Papers › SkyScapes Fine-Grained Semantic Understanding of Aerial Scenes

SkyScapes Fine-Grained Semantic Understanding of Aerial Scenes

1 Oct 2019ICCV 2019 10archive 2025-07-28

Seyed Majid Azimi, Corentin Henry, Lars Sommer, Arne Schumann, Eleonora Vig

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.

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Tasks

Autonomous DrivingEdge DetectionManagementSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation SkyScapes-Dense SkyScapesNet-Dense Mean IoU 40.13 #1 of 7 Archive leaderboard report
Semantic Segmentation SkyScapes-Lane SkyScapesNet-Lane Mean IoU 50.93 #1 of 2 Archive leaderboard report

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