Optimization of ground control point configuration for unmanned aerial vehicle survey using structure-from-motion and geographic information systems - Villanueva, Jara Kaye S.
Unmanned Aerial Vehicles (UAV) or drones have recently been a commonplace in surveying and 3D mapping – slowly making a name outside of defense-focused applications where these were initially developed and used for. With its increasing affordability, ease of deployment, and low cost operation, the use of this technology has been advanced further when applied with Structure-from-Motion technique. The integration of a low-cost but high technology photogrammetry with advanced computer vision algorithm, such as UAV-SfM, provides a great potential in data acquisition capability which can be comparable to other expensive close-range photogrammetry techniques. This has predictably led to the massive expansion of applications using these tools: environmental monitoring (glacier, landslide, dam, slope, and erosion), natural landform mapping, disaster assessment and management (earthquake, flood, forest fire, and tsunami), urban mapping and planning, crop monitoring, and construction and mine management. Along with the growth of users and functions comes the need to look into the accuracy and reliability of the data derived from UAV-SfM. This research explored on how accurate data and measurements can be achieved using UAV and SfM, along with GIS tools. Primarily, the focus is on how the configuration of the Ground Control Points (GCP), i.e., their distribution, quantity, and inter-distances, impact the values of the Digital Elevation Model (DEM) derived from the SfM platform with the help of GIS tools to validate the results. With a quarry site as the chosen area of application, volume and cross-sectional measurements are done on a stockpile to understand more thoroughly how different GCP configurations can impact the information derived from the data. There are three error propagation tests conducted in the SfM platform to assess the distribution, quantity, and inter-distances of the GCPs. Four major configurations classes are initially defined for the first error propagation distribution test: A) Clumped –center, B) Distributed – edge, C) Clumped – edge, and D) Distributed – edge and center. These are all processed with increasing GCP quantity. The second quantity test further analyzed the best configuration class derived from the distribution test. While the third and last error propagation test utilized Leave-one-out (LOO) method to evaluate the interdistances of GCPs. Using GCP Root Mean Square Error and surface checkpoint error as the accuracy criteria, results showed that the best configuration is the evenly distributed GCP set with a 1.58 m average RMSE and 1.30 m average surface checkpoint error. Configurations C) Clumped – edge and B) Distributed – edge follow suit with average GCP RMSE of 2.53 m and 3.11 m while producing average surface checkpoint error of 2.13 m and 2.54 m. The A) Clumped – center configuration, with a 6.23 m and 4.66 m RMSE and surface checkpoint error values. As the GCPs used increase, the accuracy criteria errors are observed to decrease consistently for all configurations. Further iteration of the best configuration showed that from GCP RMSE of 4.11 m when 4 GCPs are used, there is a drastic decrease of 3.24 m once 10 GCPs are used. From that quantity, only centimetre difference can be observed until the full set of 24 GCPs have been used with a 0.012 m error. This is reflected in the stockpile measurement when the iteration results are compared to the reference data. The dataset processed with a minimum of 4 GCPs have a 606,991.43 m3 difference, whereas the dataset processed with 23 out of 24 has a 791.12 m3 difference from the reference data. This confirms the results from the error propagation that the accuracy of the SfM-based DEM increases with the quantity of the GCPs used with an even distribution – this ultimately leads to an increase in the accuracy of the volumetric measurements in the data. Considering drone data analytics for mining solutions, this method using UAVSFM tools can provide a low-cost operation without compromising the accuracy and consistency of the data over time. Through this methodology, a smarter decision making can be made upon pre-planning UAV survey by having a priori knowledge of the best configuration of the GCP in the area. This completely eliminates the tendency to have a re-fly in the area once the acquired dataset is not ideal. Understanding how the GCPs behave and impact the terrain of the area and the information derived from it is essentially providing the solution before it becomes a problem.