Abstract. Drone technology has shown the potential to act as the middle ground between satellite, light aircraft, and terrestrial or in-situ methods. However, featureless terrain such as water poses a challenge when it comes to drone mapping. The main challenge is identifying matching points to combine overlapping images into a single dataset. In particular, because traditional methods such as Structure from Motion (SfM) is dependent on tie point collection, its usage over featureless terrain is almost impossible. In solving this problem, we propose that the use of Direct Georeferencing (DG) in registering images be explored as a potential method and we propose a method for correcting errors due to tilt with low-cost IMUs. This study first assesses the accuracy of direct georeferencing using low-cost Inertial Measurement Units (IMU) and Global Navigational Satellite System (GNSS) providing analysis of the error sources associated with direct georeferencing and then demonstrates new approaches to minimize them. To best simulate a water type environment or surface for the initial studies, a drone survey was conducted on flat farmland and a POSE analysis was performed. We then processed the images using direct georeferencing and then compared our error minimisation method to standard Bundle Block Adjustment with GCPs and again with no GCPs. Results showed that using the method proposed in this study helped reduce the Mean Absolute Error associated with direct georeferencing by 54%. These initial results show a clear potential for mapping over inland water using direct georeferencing.

Abstract. Drones offer a a unique survey platform that can operate below cloud cover and acquire very high spatial resolution datasets in near real-time. Studies have demonstrated that drones can be used for mapping over water using the Direct Georeferencing approach. However, this method is typically only feasible with high-end drones equipped with highly accurate GNSS/IMU systems. Moreover, placing targets over water to improve accuracy in post-processing can be challenging, further exacerbating this limitation. In this study, we developed an Assisted Direct Georeferencing method which combines the advantages of traditional Bundle Adjustment (BA) and Direct Georeferencing to overcome these challenges. Our approach utilizes BA over feature-rich segments of the drone trajectory, such as the shoreline, and DG in featureless areas, such as over water. To simulate a water-type environment or surface for our early tests, synthetic datasets have been created using Python for theoretical analysis. We then conducted a theoretical assessment of our approach under low and high variability attitude measurements. Our findings revealed that our methodology performs well under low variability attitude measurements, where wind conditions are close to optimal with an R-square value of 0.93. However, our model performs poorly under high variability attitude measurements, with an R-square value of only 0.028. These results suggest that Assisted Direct Georeferencing can serve as an alternative to high-end drones and Direct Georeferencing for water mapping applications in most standard. The findings from this theoretical assessment provide valuable insights into the achievable accuracy, error budgets, and limitations of the proposed model.