Patched Line Segment Learning for Vector Road Mapping

• URL: http://arxiv.org/abs/2309.02923v1
• Date: Wed, 6 Sep 2023 11:33:25 GMT
• Title: Patched Line Segment Learning for Vector Road Mapping
• Authors: Jiakun Xu, Bowen Xu, Gui-Song Xia, Liang Dong, Nan Xue
• Abstract summary: We build upon a well-defined Patched Line Segment representation for road graphs that holds geometric significance. Our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs.
• Score: 34.16241268436923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours.

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