Accurate Polygonal Mapping of Buildings in Satellite Imagery
This work addresses the challenge of accurately mapping building polygons from satellite imagery, which is incremental as it builds on existing deep learning methods to improve reversibility.
The paper tackles the problem of polygonal mapping of buildings in satellite imagery by addressing mask reversibility issues, resulting in improved performance on benchmarks like AICrowd and Inria with metrics such as AP, APboundary, PoLiS, IoU, and Accuracy.
This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.