Papers › DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial...
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
Hao Wu, Hanyuan Zhang, Xin-Yu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang
Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information flows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised refinement decoder to generate the prediction in a coarse-to-fine way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | BJRoad | DeepDualMapper | IoU | 60.19 | #5 of 11 | Archive leaderboard | report |
| Semantic Segmentation | TLCGIS | DeepDualMapper | IoU | 81.10 | #5 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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