discards around 98%. You can calculate this percentage by:

decimation = 50
print((1 / decimation) * 100) # <-- 2%


An Euclidean map is a georeferenced image created from Digital Elevation Models (DEMs) before filling any gaps. In this image, each pixel represents the geometric distance to the nearest void, null, or NODATA pixel. It serves as a visual indicator of how far a value in the DEM is from an area with no data. This is valuable when you want to distinguish areas in the DEM that are based on actual point cloud values from those filled with interpolation.

In the Euclidean map, every pixel with a value of zero indicates that the corresponding location in the DEM was filled using interpolation, as the distance from a NODATA pixel to itself is zero. You can generate this map by turning on this option.

DEM before hole filling (left) and corresponding euclidean map (right)

The resulting map will be available from Download AssetsAll Assets in the odm_dem folder.


DEMs are image grids with cells that must have values. Cells can have zero, one, or more points based on the raster's resolution. Assigning values to all cells, even those without direct points, is crucial to avoid gaps. To find the right radius, WebODM Lightning computes multiple DEMs with varying radii, stacking results from small radii (more accuracy, more gaps) to large radii (less accuracy, fewer gaps). If gaps persist, it fills them with less accurate interpolation. The number of layers depends on this option.

Pixels and points (left), radius of 0.5 (middle) and radius of 1 (right)
Gap fill interpolation with 2 DEM layers


This option specifies the output resolution of DEMs in cm / pixel.

Pixels in a raster DEM


This option creates a digital surface model (DSM). DSMs are created by identifying the highest elevation values in a point cloud, which includes terrain and various structures like buildings and trees. When two points coincide, only the tallest point is considered. Any gaps in the point cloud are filled using the method detailed in dem-gapfill-steps.


This option creates a digital terrain model (DTM). DTMs are created by applying a hybrid method that combines a simple morphological filter3 (SMRF) with artificial intelligence. Enabling this option also activates the pc-classify feature. Non-ground points are removed before DTM calculation. Any gaps in the point cloud are filled using the process explained in dem-gapfill-steps. For additional details refer to the pc-classify option.

DSM (left) vs. DTM (right)


Instead of processing the entire photogrammetry pipeline, the pipeline will stop the execution at the chosen step.

datasetLoad Dataset
opensfmStructure From Motion
openmvsMulti View Stereo
odm_filterpointsPoint Filtering


For flat areas (agriculture fields), this option can save some substantial computation time by not requiring the construction of the dense point cloud used for orthorectification. This option does not work well in urban scenes due to excessive relief displacement artifacts.

Normal (top) vs. fast-orthophoto (bottom)


The photogrammetry process starts by identifying points of interest (features) from the input images. To expedite this, extraction is performed on a scaled-down version of the input images, determined by a scaling factor.

high1/2 (default)

For example, choosing medium uses 1/4 of the original image size. The default value works for most cases, without affecting image sizes or orthophoto resolution. Sometimes, decreasing this value can be helpful in forest areas that lack sufficient overlap.


WebODM Lightning provides multiple algorithms for extracting image features. For the most consistent and reliable results, we recommend using the default dspsift algorithm. However, in specific scenes or situations, you may benefit from using alternative algorithms. Refer to the table below.

siftGeneral-purpose, works well in most cases4
dspsiftGeneral-purpose, slower but generally more accurate than sift. Performs better in scenes with low overlap or vegetation5
akazeGeneral-purpose, can perform better on scenes with fewer objects of interest (e.g. forests, vegetation)6
hahogGeneral-purpose, similar to sift. It's the only one that works with matcher-type bow7
orbFast, but does not work well with images that have scale variations (images taken at varying altitudes)8


When a GCP file is utilized, the default behavior is to disregard all GPS data, relying solely on the GCP file for georeferencing. The underlying assumption is that GCP data is more accurate than GPS. However, if the GPS data is highly accurate (e.g., with RTK9 correction), enabling this option directs the program to use both GCP and GPS data for georeferencing.


GPS data has a certain level of accuracy. This value is used to specify how much GPS data should be constrained during the photogrammetry process.

