# DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories

arXiv: https://arxiv.org/abs/2002.06832 · HTML: https://mlsanity.com/paper/2002.06832/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction](https://mlsanity.com/paper/2111.15119/) (2021)
- [A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification](https://mlsanity.com/paper/2106.07879/) (2021)
- [Road Extraction with Satellite Images and Partial Road Maps](https://mlsanity.com/paper/2303.12394/) (2023)
- [Translating Images to Road Network: A Sequence-to-Sequence Perspective](https://mlsanity.com/paper/2402.08207/) (2024)
- [MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation](https://mlsanity.com/paper/2302.09411/) (2023)
- [AI Powered Road Network Prediction with Multi-Modal Data](https://mlsanity.com/paper/2312.17040/) (2023)
- [Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization](https://mlsanity.com/paper/2503.23370/) (2025)

## Abstract (arXiv)

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.