Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

Yang, Hsiuhan Lexie; Yuan, Jiazheng; Lunga, Dalton; Laverdiere, Melanie; Rose, Amy; Bhaduri, B. L.

doi:10.1109/jstars.2018.2835377

Cited by 174 publications

(121 citation statements)

References 32 publications

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“…The main factors of data source selection for BFE are related to separation between buildings from non-buildings (spatial resolution considerations), the confusing effect of vegetationcover on building detection (spectral resolution considerations), and the shade of building/non-building objects and lighting conditions (types of sensors considerations). The majority of related works that uses the multi-sensor data consist of very high spatial resolution multispectral images and light detection and ranging (LiDAR) data (Huang et al, 2019;Li et al, 2013;Rottensteiner et al, 2003;Volpi and Tuia, 2018;Yang et al, 2018). LiDAR data (also known as point clouds) and digital surface models (DSMs) generated by aerial platform equipped with airborne laser scanning, such as unmanned aerial vehicle or aircraft are applicable for the automatic BFE, because these data provide the geometrical features of buildings shapes (Cai et al, 2019;Jung and Sohn, 2019;Rottensteiner et al, 2007;Sohn and Dowman, 2007).…”

Section: Data Sourcementioning

confidence: 99%

“…Highlight of characteristics ConvNet+ SignedDist (Yuan, 2018) -Integrating multi-layer information and a unique output representation -Combine signed-distance labels with ConvNet ABF+SegNet (Masouleh and Shah-Hosseini, 2018) -Fusion of convolutional layer with adaptive bilateral filter -The minimum bounding rectangle were used for outline regularization SegNet-Dist-Fused (Yang et al, 2018) -Combine signed-distance labels with SegNet -Encoding stage based on residual learning network -Improving feature learning with a gated feature labelling unit Table 1. Overview of recent BFE researches using DL models…”

Section: Algorithmmentioning

confidence: 99%

“…The evaluation metrics of overall accuracy (OA) and intersection over union (IoU) are used to evaluate the performance of the models based on the values of true positive (white), false positive (red), false negative (blue), and true negative (black). The OA and IoU are the most used evaluation measures for BFE in many previous studies (Khoshboresh Masouleh and Shah-Hosseini, 2019b;Maggiori et al, 2017;Masouleh and Sadeghian, 2019;Volpi and Tuia, 2018;Xu et al, 2018;Yang et al, 2018). The OA and IoU are defined as:…”

Section: Building Footprint Extractionmentioning

confidence: 99%

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Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

Khoshboresh-Masouleh¹,

Saradjian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 ABSTRACT:Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.

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Section: Data Sourcementioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Building Footprint Extractionmentioning

confidence: 99%

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Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

Khoshboresh-Masouleh¹,

Saradjian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In recent years, deep convolutional neural networks (DCNNs) [4] have been widely used in image classification [5], object detection [6] and semantic segmentation [7,8], because of their end-to-end learning mechanism and feature representation. In the remote sensing field, some DCNN-based segmentation approaches, such as U-Net [8], Deeplabv3+ [7], etc., are used to achieve excellent results in building detection [9][10][11][12][13][14]. All of these approaches are fully supervised, which means a pixel-level label benchmark is essential for training the semantic segmentation networks.…”

Section: Introductionmentioning

confidence: 99%

SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion

Chen

Zhang

et al. 2020

Remote Sensing

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The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method.

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“…Establishing the spatial feature that contains the shape information of buildings can be convenient for building extraction and change detection in VHR images. For example, feature points and line information are obtained to get buildings' locations [23][24][25]; indexes that can describe specific attributes of buildings are designed, such as the length and width of connected pixel groups [26], the pixel shape index (PSI) [27] and the morphological building index (MBI) [28]; and a deep convolutional neural network is used to find the expression of the characteristics of buildings [29][30][31]. By combining spatial features and shape information with spectrum and texture features, various methods have been proposed for building extraction and change detection [23][24][25][26][27][28][29][30][31][32][33][34][35][36].However, the diversity of buildings in VHR remote sensing images is various, that is, differences in spectral, shape, textural and spatial background information naturally exist among buildings.…”

mentioning

confidence: 99%

Line-Constrained Shape Feature for Building Change Detection in VHR Remote Sensing Imagery

Liu

Min-hua

Chen

et al. 2018

IJGI

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Buildings represent the most relevant features of human activity in urban regions, but their change detection using very-high-resolution (VHR) remote sensing imagery is still a major challenge. Effective representation of the building is the key point in building change detection. The linear feature can indirectly represent the structure and distribution of man-made objects. Thus, this study proposes a shape feature-based building change detection method. Specifically, a line-constrained shape (LCS) feature is developed to capture the shape characteristics of buildings. This feature improves the discriminability between buildings and other ground objects by integrating the pixel shape feature and line segments. The building candidate area (BCA) is created in accordance with the distribution of the line segments in two-phase images. The problem space is constrained in a high-likelihood region of buildings because of the BCA. Comparative experimental results demonstrate that the combination of the spectral feature and the developed LCS feature achieves the best performance in object-based building change detection in VHR imagery.

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Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

Cited by 174 publications

References 32 publications

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

Robust Building Footprint Extraction From Big Multi-Sensor Data Using Deep Competition Network

SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion

Line-Constrained Shape Feature for Building Change Detection in VHR Remote Sensing Imagery

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