Typically, accuracy information is obtainable from XMP tags in the images. WebODM Lightning uses twice the number indicated in any of the following tags (to account for underestimation):

  • drone-dji::RtkStdLon
  • drone-dji::RtkStdLat
  • drone-dji::RtkStdHgt
  • Camera::GPSXYAccuracy
  • GPSXYAccuracy
  • Camera::GPSZAccuracy
  • GPSZAccuracy

If multiple tags are present, the maximum value is used. If no tags are available, the default is 10 meters.


During reconstruction image pairs are matched by identifying common features. The brute-force approach compares each image with every other, resulting in exhaustive but slow searching. For a 100-image dataset, it would require numerous comparisons.

print(100 * (100 - 1)) # <-- 9900 comparisons

To enhance efficiency, the program employs optimizations. The concept is that for datasets gathered uniformly, most images are paired with nearby ones. Using GPS data, the program quickly approximates which images are adjacent and excludes distant ones. This process is termed preemptive matching.

WebODM Lightning by default applies a graph connectivity approach for preemptive matching. It uses GPS locations to link images with edges through Delaunay triangulation10. If two images share an edge, they form a pair. To generate more pairs, the method shuffles GPS locations to create multiple graphs (a total of 50). All pairs from these graphs are considered for further matching.

Initial graph (left) and graph with randomly moved positions and new edges (right). Each edge represents an image pair

WebODM Lightning offers an alternative preemptive matching method that focuses on the nearest neighbors of each image instead of using a graph. You can enable this method by turning on this option:

Dots represent approximate image locations, extracted from EXIF tags. When matcher-neighbors is set to 8, only the 8 nearest neighbors (highlighted in gray) are considered for matching with image p1

This option can speed up processing by reducing the number of matching pairs, especially when GPS data is available. If no GPS information is provided, this option is disabled, and all image pairs are considered unless matcher-order is specified.


Like matcher-neighbors, this option decreases the number of candidate pairs for matching based on the sequential order of image filenames. For instance, if you have 3 images sorted by filename:

  • 1.JPG
  • 2.JPG
  • 3.JPG

With this option set to 1, the program will evaluate matches between:

  • 1.JPG and 2.JPG
  • 2.JPG and 3.JPG

This is because the "distance" between these image pairs in the list is 1. 1.JPG and 3.JPG have a distance of 2, so this pair will be excluded from matching.

This option determines the maximum distance between image filenames for them to be considered a matching pair. It is exclusively useful for datasets without GPS information, particularly for expediting the processing of sequentially ordered images, like frames extracted from videos.


After preemptive matching finds potential image pairs (as discussed in matcher-neighbors), further computation identifies the actual image pairs by comparing their features.

To expedite feature matching, specific algorithms have been developed, given the large number of features in each image.

OptionSearch For Features With
flannAn index powered by the Fast Library for Approximate Nearest Neighbors
bowA Bag Of Words11 approach

The default method, flann, is highly versatile, providing an excellent balance between accuracy and speed. bow is quicker but compatible only with HAHOG features and potentially misses some matches.


Controls the quality of the 3D textured models. A higher value results in a finer model. However, this comes at the cost of significantly increased processing time. The default value of 11 is suitable for most scenarios. Lower values (6-8) can be sufficient in flat areas, while setting it higher (12) can improve results in urban areas. When raising this option, consider increasing mesh-size as finer meshes require more triangles.

mesh-octree-depth 6 and mesh-size 10000 (top) vs. mesh-octree-depth 11 and mesh-size 1000000 (bottom)


WebODM Lightning automatically simplifies the textured 3D models by limiting their triangle count. A high triangle count prolongs subsequent processing steps. A low count can reduce model quality, while increasing it can enhance results, especially in urban areas requiring fine building details.


This option controls the minimum number of features detected in each image, increasing the potential for finding matches.

Features (red points) and matches between overlapping images (white lines). min-num-features controls the desired number of red points in each image

Increase this option when mapping areas with few discernible features, like forests.


This option is always turned on. No need to worry about it.


Enabling this option results in the program creating a cutline, which is a polygon within the crop area of the orthophoto that aims to trace feature edges.


The resulting cutline can be downloaded from the cloud interface by clicking Download AssetsAll Assets (odm_orthophoto/cutline.gpkg).


This option specifies the output resolution of the orthophoto in cm / pixel.

See also dem-resolution.


