Publication: Keeping walls straight: data model and training set size matter for deep learning in building generalization
Keeping walls straight: data model and training set size matter for deep learning in building generalization
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Fu, C., Zhou, Z., Feng, Y., & Weibel, R. (2024). Keeping walls straight: data model and training set size matter for deep learning in building generalization. Cartography and Geographic Information Science, 51(1), 130–145. https://doi.org/10.1080/15230406.2023.2264757
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Deep learning-backed models have shown their potential of conducting map generalization tasks. However, pioneering studies for raster-based building generalization encountered a common “wabbly-wall effect” that makes the predicted building shapes unrealistic. This effect was identified as a critical challenge in the existing studies. This work proposes a layered data representation model that separately stores a building for generalization and its context buildings in different channels. Incorporating adjustments to training sample ge
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- Fu, Cheng
- Zhou, Zhiyong
- Feng, Yu
- Weibel, Robert
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Fu, C., Zhou, Z., Feng, Y., & Weibel, R. (2024). Keeping walls straight: data model and training set size matter for deep learning in building generalization. Cartography and Geographic Information Science, 51(1), 130–145. https://doi.org/10.1080/15230406.2023.2264757
