SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes
This addresses the need for high-precision aerial scene analysis for applications like autonomous driving and urban management, but is incremental as it builds on existing segmentation methods with a new dataset and model.
The authors tackled the lack of fine-grained aerial image datasets for urban understanding by introducing SkyScapes, a dataset with 31 semantic categories including detailed lane markings, and proposed a multi-task model that improved over baselines in segmentation and lane-marking prediction tasks.
Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling. SkyScapes provides annotations for 31 semantic categories ranging from large structures, such as buildings, roads and vegetation, to fine details, such as 12 (sub-)categories of lane markings. We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction. We carry out extensive experiments to evaluate state-of-the-art segmentation methods on SkyScapes. Existing methods struggle to deal with the wide range of classes, object sizes, scales, and fine details present. We therefore propose a novel multi-task model, which incorporates semantic edge detection and is better tuned for feature extraction from a wide range of scales. This model achieves notable improvements over the baselines in region outlines and level of detail on both tasks.