# Autonomous Environment Generator for UAV-Based Simulation
## 6 Conclusions and Future Work  
In this paper, a UAV testbed that can procedurally generate simulated environments was proposed. This testbed can quickly transform a satellite image of any physical location into a 3D testing environment using image recognition algorithms and neural networks. While our simulation currently lacks certain graphical detail, our product proves the novel concept of an automated and time-efficient way to generate real-life locations. Standard testing protocols, such as flight demos or manual simulations, are now replaced by a much less expensive and more exhaustive process. With this platform, users will be able to perform repeated simulations with their UAV in real-world scenarios with minimized risk and cost. Our model bases every output on publicly available and regularly updated satellite data, eliminating the need for environmental sensors or cameras. Therefore, its application is straightforward and accessible to every user, while maintaining the accuracy and fidelity in the simulation model. Two machine learning algorithms, YOLO v4 and Mask R-CNN, were utilized and tested in isolation before integration with the Gazebo simulator. The use of these two algorithms balance efficiency in identifying distinct objects with the precision required to encapsulate objects of varying shape. The results prove that a testbed framework backed by image processing is possible and efficient for UAV simulations. Further, testing sensitive equipment via UAVs is safe and allows for efficient, synchronous control. Though a reputable final product was achieved, there is much more to be done in creating a robust testbed for all users and use cases. In the future, this work will be enhanced with the following additions:  
* _Increased classes and model accuracy._ Road segmentations occasionally bleed past their boundaries or get split by excessive shading or foliage. YOLO v4 bounding boxes are not as tight as they can be on certain buildings or trees. A more expansive training set can help mitigate these shortcomings over future iterations [36]. Our image processing algorithms additionally identify a limited set of objects, namely those necessary for testing. More classes and models will be added to vary the situations users may face on a case by case basis. * _Decrease load time._ Loading an average-sized neighborhood or a couple urban blocks is on the order of a couple of minutes due to running our machine learning algorithms  
Figure 18: Validation process: (**a**) an original satellite image, unmarked for comparison in validation process; (**b**) the same satellite image, but, this time, real-life buildings are annotated and outlined; and (**c**) the same satellite image, but Gazebo-placed models are outlined instead. one after the other. Multiprocessing can be implemented to effectively half the loading time for any given world. * _Reload saved image._ While our satellite data is being saved to our backend database, future work would entail an efficient way to recreate previously generated locations. In this fashion, the ability to combine several past locations together into one larger simulation is also an idea to implement in the future. Conceptualization, J.N., R.P., and J.P.M.-C.; Funding acquisition, L.M.C. ; Investigation, J.N., R.P., J.P.M.-C., F.A., and D.P. ; Resources, L.M.C. ; Software, J.N., R.P., J.P.M.-C., F.A., and D.P. ; Supervision, L.C. ; Validation, J.N., R.P., and J.P.M.-C.; Writing--original draft, J.N., R.P., and J.P.M.-C.; Writing--review & editing, J.N., R.P., F.A., D.P., and L.C. All authors have read and agreed to the published version of the manuscript. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783119. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden.