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

TL;DR

DeepDualMapper is a neural network that effectively fuses aerial images and GPS trajectories to automatically generate accurate digital maps, improving upon previous methods by explicitly controlling information flow and refining predictions.

Contribution

It introduces a gated fusion module and a densely supervised decoder for better integration of multimodal data in map extraction.

Findings

Outperforms existing methods in map accuracy

Effectively fuses aerial images and trajectories

Generates high-quality digital maps

Abstract

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.