# Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery Source: [Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:56:51.523Z Authors: Ye Lyu, George Vosselman, Gui-Song Xia, Michael Ying Yang Published: 2021-02-05T11:02:15.000Z ## Abstract Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation. Most of the semantic segmentation research focused on scenes captured in nadir view, in which objects have relatively smaller scale variation compared with scenes captured in oblique view. The huge scale variation of objects in oblique images limits the performance of deep neural networks (DNN) that process images in a single scale fashion. In order to tackle the scale variation issue, in this paper, we propose the novel bidirectional multi-scale attention networks, which fuse features from multiple scales bidirectionally for more adaptive and effective feature extraction. The experiments are conducted on the UAVid2020 dataset and have shown the effectiveness of our method. Our model achieved the state-of-the-art (SOTA) result with a mean intersection over union (mIoU) score of 70.80%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()