Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks

Maltezos, Evangelos; Doulamis, Anastasios; Ioannidis, Charalabos

doi:10.1109/lgrs.2018.2867736

Cited by 75 publications

(57 citation statements)

References 13 publications

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“…Experimental results indicate that a combination of raw image data with height information provides potentials in robust and efficient building detection. They further employ a CNN classifier for building extraction from the LiDAR data [41]. The proposed deep learning classifier outperforms the compared linear and nonlinear classification methods.…”

Section: A Data-fusion-based Methodsmentioning

confidence: 99%

Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Chen

Shi

Zhou

et al. 2020

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

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Extracting buildings from remotely sensed data is a fundamental task in many geospatial applications. However, this task is resistant to automation due to variability in building shapes and the environmental complexity surrounding buildings. To solve this problem, this article introduces a novel automatic building extraction method that integrates LiDAR data and high spatial resolution imagery using adaptive iterative segmentation and hierarchical overlay analysis based on data fusion. An adaptive iterative segmentation method overcomes over-and undersegmentation based on the globalized probability of boundary contour detection algorithm. A data-fusion-based hierarchical overlay analysis extracts building candidate regions based on segmentation results. A morphological operation optimizes a building candidate region to obtain final building results. Experiments were conducted on the international society for photogrammetry and remote sensing (ISPRS) Vaihingen benchmark dataset. The extracted building footprints were compared with those extracted using the state-ofthe-art methods. Evaluation results show that the proposed method achieved the highest area-based quality compared to results from the other tested methods on the ISPRS website. A detailed comparison with four state-of-the-art methods shows that the proposed method requiring no samples achieves competitive extraction results. Furthermore, the proposed method achieved a completeness of 94.1%, a correctness of 90.3%, and a quality of 85.5% over the whole Vaihingen dataset, indicating that the method is robust, with great potential in practical applications.

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Section: A Data-fusion-based Methodsmentioning

confidence: 99%

Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Chen

Shi

Zhou

et al. 2020

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

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“…Figure 7 shows CSS algorithm. The corner points are repeatedly traced through the scale space to improve positional accuracy of the corner points ( [36,37]). MKPs detected from each SRI were combined by taking average coordinates of the MKPs.…”

Section: Anisotropic Diffusionmentioning

confidence: 99%

“…It is expected that the DL approach will be extended to reconstruct various types of the 3D building models by utilizing variety of the geospatial data such as airborne and/or terrestrial LiDAR, multi/hyperspectral imagery, thermal-IR imagery, topographic maps, and various information derived from LiDAR data. Maltezos et al [37] proposed multidimensional feature vector that consists of the raw LiDAR data, and additional features including entropy, height variation, intensity, distribution of normal vectors, number of returns, planarity, and standard deviation. The feature vector was used for training data with deep convolutional neural networks to extract buildings.…”

Section: Introductionmentioning

confidence: 99%

Determination of Building Model Key Points Using Multidirectional Shaded Relief Images Generated from Airborne LiDAR Data

Lee

2019

Journal of Sensors

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Light detection and ranging (LiDAR) data collected from airborne laser scanner system is one of the major sources to reconstruct Earth’s surface features. This paper presents a method for detecting model key points (MKPs) of the buildings using LiDAR point clouds. The proposed approach utilizes shaded relief images (SRIs) derived from the LiDAR data. The SRIs based on the concept of the shape from shading could provide unique information about individual surface patches of the building roofs. The main advantage of the proposed approach is to detect directly MKPs, which are primitives for 3D building modeling, without segmenting point clouds. Depending on the location of the light source, the SRIs are created differently. Therefore, integration of the multidirectional SRIs created from different locations of the light source could provide more reliable results. In addition, the vertical exaggeration (i.e., scaling Z-coordinates) is also beneficial because constituent surface patches of the roofs in the SRIs created with vertically exaggerated LiDAR data are more distinguishable. To determine the MKPs of the roofs, building data was separated from other objects using modified marker-controlled watershed algorithm in accordance with criteria to specify buildings such as area, height, and standard deviation. This process could remove the unnecessary objects such as trees, vegetation, and cars. The curvature scale space (CSS) corner detector was used to determine MKP since this method is robust to geometric changes such as rotation, translation, and scale. The proposed method was applied to simulated and real LiDAR datasets with various roof types. The experimental results show that the proposed method is effective in determining MKPs of various roof types with high level of detail (LoD).

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“…Yang [27] accurately established the United States building distribution map based on various training sets from different geographical regions. For the multi-source features, Sun [28], Maltezos [29], and Huang [30] proposed a deep neural network model that combines Lidar data with optical remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Arbitrary-Shaped Building Boundary-Aware Detection With Pixel Aggregation Network

Jiang

Zhang

Xin

et al. 2021

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

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Large-scale building extraction is an essential work in the field of remote sensing image analysis. The high-resolution image extraction methods based on deep learning have achieved state-of-the-art performance. However, most of the previous work has focused on region accuracy rather than boundary quality. Aiming at the low accuracy problems and incomplete boundary of the building extraction method, we propose a predictive optimization architecture, BAPANet. Notably, the architecture consists of an encoder-decoder network and residual refinement modules responsible for prediction and refinement. The objective function optimizes the network in the form of three levels (pixel, feature map, and patch) by fusing three loss functions: binary cross-entropy (BCE), intersection over-union (IoU) and structural similarity (SSIM). The five public datasets' experimental results show that the extraction method in this paper has high region accuracy, and the boundary of buildings is clear and complete.

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Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks

Cited by 75 publications

References 13 publications

Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Determination of Building Model Key Points Using Multidirectional Shaded Relief Images Generated from Airborne LiDAR Data

Arbitrary-Shaped Building Boundary-Aware Detection With Pixel Aggregation Network

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