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.

DeepDualMapper 是一个可深度融合高空影像和 GPS 轨迹数据以提取数字地图的卷积神经网络，通过门控融合模块和密集监督精细化解码器控制数据的信息流，可更有效地提取地图。