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- W2990367002 abstract "The availability of large amount of high-resolution aerial images, together with the recent advancement of deep convolutional neural networks (DCNNs) for extracting rich-and-hierarchical features from unstructured data, has propelled the automation progress of extracting roads from aerial images. Despite the superior performance of DCNNs, a common problem of choosing between the classification and segmentation DCNNs still remains. By comparing two state-of-the-art baseline classification/segmentation DCNNs in several industrial application scenarios, we illustrate that their relative performance may vary, leading to different choices. We also propose a strategy of fusing multiple pre-trained DCNNs and empirically discover that it guarantees superior results in all of the experimented scenarios, using far less development time. A few tools and pre-trained models (https://github.com/caolele/road-discovery) are open-sourced to facilitate research and engineering activities." @default.
- W2990367002 created "2019-12-05" @default.
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- W2990367002 date "2019-11-20" @default.
- W2990367002 modified "2023-09-24" @default.
- W2990367002 title "Fusing Classification and Segmentation DCNNs for Road Feature Mining on Aerial Images" @default.
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- W2990367002 doi "https://doi.org/10.1007/978-3-030-31608-2_7" @default.
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