Jan.01
Geospatial Adventures – Making your own ‘Google Maps’
Over the past year, amongst other things, I’ve been working on geospatial analysis and mapping projects with my current employer. I’ve found that I quite enjoy working on this kind of project, having been fascinated with Google Earth and satellite imagery for the past decade. Combining this interest up with my love for all things flying has resulted in another avenue of interest, this time more altruistic than anything else, I’ve decided to develop a new community based website where users can combine self-generated 3D digital elevation models (DEM) or 2D Orthomosaic imagery with publicly available satellite imagery, with the aim to hopefully generate a high accuracy local map made by users for public and research applications.
Currently I’ve collected a number of systems for collecting the data, starting with a small 450 Quadrotor, a heavy lift 1200mm Hex with a 17kg payload capacity, a DJI Mavic Pro with CrystalSky Monitor, rugged case kit and DJI Goggles. In regards to fixed wing systems the kit also includes a 2500mm MQ9 RC Model, an Sbach S300 fast mover and a bog-standard Bixler2 for longer range and large area missions.
Recently I’ve also designed and built a large quadrotor platform based on a TBS Source X design with a number of modifications, fitting the airframe with a CubePilot Orange flight controller, motors from MAD Motors and APD 120F3 ESCs from Advanced Power drives. This one has a MTOW of around 35kg and can stay airborne for around 38 minutes using 2 Tattu 14,000MAh 6S batteries in series. One serious machine that theoretically should have a Max speed in excess of 200km/h (difficult to test without breaking a few CASA regs).
Processing the data is one of the biggest bottlenecks in the entire process, where the software requires vast amounts of RAM, CUDA cores and a decent CPU. While it can be done using a home PC and a decent gaming graphics card; such as an Nvidia 2080 Ti and a least 32gb of DDR4 RAM, any sufficiently large data-set will take quite a number of hours to days to process at mid to high level quality settings.
I’ve used a number of processing pipelines from Agisoft Metashape/Photoscan to WebODM, for ease of use and easy setup I recommend at least testing out WebODM as firstly it is free AND secondly it can be deployed in either a cluster or single node system. You will produce more tunable results with Agisoft’s package but it will take a bit of training to get the best out of it.
More to come soon…
About Luke Nyhof
Previously employed in the Telecommunications industry as a Technical Manager supporting innovative geospatial data exploration and visualisation tools enabling strategic network planning. His role at the company as Integration Data Interlock Engineer gave light to his work on predictive maintenance systems, machine learning and network operations & geospatial analytics.
Luke's post-graduate career path began at the Centre for Intelligent Systems Research (CISR, now named The Institute for Intelligent Systems Research & Innovation, IISRI), where he was an active and published academic who's engineering research projects included robust adaptive signal processing for biomedical applications, robotic systems, autonomous ground vehicles and advanced robotic motion simulators.
Before joining the Centre he completed his bachelor's degree in engineering, majoring in Mechatronics and Robotics graduating with 1st Class Honours. During this time he was also employed by Robert Bosch Australia. Based at the Australian Automotive Research Complex in Anglesea, the role gave him the opportunity to work as part of a highly competent team of engineers developing chassis control systems for development vehicles. He truly passionate about his work and always eager to connect with other researchers for collaboration.
While Luke have been involved in both collaborative and independent work, his favorite part of project work has been in developing unique solutions for intriguing problems.