Introduction to Processing Data with Pix4D Software

Introduction:

Pix4D mapper is a professional photogrammetry and drone mapping application that allows the user to upload images and analyze them with extreme detail. In this lab, we used sets of data that another class collected with various drones and sensors. Using these images, we went through the basics of Pix4D like how to upload images correctly, how to set coordinate systems, and processing the imagery. In later labs we will dive into aspects like GCPs but for now are focusing on the basics of the software.

Objectives:

#1: To demonstrate the ability to access, and log into Pix4D either via a remote connection or within the lab

#2: To engage in processing several forms of UAS data using Pix4D software

#3: To demonstrate a firm understanding of the basic concepts and data outputs of Pix4D

Methods/Assignment:

This assignment focuses around implementing the into the software correctly with some key takeaways for future use. To begin we looked at the image properties for the various images to get a better understanding of their details. For example, the camera make and model, the focal length, the ISO, and even the F-stop. We also wanted to know if the images are geotagged which is important for the upcoming steps. 

After analyzing the image properties, we loaded all of the images for the Mavic 2 Pro into the software. Pix4D has it's own set of image properties as well. (Figure.1) This window is important for adjusting settings before the images are loaded into the software. From here, we can change the coordinate system, the geolocation and orientation, (if the images are not geotagged we can upload the coordinate from a separate file) and adjust the camera model. In this case, the default coordinate system was correct so the only thing we needed to check was the cameras shutter. The camera on the Mavic 2 Pro uses a rolling shutter which can cause distortion in image processing. We wanted to ensure that the Shutter Model was set to Linear Rolling Shutter. This is because Pix4D has an algorithm that adjusts the imagery to compensate for this. (Figure.2) This window also shows a table with detailed information about our images such as name, latitude, longitude, and altitude. 

(Figure.1 Pix4D Image Properties Window)

(Figure.2 Camera Properties Window)

After confirming all of the above information we next had to ensure that the output coordinate system was correct. In this case the WGS94 geographic coordinate system measured in meters (default) is the correct coordinate system. If there were other aspects of data we were implementing this would need to be changed to match that coordinate system. For example, changing the coordinate system to match that of the GCP data you are implementing. Once the map template is selected, in this case we chose 3D maps, this screen appeared. (Figure.3)

(Figure.3 Map View)

This view shows us the map that we are looking at. The points show the location that each image was taken during flight. From here we are going to process the data in order to look at a point cloud. The processing was fairly quick due to there not being that many images. However, I did run into some complications when trying to process the images for the Mavic 2 Pro. This set of data took an immense amount of time for having only 81 images. This could be due to a number of reasons like hardware or the fact that I was remoted in to the computers in the lab from my personal computer. This shows how long processing can take from larger projects and it is important to understand. If I were processing a larger set of data that also had GCP data implemented, it would be a good idea to only do initial processing first to ensure that all of the data is correct before moving on. At the end of initial processing a quality report will be presented that the user can use to verify the accuracy of the work so far.

Below are the results of the processing for each of the 4 data sets. 

Mavic 2 Pro

(Figure.4 Mavic 2 Pro Point Cloud)

(Figure.5 Mavic 2 Pro Point Cloud)

(Figure.6 Mavic 2 Pro Triangle Mesh)

(Figure.7 Mavic 2 Pro Triangle Mesh)

XT2 RGB

(Figure.8 XT2 RGB Point Cloud

(Figure.9 XT2 RGB Triangle Mesh)

(Figure.10 XT2 RGB Triangle Mesh)

XT2 Thermal

(Figure.11 XT2 Thermal)

(Figure.12 XT2 Thermal)

Something to note is that this was the result of the processing. I was previously having issues with the images loading into the application. This could be because of the internet connection remoting in or not having the processing folder within the data folder.

A6000

For the A6000 data set, none of the images were geotagged so for this part we had to import our own geolocation information for the images. Similar to before, we uploaded the images into the software but instead of moving on we selected 'From File' in the geolocation details. From here we imported an excel file that had all of the coordinates for each image. (Figure.13)

(Figure.13 Importing geolocation details)

 Now that all of the images are geotagged we could move on to processing 

(Figure.14 A6000)

(Figure.15 A6000)

Discussion:

Pix4D allows us to import images and look at them in a whole new light. This software takes 2D images and allows the user to look at them in even more dimensions than before. In this lab, we focused on importing and processing as well as ensuring geolocation and camera details were correct. In the future we will work with more complex sets of data but for now this was a good introduction into the software and what it's basic capabilities are.