SkyAI Sim: An Open-Source Simulation of UAV Aerial Imaging from Satellite Data
<|reference_start|>SkyAI Sim: An Open-Source Simulation of UAV Aerial Imaging from Satellite Data: Capturing real-world aerial images for vision-based navigation (VBN) is challenging due to limited availability and conditions that make it nearly impossible to access all desired images from any location. The complexity increases when multiple locations are involved. The state of the art solutions, such as flying a UAV (Unmanned Aerial Vehicle) to take pictures or using existing research databases, have significant limitations. SkyAI Sim offers a compelling alternative by simulating a UAV to capture bird's-eye view satellite images at zero-yaw with real-world visible-band specifications. This open-source tool allows users to specify the bounding box (top-left and bottom-right) coordinates of any region on a map. Without the need to physically fly a drone, the virtual Python UAV performs a raster search to capture satellite images using the Google Maps Static API. Users can define parameters such as flight altitude, aspect ratio and diagonal field of view of the camera, and the overlap between consecutive images. SkyAI Sim's capabilities range from capturing a few low-altitude images for basic applications to generating extensive datasets of entire cities for complex tasks like deep learning. This versatility makes SkyAI a valuable tool for not only VBN, but also other applications including environmental monitoring, construction, and city management. The open-source nature of the tool also allows for extending the raster search to other missions. A dataset of Memphis, TN has been provided along with this simulator, partially generated using SkyAI and, also includes data from a 3D world generation package for comparison.<|reference_end|>
@article{dajkhosh2024skyai,
    title={SkyAI Sim: An Open-Source Simulation of UAV Aerial Imaging from
  Satellite Data},
    author={S. Parisa Dajkhosh, Peter M. Le, Orges Furxhi, Eddie L. Jacobs},
    journal={arXiv preprint arXiv:2410.02003},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02003},
    primaryClass={cs.CV cs.HC eess.IV}
}