Lab 6: Bad Elf Pro +

Introduction

The purpose of this lab was to use a Bad Elf GPS Pro + and iPhone to track a route taken by the group around campus. This introduced the Bad Elf GPS technology and provided a data set to practice importing KML and GPX files into relevant software such as ArcMap and ArcGIS Earth. Bad Elf provides accurate and reliable location services for collecting data with or without cellular data. The GPS unit is connected to an iPhone via Bluetooth which utilizes its superior computing power. This allows Bad Elf and other GPS companies to focus on developing better GPS units instead of GPS and the corresponding computational component. iPhones also have the ability to access data online which results in faster data updates. This trend has emerged in recent years because companies that make GPS units take 4-5 years to bring an idea into production and by that point the technology is dated. By using phones which are updated by big companies such as Apple and Google, GPS companies can focus on creating better GPS software.

Bad Elf Compatible Apps

Bad Elf also has useful apps that can be downloaded from the App store on iPhones. These apps are separated into the following categories: agriculture, aviation, fitness and sport, GIS, marine, motor sports, recreation, travel, UAS, and vehicle navigation. Five examples from several of the categories include Pocket Earth PRO, Runtastic Running and Fitness, FieldX Journal, TerraGo Edge, and CamerAlert. These apps were chosen based on personal interests and career applications.

Pocket Earth PRO is filled with detailed maps and travel guides that cover the whole world. They can be downloaded to work offline which eliminates data roaming charges. What’s great about this app is the variety of tools available including topography maps, street navigation, and search options. With such a wide variety, this app can be used by a traveler for all their needs whether it’s in the city or off in the wilderness. This app would also be great because a traveler can change plans last minute and do something ‘off the beaten path’ and still have an app that gives them the maps to complete their adventure.

Runtastic Running and Fitness is an app that tracks running routes completed by the user. I personally use this app and it works great! Some of the features include real time tracking, voice coaching, group sharing, and goal setting options. With a Bad Elf GPS routes can be tracked in more remote areas that normally would not have accessible cellular data. More accurate GPS tracking also creates a more detailed route map and more accurate mile times which can become important in race training. This app can be used by a wide range of users from first-time runners to seasoned pros.

FieldX Journal is an app that I picked based on my internship experience with the Natural Resources Conservation Service (NRCS). This app can be used to record agricultural field data with or without an internet connection, view field history, and navigate to georeferenced field borders. Data about crop health, harvest times, and field observations can all be stored in journal entries that can then be synced to the FieldX cloud. I see huge potential for this app in NRCS. Currently paper maps are printed and field observations are recorded with a pen or marker. The map is then given to a GIS technician that will create the features drawn on paper in a map document. This app would allow the field technician to digitally record observations that can then be imported into ArcGIS or a similar platform.

TerraGo Edge allows users to collect GPS data and complete various forms, surveys, and field service records attached to its GPS point. Maps can be created offline that show where forms were completed and real-time locator features are also included. Again, an agency like NRCS that has multiple field technicians working on the same field but on separate sides could both record observations in an online form and share it real-time through the cloud and attach that form to the specific GPS points of the observation. This could increase the accuracy of necessary observations such as crop cover, fertilizer cover, irrigation range, and water pump locations.

Finally, CamerAlert is more of a fun app that alerts drivers of speed and red light cameras in audio and visual format. In addition to showing a map of speed cameras, the system will filter out which cameras a driver is alerted to based on the projected route. The reviews do say it is a bit pricey, but I guess one should be going the speed limit anyways.

Overall Bad Elf is a great GPS tool that can grow with the constantly changing technology market and provide more accurate GPS data for a multitude of other apps.

Methods

A route taken on the University of Wisconsin-Eau Claire campus was tracked using a Bad Elf Pro + GPS unit. First, the Bad Elf unit was connected via Bluetooth to an iPhone. Special care was taken to connect to the correct unit by using the GPS unit’s identification number. Then the Bad Elf app was downloaded from the App store and used to confirm that the Bad Elf unit was indeed connected to the phone. The app itself has quite a few sections to it. At the top is the current speed of the user and the elevation. Underneath is where the currently connected Bad Elf unit would show up. There are also tabs to access trips downloaded from the Bad Elf to the phone via Bluetooth, a current location map, a troubleshooting guide, and settings (figure 1).

