SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes
Seyed Majid Azimi, Corentin Henry, Lars Sommer, Arne Schumann and, Eleonora Vig

TL;DR
SkyScapes is a new aerial image dataset with detailed, pixel-level annotations for urban scenes, enabling advanced semantic segmentation and lane-marking prediction, and highlighting challenges for current methods.
Contribution
We introduce SkyScapes, a comprehensive dataset with fine-grained annotations and propose a multi-task model that improves segmentation accuracy on complex aerial scenes.
Findings
Existing methods struggle with diverse classes and fine details.
The proposed multi-task model outperforms baselines in segmentation accuracy.
SkyScapes enables better understanding of urban aerial imagery.
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Automated Road and Building Extraction