Points in a point cloud can be assigned classification values12 to indicate if they belong to the terrain (ground), a building, a tree (vegetation), or other categories. By default, all points are labeled as unclassified, and the software doesn't assign specific labels to points. Enabling this option utilizes a Simple Morphological Filter3 (SMRF) to identify and classify terrain points as ground. An AI classifier13 is then applied to the remaining (non-ground) points to recognize vegetation, buildings, and other structures. This process results in a classified point cloud.

Point cloud (top) and classification results (bottom)
smrf-scalarThis parameter makes the threshold dependent on the slope. To enhance results, consider slightly decreasing this value when raising the smrf-threshold, and vice versa..
smrf-slopeSet this parameter to the steepest common terrain slope, represented as the ratio of elevation change to horizontal distance change (e.g., 1.5 meters over 10 meters is 1.5 / 10 = 0.15). Increase it for terrains with significant slope variation, like hills and mountains, and reduce it for flat areas. For optimal results, it should be above 0.1 but not exceed 1.2.
smrf-thresholdDefines the minimum height (in meters) of non-ground objects. For instance, a setting of 5 meters is suitable for identifying buildings but may not suffice for recognizing cars. To identify cars, lower the value to 2 or even 1.5 meters (the average car height). This parameter significantly influences results.
smrf-windowSet this to the size of the largest non-ground feature in meters. If the scene primarily consists of small objects like trees, reduce this value. If there are larger objects like buildings, increase it. It's advisable to maintain a value above 10 meters.

SMRF has limitations, including occasional misclassification of buildings or trees as ground points (type II errors).

Input surface model
Terrain model created using default SMRF settings. Note a few houses were incorrectly included, and there are lingering artifacts near the edges of removed objects.
An improved terrain model obtained by setting smrf-threshold 0.3 (decreased), smrf-scalar 1.3 (increased), smrf-slope 0.05 (decreased) and smrf-window 24 (increased)


Noise from the point cloud can be partially removed using a combination of statistical and visibility filtering. This option sets the standard deviation threshold value for the statistical filter. In this context standard deviation is a measure of how spread out points are relative to their neighbors. The filter looks at the closest 16 neighbors for each point and computes their standard deviations, which gives a measure of how far each point deviates from the average distance to each other point. If a point is found to be too far away relative to its neighbors, thus having a standard deviation higher than the threshold, the point is labeled as an outlier.

The gray point is an outlier due to its high standard deviation

Adjusting this value too high retains noisy points, and setting it too low may eliminate valid ones. You can disable filtering by setting this option to zero.


This option affects the density of the point cloud. Higher values use higher resolution images according to a scale factor:

OptionScaling Factor

Different image dimensions also correspond to a multiplier value:

Largest Image Dimension (megapixels)Multiplier
< 62
6 - 421
> 421/2

The actual resolution of the images used for point cloud estimation is then calculated with:

resolution = max(320, max_image_dimension * scaling_factor * multiplier)

For example, in a dataset with 4000x3000 (12 megapixels) images, setting this option to high will use images scaled to 1000 pixels for computing a point cloud:

image = (4000,3000)
max_image_dimension = max(image) # <-- 4000
megapixels = image[0]*image[1]/1000000 # <-- 12
multiplier = 1 # From table
scaling_factor = 1/4 # pc-quality: high
print(max(320, max_image_dimension * scaling_factor * multiplier)) # <-- 1000 pixels


This option imposes an upper limit on the density of the dense point cloud, defined as a radius in meters that ensures no two points are closer than the specified value. For instance, a setting of 0.05 ensures points are at least 5 centimeter (0.05 meters) apart. This option is always set to at least 0.01.


A geometric refinement process is used to improve the point cloud. This process can take some time and can be skipped by using this option.

Elevation model with defaults (left) vs. pc-skip-geometric (right). Improved building and car definition on the left.


This option selects the band name (Red, Blue, Green, NIR, etc.) for reconstructing multispectral datasets. The chosen band name must match the Camera::BandName EXIF1 tag. Only the images associated with this band will be utilized for 3D reconstruction.

By setting it to auto (the default), the band will be automatically selected.


