TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings

Chen, Qi; Wang, Lei; Wu, Yifan; Wu, Guoxing; Guo, Zhiling; Waslander, Steven L.

doi:10.1016/j.isprsjprs.2018.11.011

Cited by 109 publications

(76 citation statements)

References 66 publications

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“…It aims to classify each pixel with a corresponding class. Various semi-automatic and automatic methods [1] [2] [3] [4] have been developed to improve segmentation accuracy within this method; traditionally, feature extraction and classification are its two main steps. The extraction of such handcrafted features usually require a strong domain-specific knowledge.…”

Section: Introductionmentioning

confidence: 99%

Building segmentation through a gated graph convolutional neural network with deep structured feature embedding

Shi

Zhu

2020

ISPRS Journal of Photogrammetry and Remote Sensing

100

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Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible. Yet one central issue remains: the precise delineation of boundaries. Deep architectures generally fail to produce finegrained segmentation with accurate boundaries due to their progressive down-sampling. Hence, we introduce a generic framework to overcome the issue, integrating the graph convolutional network (GCN) and deep structured feature embedding (DSFE) into an end-to-end workflow. Furthermore, instead of using a classic graph convolutional neural network, we propose a gated graph convolutional network, which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification. Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks. Our proposed framework with the new GCN architecture outperforms state-of-the-art approaches. Although our main task in this work is building footprint extraction, the proposed method can be generally applied to other binary or multi-label segmentation tasks.

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Section: Introductionmentioning

confidence: 99%

Building segmentation through a gated graph convolutional neural network with deep structured feature embedding

Shi

Zhu

2020

ISPRS Journal of Photogrammetry and Remote Sensing

100

View full text Add to dashboard Cite

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“…3D information still needs to be included in the CNN-based boundary classification. Compared to computer vision, the amount and size of benchmark image data are marginal: Existing benchmarks cover aerial data for urban object classification [38] and building extraction [39], satellite imagery for road extraction, building extraction and land cover classification [40], as well as satellite and aerial imagery for road extraction [41]. Such benchmarks in combination with open data initiatives for governmental cadastral data [42], aerial imagery [43] and crowdsourced labeling [44][45][46] may propel deep learning frameworks for cadastral boundary delineation, i.e., cadastral intelligence.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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“…Deep-learning methods, especially FCN-based models, are widely adopted for automatic building extraction from large-scale aerial images [57,58]. Compared to conventional methods, the FCN-based models significantly improve segmentation performance when tested on various benchmark datasets [59,60]. Recently, more advanced FCN-based models have enhanced feature representation capabilities to achieve better model performance (e.g., FPN, MC-FCN, and BR-Net).…”

Section: Regarding the Proposed Feature Alignment Frameworkmentioning

confidence: 99%

A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation

Guo

Song

et al. 2019

Remote Sensing

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Applying deep-learning methods, especially fully convolutional networks (FCNs), has become a popular option for land-cover classification or segmentation in remote sensing. Compared with traditional solutions, these approaches have shown promising generalization capabilities and precision levels in various datasets of different scales, resolutions, and imaging conditions. To achieve superior performance, a lot of research has focused on constructing more complex or deeper networks. However, using an ensemble of different fully convolutional models to achieve better generalization and to prevent overfitting has long been ignored. In this research, we design four stacked fully convolutional networks (SFCNs), and a feature alignment framework for multi-label land-cover segmentation. The proposed feature alignment framework introduces an alignment loss of features extracted from basic models to balance their similarity and variety. Experiments on a very high resolution(VHR) image dataset with six categories of land-covers indicates that the proposed SFCNs can gain better performance when compared to existing deep learning methods. In the 2nd variant of SFCN, the optimal feature alignment gains increments of 4.2% (0.772 vs. 0.741), 6.8% (0.629 vs. 0.589), and 5.5% (0.727 vs. 0.689) for its f1-score, jaccard index, and kappa coefficient, respectively.

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TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings

Cited by 109 publications

References 66 publications

Building segmentation through a gated graph convolutional neural network with deep structured feature embedding

Building segmentation through a gated graph convolutional neural network with deep structured feature embedding

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation

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