Figure 1. Bad Elf app layout.

On the Bad Elf Pro + the tracklog was turned on to begin tracking the group’s movements. The unit then recorded the entirety of the trip taken around campus. The activity was essentially a game of hide-and-seek using a tracking transmission device. One group hide the receiver and another group used the tracking device to locate said receiver. This was done several times. The tracking device and receiver are pictured in figure 2.

Figure 2a. Receiver used in the tracking activity.

 2b. Tracking device held parallel to the ground used to locate the receiver.

The tracker will pick up the signal from the receiver and point in the direction it is located. The user will then follow the arrow until the receiver is found. The tracker must also be held parallel to the user at chest level as pictured in figure 2b. During each seeking session, the Bad Elf app updated the location map and values recorded by the Bad Elf Pro + unit.
Once the tracking activity was completed the Bad Elf app was opened and the tracklog from the GPS unit was downloaded. Within the app the tracklog was shared via email as a GPX and KML file. The tracklog was then imported into ArcMap as a layer and then exported as a feature class. The resulting map is shown in figure 3.

Figure 3. Map of the tracklog recorded by the Bad Elf Pro + GPS unit. 

Discussion/Conclusion

The Bad Elf Pro + is a versatile GPS unit that can be applied to a variety of uses through apps on the iPhone. This lab introduced the unit and how it records and shares data. This simple task can be integrated into a variety of other apps such as FieldX Journal, Pocket Earth PRO, and TerraGO Edge. One real world application is walkability studies in urban environments. The Bad Elf unit is small and lightweight so it can be given to individuals to carry throughout the day. The data from the unit can then be synced to the participant or researcher’s phone and subsequently exported as a GPX or KML file. The data obtained from the GPS unit can be used to determine which areas of a city are walkable and which are avoided. This information would be of great interest to a city planner who is trying to revitalize a downtown area. Another real world application is field surveying. As mentioned earlier, field technicians for NRCS must often go to farm fields and survey their current status. Bad Elf and FieldX Journal could be used to record both observations and where they were obtained. This would be in a digital format that can be sent to a GIS technician who can then directly import it to ArcMap for easy integration into current geodatabases. This would greatly expedite the current process which includes printing out paper maps and giving the drawings to a GIS technician who must then create the features by hand. Overall Bad Elf has a wide variety of applications for both personal and professional use. 

Sources

 "Bad Elf Compatible Apps." (2018). Accessed March 6, 2018. https://bad-elf.com/pages/compatible-apps#agriculture.

Lab 5: Processing Data in Pix4D

Introduction

This lab builds off of the work done last week in Lab 4. Last week’s lab was an overview of Pix4D and the basic abilities of the software. This week real-world data will be processed including Ground Control Points (GCPs). GCPs are points of known coordinates in the area of interest. These are measured using survey methods on the ground such as a Trimble GPS unit. These points can be used to place a model in Pix4D in the correct location, scale it, and orient it. The goal of this lab is to perform basic processing on a data set including mosaicking, GCP calibration, and the creation of a shapefile that can be imported to ArcGIS. 

Study Area

The imagery provided in this lab is a fly-over of a garden on the south side of Eau Claire shown in figure 1.  The garden is associated with South Middle School.

Figure 1. Community Garden at South Middle School in Eau Claire, WI where UAS data was collected.

Methods

First the Pix4D software was opened and a new project was started. Then within the data folder provided by Dr. Hupy 69 images captured by UAS were added. Before going any further the camera setting had to be changed from Global Shutter to Linear Rolling Shutter. This setting describes how the imagery was captured by the camera on the UAS. Then the coordinate system was confirmed as WGS UTM zone 15N. The 3D map option was chosen.

 Some further options were explored within the project before starting the initial processing. On the bottom bar “point cloud and mesh” and “DSM/orthomosoaic and Index” were unchecked to only run initial processing (figure 2). 

