Building simplification of vector maps using graph convolutional neural networks

Zhou, Zhiyong; Fang, Cheng; Weibel, Robert

doi:10.5194/ica-abs-5-86-2022

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Graph convolutional network [34] has shown compelling advantages in graph data from many fileds, such as graph classification [35], recommendation system [36], node classification [37] and so on. In addition, GCN has also been introduced into the PolSAR filed.…”

Section: A Graph Convolutional Networkmentioning

confidence: 99%

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Multiscale Superpixel-Guided Weighted Graph Convolutional Network for Polarimetric SAR Image Classification

Wang,

Nie,

Geng

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) has attracted more attentions because of its excellent observation ability, and PolSAR image classification has become one of the significant tasks in remote sensing interpretation. Various types and sizes of land cover objects lead to misclassification, especially in the boundaries of different categories. To solve these issues, a multiscale superpixel-guided weighted graph convolutional network (MSGWGCN) is proposed for classifying PolSAR images. In the proposed MSGWGCN, multiscale superpixel features are imported into the weighted graph convolutional network to obtain higher-level representation, which can make full use of land cover object information in PolSAR images. Moreover, to fuse pixel-level features at different scales, a multiscale feature cascade fusion module is built, which plays an important role in preserving classification details. Experiments on three PolSAR datasets indicate that the proposed MSGWGCN performs better than other advanced methods on PolSAR classification task.

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Section: A Graph Convolutional Networkmentioning

confidence: 99%

“…Several advanced methods are selected to compare with MS-GWGCN model, including SVM [51], 2DCNN [35], 3DCNN [52], AFS-CNN [45], DSNet [53], GCN [38], CEGCN [54] and ViT [24]. The detailed experimental settings of these compared methods are introduced below.…”

Section: B Experimental Details and Comparisonsmentioning

confidence: 99%

Multiscale Superpixel-Guided Weighted Graph Convolutional Network for Polarimetric SAR Image Classification

Wang,

Nie,

Geng

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI

Fu,

Zhou,

Xin

et al. 2024

International Journal of Geographical Information Science

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Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks.

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Keeping walls straight: data model and training set size matter for deep learning in building generalization

Fu,

Zhou,

Feng

et al. 2023

Cartography and Geographic Information Science

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Building simplification of vector maps using graph convolutional neural networks

Cited by 6 publications

References 9 publications

Multiscale Superpixel-Guided Weighted Graph Convolutional Network for Polarimetric SAR Image Classification

Multiscale Superpixel-Guided Weighted Graph Convolutional Network for Polarimetric SAR Image Classification

Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI

Keeping walls straight: data model and training set size matter for deep learning in building generalization

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