Radiometric calibration converts pixel values (digital numbers) into reflectance. WebODM Lightning can automate radiometric calibration and compute reflectance and temperature values for various sensors14.

noneNo radiometric calibration (digital number outputs)
cameraApplies black level, vignetting, gain, and exposure corrections based on information from the EXIF tags. Additionally, computes absolute temperature values when applicable
camera+sunSame as camera, but also applies corrections from a downwelling light sensor (DLS) when available


This option cannot be used. Don't worry about it.


Enables rolling shutter correction to enhance reconstruction accuracy in datasets captured with rolling shutter sensors. If GPS information is present in the input images, and the dataset was captured with a single camera, WebODM Lightning can correct for rolling shutter distortion by initially estimating the aircraft's velocity at the time each picture was taken. Some cameras store velocity information in the EXIF/XMP tags of the images (SpeedX, SpeedY, and SpeedZ), which provides the most reliable estimate. In the absence of these tags, velocity is estimated based on the time and positional differences between consecutive images.

speed = (position_2 - position_1) / (time_2 - time_1)

The accuracy of the formula mentioned above can be compromised in situations where the drone is stationary while capturing a picture, as it assumes continuous motion and sequential image capture. In such cases, the calculation may yield incorrect estimates, potentially degrading results. However, as long as most images can be assigned accurate velocity estimates, rolling shutter correction can still be effective, even if a few images have incorrect estimates.

Once the velocities are estimated, the program searches a database to retrieve the rolling shutter readout time for the camera sensor. This readout time represents the duration (in milliseconds) required for the sensor to capture an image. In cases where your camera is not listed in the database, a warning will appear in the task output:

[WARNING] Rolling shutter readout time for "make model" is not in 
our database, using default of 30ms which might be incorrect.
You can access the task output by expanding a task from the Dashboard and toggling the Task Output button to On. If the task output is truncated, first you'll need to download the task output to a file by pressing the Download To File button

With known camera velocities and sensor readout times, the correction is applied by shifting image features based on these factors. Subsequently, the reconstruction is repeated, effectively doubling the processing time but enhancing accuracy in datasets influenced by rolling shutter distortions.


You can estimate the correct sensor value by creating a cost-effective calibration device using an Arduino. Detailed instructions are available at


Overrides the default sensor readout time value used for rolling shutter correction.


There are three methods to reconstruct a scene:

incrementala general-purpose approach suitable for all scenes. It supports multiple cameras and adds them to the reconstruction incrementally, ensuring high reliability.
triangulationIf gimbal angles and GPS information are available, camera positions are initialized from those values in a single step and then iteratively improved. This method can yield better results and may be slightly faster than incremental. However, it's experimental and may not work with all camera types.
planarFor flat scenes, like a farm field, captured with a single camera at a constant altitude and a downward-facing view (nadir), this option is recommended. It processes 5-10 times faster than the incremental method and is compatible with multispectral datasets.


This option is always turned on. No need to worry about it.


If a user only needs an orthophoto, there's no need to create a complete 3D model. This option saves time by skipping the steps for generating a 3D model. Instead, it creates a 2.5D model, where elevation is extruded from the ground plane. While not a full 3D model, it works effectively for rendering orthophotos, although it can't accurately represent objects like overhangs.

See also use-3dmesh.


When capturing multispectral images, the sensors for each band are often slightly misaligned, causing small misalignments between the image bands. ODM automatically aligns these bands as part of its multispectral processing. If manual alignment has already been done using other software, you can disable the automatic alignment using this option.


If you don't require an orthophoto, this option can save you time by skipping the orthophoto generation step.


If you don't require a PDF report, this option can save you some time by skipping the report generation step.


Utilizes AI methods to automatically create image masks for sky removal. This is beneficial for datasets that include sky portions, especially in cases where oblique images are used for 3D structure capture. Sky areas can introduce noise in the 3D model, and this option helps in its reduction.

3D point cloud without (top) and with sky masks (bottom). Sceaux castle model generated from photos by Pierre Moulon


Sets the scalar variable for SMRF. See pc-classify.


Sets the slope variable for SMRF. See pc-classify.


Sets the threshold variable for SMRF. See pc-classify.


Sets the window variable for SMRF. See pc-classify.


By default, if a triangle in the 3D textured model isn't visible by any camera, it's removed from the output.

