MAPPING NEPAL: DRONES AND THE FUTURE OF DISASTER RESPONSE
Introduction In September 2015, Pix4D joined with technology leaders and partners for a week-long UAV training mission in Nepal: teaching engineering students at Kathmandu University how to use drones and image-processing software for a wide range of humanitarian and development purposes. Drones and photogrammetric software for mapping applications are quickly expanding from surveying and geomatics use to other fields, disaster response among them. In the event of a disaster like the earthquake that struck Nepal in spring 2015, maps and models produced from drone-acquired imagery and image-processing software can help assist search and rescue operations, damage assessment, reconstruction, preparedness planning and cultural preservation. Before the training, Kathmandu University (KU) had already been conducting research with drones and was looking to expand its expertise. Humanitarian UAV Network (UAViators) founder Patrick Meier spearheaded the drone-mapping training in collaboration with KUâ€™s Department of Civil and Geomatics Engineering, Kathmandu Living Labs (KLL), DJI, Pix4D and Smartisan, with the intent of building a community of Nepali UAV operators skilled in imagery analysis. Thirty two participants from different organisations (KU, Land Management Training Center, Nepal Survey Department, ICIMOD, Nepal Police, Practical Action Nepal, NSET and KLL) learned drone operation guidelines and regulations from KU staff, the Civil Aviation Authority of Nepal (CAAN) and UAViators. They also trained in drone navigation with DJI, photogrammetric software with Pix4D, and Open Street Map usage with KLL. Participants created specialized flight plans for image acquisition with Pix4Dmapper Capture app, then used photogrammetry software Pix4Dmapper to turn that data into 3D models and maps for further analysis.
In an emergency response scenario, such maps and models provide critical information for disaster relief. Satellite imagery has been used in these situations for decades, but not without shortcomings: Availability, spatial resolution and restrictive vertical perspective limit its usability. Drones, on the other hand, are low-cost and can be combined with image-processing software for frequent surveys of rapidly changing areas, which are crucial in a disaster. Drone imagery offers a reliable oblique perspective without cloud coverage concerns. These advantages and the high resolution outputs generated by software like Pix4Dmapper have placed UAVs in the spotlight of the disaster response community.
During operational training in the field, participants worked alongside DJI, Pix4D, and the Community Disaster Management Committee (CDMC) of Panga - a village that had been badly damaged in the earthquake - to create a complete map of the area. Using Phantom 3 Advanced quadcopters and Pix4Dmapper, orthomosaics were produced overnight for the community to use in the reconstruction process and disaster preparedness. While this training had a humanitarian base, the goal was to demonstrate the potential drones and image analysis have for positive impact in disaster situations. Participants, apart from just gaining firsthand experience in the field, manifested this potential through their motivation for practical application and knowledge. Their collaboration and innovation will continue to see the applications of drone imagery and mapping software in disaster response and more.
DJI Phantom 3 Advanced
DJI Remote Controller
Pix4Dmapper Capture App
Location/Safety When we arrived on site, we coordinated with the local Community Disaster Management Committee (CDMC) to see which areas needed to be mapped the most. Permission to fly had already been obtained from the Civil Aviation Authority (CAAN).
Because the streets were not clear: with debris, powerlines and people around, we climbed on the rooftop of the highest surrounding building to ensure safe flying as well as to keep the drone in our line of sight.
Data Acquisition We opened the Pix4Dmapper Capture app and chose a grid mission, which is optimal for most mapping, then used GPS to localize ourselves on a low resolution satellite map. Dragging the flight grid selection tool, which can scale to approximately 300x400 meters, depending on height, we defined the area we wanted to map. Flight altitude, speed, image overlap percentage, and angle of the camera can all be adjusted.
Turn on the drone, connect the phone to the remote control using the cable provided, then tap on the â€œstart missionâ€? button. The Phantom takes off automatically, acquiring images with a high overlap for a proper reconstruction in Pix4Dmapper. During the flight it is very important to keep eye contact with the drone at all times, so it can be quickly brought back in case of an emergency. Point the controller in the direction of the drone at all times to ensure a reliable connection between the drone and the controller, without any interfering walls or objects, so that images can be correctly triggered.
We took a total of 9 small flights, with a total flight time of 45 minutes, acquiring around 900 images at 3.4 cm resolution.
Although a Phantom has up to 20 minutes of flight time, our flights on the field were short, because high levels of airwave interference shortened the distance the drone and controller could be apart without risking a loss of connection for image triggering. Without this interference, the area covered could be much larger and by fewer flights, although for this project, most of the time expended was from climbing to the top of each building. Depending on the size of a project, different types of fixed wing or copter drones can provide different efficiencies.
The Phantom automatically came back to its starting point after the last image was taken for each flight. Although the GPS-based automatic landing feature will put the drone within 2 meters of its starting point, we always switched back to manual mode for landing as the rooftops were usually very narrow.
As soon as the drone landed, we wanted to make sure that all pictures were taken properly, while we were still on site. To do so, we uploaded the images to the Capture Cloud service that computes a 2D and 3D preview within minutes after the images automatically upload on the phone itself, and viewed the orthomosaic on our phoneâ€™s browser.
For processing, we transferred the images to the desktop. Either take the SD card from the drone to transfer, or transfer the pictures (used for the 3D preview) from your phone.
Processing Using Pix4Dmapper on the laptop, we selected the 3D Maps template with default (WGS84) coordinate system. The softwareâ€™s automatic processing comprises 3 main steps: The first step optimizes camera positions and analyzes image information, extracting keypoints and matching them across the images. The second step builds a 3D point cloud and model, and the third step generates the DSM and orthomosaic.
All of the images from different flights were processed together in one project, taking around 70 minutes on a MSI laptop with an i7 quad core and GTX 970M GPU. For projects where this doesnâ€™t work (flights may have very different resolutions for example), flights can be processed separately and merged together by creating manual tie points between the images.
Final Results We obtained a 3D point cloud, 3D model, and 2D map (orthomosaic) of the village of Panga. Instead of vertical or close to nadir imagery with meter resolution as we might get with satellite maps, we have data that is at centimeter resolution and provides a more comprehensive perspective with its oblique viewing angle. The built in tools of Pix4Dmapper, like the raycloud and mosaic editor, give us access to the accuracy and quality of projects.
This project was possible thanks to:
Special thanks to photographer and friend Kike Calvo
Mapping Nepal: Drones and the Future of Disaster Response