Papers › Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery

Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery

4 May 2019CVPR 2019 6arXiv:1905.01447archive 2025-07-28

Tao Sun, Zonglin Di, Pengyu Che, Chun Liu, Yin Wang

Deep learning is revolutionizing the mapping industry. Under lightweight human curation, computer has generated almost half of the roads in Thailand on OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are displaying 125 million computer-generated building polygons in the U.S. While tremendously more efficient than manual mapping, one cannot map out everything from the air. Especially for roads, a small prediction gap by image occlusion renders the entire road useless for routing. Misconnections can be more dangerous. Therefore computer-based mapping often requires local verifications, which is still labor intensive. In this paper, we propose to leverage crowdsourced GPS data to improve and support road extraction from aerial imagery. Through novel data augmentation, GPS rendering, and 1D transpose convolution techniques, we show almost 5% improvements over previous competition winning models, and much better robustness when predicting new areas without any new training data or domain adaptation.

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Tasks

Data AugmentationDomain AdaptationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation BJRoad Sun et al. IoU 59.18 #6 of 11 Archive leaderboard report
Semantic Segmentation Porto Sun et al. IoU 71.79 #5 of 6 Archive leaderboard report

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Methods

Convolution

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