 

Figure 2. Bottom bar of Pix4D. For the first processing step number 2 and 3 were unchecked and number 1 was checked (initial processing).

Within the processing options tab under DSM/orthomosaic and Index “google maps and KML” was selected so the final product could be shared to other audiences without access to ArcGIS software. The shapefile option was also selected to create a shapefile of the contours with 1 meter elevation intervals, a resolution of 50 cm, and a minimum line size of 100. The resulting images can then be imported to ArcGIS for mapping purposes. The next step was to import GCPs. In the project tab on the top of the page the GCP and MTP manager was opened and GCPs were imported in the Y,X,Z format. Finally, initial processing commenced. Initial processing goes through the images and matches them together while determining if there are poor images in the group. For this reason when fly-overs are conducted there must be at least a 75% overlap in imagery. A quality report is created after initial processing (figure 3). 

 

Figure 3. A summary from the first quality report. One error notification showed up in camera parameters.

This should be reviewed to make sure the data is ready for the next processing step. There was a notification alert, but this was corrected using the GCPs in the following step. It is worth noting that the RMS error was much smaller with the GCPs than without, which is what makes this data acceptable for use (figure 4)..

Figure 4. The top table shows the RMS error for the geolocation variance without GCPs. The bottom table shows the RMS error with GCPs.

 Next the GCP and MTP Manager was selected again and the raycloud editor was opened. In raycloud editor the user can check GCPs to make sure they are as accurate as possible.

 

Figure 5. GCP locations (shown as blue circles on the ground image) and the corresponding images above denoted by the green lines.

 For each GCP 3-5 images had the GCP location calibrated to the real-world object it was based on. This calibrates the GCP to within centimeters. 

 

Figure 6. Calibration of GCP locations. The blue circle in the northwest corner is the original location set by Pix4D. The yellow circle over the pink object is the actual GCP location.

An important step here was to use the reoptimize option in the processing tab to adjust the images after GCP calibration. At this point another quality report could be generated, but this is up to each individual user. For the purposes of this lab another quality report was not generated. On the same bottom tab mentioned in figure 2 initial processing was unchecked and the other two options were selected. Then the next two processing steps were completed. Finally, using data created from Pix4D a map was created in ArcGIS showing the DSM and mosaic tif image. 

Discussion

 Figure 7 shows the map data that was created using Pix4D.

Figure 7. Final Map of UAS imagery of a garden at the Priory in Eau Claire, WI. The top map is a mosaic of the images taken by the UAS with a locator map of the Priory. The bottom map is a DSM created with the UAS data over a hillshade raster. The bottom map also includes a locator map of Eau Claire in the state of Wisconsin.

The mosaicked image is complete with no gaps and has accurate sub-meter resolution. The DSM of the area is relatively simple because the trees are the only elevated surface on the map. However  there is a slight increase in elevation in the eastern portion of the image. This could have implications for water runoff subsequent effects on garden crops.

Overal Pix4D software is useful for processing UAS data and converting it into a format usable by mapping software such as ArcGIS. GCPs corrected the images to create a more accurate final mosaicked image. Processing times varied, but for such a small data set the time needed for processing was relatively short. The layout would have been confusing if Dr. Hupy had not demonstrated where each task button was located. The quality report was long and confusing in certain sections. I would have liked an abbreviated version that still retained the pertinent information. However, the final products of the software are aesthetically pleasing and easy to use in ArcGIS. There are also some impressive tools within Pix4D, such as GCP calibration, fly-over, and volume measurement. The powerful uses of Pix4D are apparent even with such limited interactions.

Conclusion

This lab introduced the necessary tasks to take UAS data and transform it into a usable format for mapping and other related uses. The data set in this example is small but still demonstrates the basic concepts in processing UAS data. As UAS become cheaper and easier to use, knowing how to process the data they collect is crucial. While simple in nature this lab provides basic knowledge of UAS processing.

Sources

 Pix4D SA. "Pix4Dmapper 4.1 User Manual." Pix4D, 14 Dec. 2017, https://support.pix4d.com/hc/en-us/articles/204272989-Offline-Getting-Started-and-Manual-pdf-. pdf.