Title:
Improving Real-world Aerial Scene Understanding With a Synthetic Dataset
Improving Real-world Aerial Scene Understanding With a Synthetic Dataset
Author(s)
Khose, Sahil Santosh
Advisor(s)
Hoffman, Judy
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Abstract
Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges in obtaining such images in controlled real-world settings, this thesis introduces SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives. We carefully curate SkyScenes images from CARLA to comprehensively capture diversity across layouts (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations.
Through experiments using SkyScenes, this thesis demonstrates that (1) models trained on SkyScenes generalize well to different real-world scenarios, (2) augmenting training on real images with SkyScenes data can improve real-world performance, (3) controlled variations in SkyScenes can offer insights into how models respond to changes in viewpoint conditions, and (4) incorporating additional sensor modalities (depth) can improve aerial scene understanding.
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Date Issued
2024-04-29
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Thesis