'Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction',
Lingbo Liu, Zewei Yang, Guanbin Li, Kuo Wang, Tianshui Chen and Liang Lin,
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.
In this work, we focus on a challenging task of land remote sensing analysis, i.e., automatic extraction of traffic roads
from remote sensing data. Nevertheless, conventional methods either only utilized the limited information of
aerial images, or simply fused multimodal information (e.g., vehicle trajectories), thus cannot well recognize unconstrained roads.
To facilitate this problem, we introduce a novel neural network framework termed Cross-Modal Message Propagation Network,
which fully benefits the complementary different modal data (i.e., aerial images and crowdsourced trajectories). Extensive experiments
on three real-world benchmarks demonstrate the effectiveness of our method for robust road extraction benefiting from
blending different modal data, either using image and trajectory data or image and Lidar data.
We propose a novel Cross-Modal Message Propagation Network (CMMPNet) for multimodal road extraction.
Specifically, our CMMPNet is composed of (i) two deep AutoEncoders that take an aerial image and a trajectory
heat-map respectively to learn modality-specific features, and (ii) a Dual Enhancement Module (DEM) that
dynamically propagates the non-local messages (NLM, i.e, local one and global one) of every modality with gated functions to
enhance the representation of another modality. The final features of the image and trajectory heat-map are concatenated
to generate a traffic road map.
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- Deepdualmapper: A gated fusion network for automatic map extraction using aerial images and trajectories, AAAI 2020
- D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction, CVPR Workshop 2018