Unseen faces are removed from the textured mesh (top) vs. faces are kept with no color (bottom)

This option directs the program to retain all triangles.


The 3D models created by WebODM Lightning are in the Wavefront OBJ15 format. This format supports storing color information across multiple image files (textures). Each texture in the model is linked to a "material." WebODM Lightning typically uses multiple materials and textures when generating OBJ files by default. However, some software may have issues opening OBJs with multiple materials, or performing certain operations on meshes with multiple materials, which can be complex. This is especially true when editing the mesh in programs like Blender16.

Enabling this option will produce an OBJ file with a single material.


Images with significant color variations caused by differences in illumination and exposure need to be merged using a global optimization process. This process slightly affects reflectance/temperature values when processing multispectral datasets and it might be desirable to enable this option to turn it off.


The application of global seam leveling (discussed in texturing-skip-global-seam-leveling) may not completely eliminate smaller seams.

To tackle this, localized Poisson editing is used to blend images at texture patch seams. This "local" method only affects a small 20-pixel buffer around patch boundaries.

This option disables local seam leveling (not recommended).


This option creates static TMS17 tiles for orthophotos and DEMs, ideal for hosting and sharing maps on websites. These tiles work seamlessly with various viewers, like Leaflet18. DEM tiles are produced with a colored hillshade style and can be downloaded from the cloud interface by clicking on the Download Assets button.


By default, a 2.5D textured mesh is used for orthophoto rendering, which usually works well for most aerial datasets. However, it may yield suboptimal results, especially when nadir images (images with the camera pointed straight or nearly straight at the ground) are missing. Furthermore, for specific scenes like single building orbits with oblique images, a 2.5D mesh may not perform well.

This option instructs the program to utilize the full 3D model for orthophoto generation while skipping the creation of the 2.5D model. For additional information, see skip-3dmodel.


When a GCP file is uploaded with a dataset, it is always used for georeferencing. Enabling this option causes the program to disregard the GCP file and rely on location information from the images' EXIF1 tags instead.


Camera internal parameters are estimated and refined during reconstruction. Poor image capture practices can lead to incorrect estimations and a "doming" effect. Enabling this option keeps camera parameters fixed, potentially improving results when images have little geometric distortion.


This option will not magically fix problems associated with poor image captures.


This option increases the number of times that bundle adjustment is performed.

Turning on this option increases the total run-time, but can help increase the accuracy of the reconstruction in larger datasets that exhibit doming.


WebODM Lightning can process video files (.mp4, .mov, .lrv, and .ts) by extracting image frames at regular intervals. The program automatically filters out blurry and dark frames.

For DJI drones, if a matching subtitle (.srt) file is available, it will be used to add GPS information to the extracted images. The subtitle file should have the same filename as the video file, and it is case-sensitive. For example, video.mp4 should have a corresponding file.

This option allows you to set the number of images to extract from the video files.


This option defines the resolution of the images extracted from video files. For instance, if a video file has a resolution of 3840x2160 pixels and set this option to 2000, the extracted images will be 2000x1125 pixels in resolution.

See also video-limit.


  1. EXIF Tags: 2 3

  2. XMP Tags:

  3. SMRF: A Simple Morphological Filter for Ground Identification of LIDAR Data. 2

  4. SIFT: Scale Invariant Feature Transform.

  5. DSP-SIFT: Domain-Size Pooling in Local Descriptors.

  6. AKAZE: Accelerated-KAZE. KAZE is a Japanese word that means wind (a tribute to Iijima, the father of scale space analysis).

  7. HAHOG: Hessian Affine (point detector) + Histogram of Oriented Gradients (descriptor).

  8. ORB: Oriented FAST (point detector) and Rotated BRIEF (descriptor).

  9. RTK: Real Time Kinematic is a technique used to increase the accuracy of GPS positions using a stationary base station that sends out correctional data to the drone.

  10. Delaunay Triangulation:

  11. Bag-of-words model in computer vision:

  12. LAS 1.4 Specification:

  13. OpenPointClass: Fast and memory efficient semantic segmentation of 3D point clouds.

  14. Supported Multispectral Hardware:

  15. Wavefront OBJ:

  16. Blender:

  17. TMS: Tile Map Service:

  18. Leaflet: