Building Extraction from Airborne Laser Point Cloud Using NDVI Constrained Watershed Algorithm

Zongze, 赵宗泽 Zhao; Yongjun, 张永军 Zhang

doi:10.3788/aos201636.1028002

Cited by 4 publications

(4 citation statements)

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“…As a means of active mapping and mapping of surface spatial information, it has the advantages of fast all-day acquisition of three-dimensional coordinates, rich data information and high precision, safe operation, and certain penetration of ground vegetation, etc. At present, it has been widely used in many fields of geospatial information disciplines such as digital ground model acquisition (Hui Zhenyang, et al, 2018;Huang Zuowei, et al, 2018), road extraction (Cheng Xiaojun, et al, 2018), power line extraction (Lin Xiangguo, 2017), forest parameter estimation (Zhao Zongze, et al, 2016), three-dimensional city model establishment (Yang Wei, et al, 2018), point cloud classification (Lin Xiangguo, 2017;Zhang Aiwu, et al, 2016). In China, the research and application development of this technology is slightly late, and the relevant data processing methods need to be further explored and improved.…”

Section: Airborne Light Detection and Ranging Combines Globalmentioning

confidence: 99%

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Hu,

2024

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

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Abstract. Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.

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Section: Airborne Light Detection and Ranging Combines Globalmentioning

confidence: 99%

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Hu,

2024

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

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“…They are widely used in remote sensing image information extraction [14][15][16][17][18][19][20], bringing a new research perspective for efficiently evaluating traditional village buildings. Compared with traditional methods such as the feature detection method [21][22][23][24], region segmentation method [25][26][27][28][29][30], and auxiliary information combination method [31][32][33][34][35][36], remote sensing information extraction based on deep learning can spatially model adjacent pixels and obtain higher quality results in processing numerous vision tasks. Xiong et al [14] proposed a detection model for traditional Chinese houses-Hakka Weirong Houses (HWHs)-based on ResNet50 and YOLO v2 and combined with multi-metric evaluation proved that the model has high accuracy and excellent performance; Liu et al [15] drew the advantages of U-Net and ResNet and proposed the SSNet deep residual learning sequence semantic segmentation model, and proved the superiority of the model through experiments.…”

Section: Introductionmentioning

confidence: 99%

Traditional Village Building Extraction Based on Improved Mask R-CNN: A Case Study of Beijing, China

et al. 2023

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As an essential material carrier of cultural heritage, the accurate identification and effective monitoring of buildings in traditional Chinese villages are of great significance to the sustainable development of villages. However, along with rapid urbanization in recent years, many towns have experienced problems such as private construction, hollowing out, and land abuse, destroying the traditional appearance of villages. This study combines deep learning technology and UAV remote sensing to propose a high-precision extraction method for conventional village architecture. Firstly, this study constructs the first sample database of traditional village architecture based on UAV remote sensing orthophotos of eight representative villages in Beijing, combined with fine classification; secondly, in the face of the diversity and complexity of the built environment in traditional villages, we use the Mask R-CNN instance segmentation model as the basis and Path Aggregate Feature Pyramid Network (PAFPN) and Atlas Space Pyramid Pool (ASPP) as the main strategies to enhance the backbone model for multi-scale feature extraction and fusion, using data increment and migration learning as auxiliary means to overcome the shortage of labeled data. The results showed that some categories could achieve more than 91% accuracy, with average precision, recall, F1-score, and Intersection over Union (IoU) values reaching 71.3% (+7.8%), 81.9% (+4.6%), 75.7% (+6.0%), and 69.4% (+8.5%), respectively. The application practice in Hexi village shows that the method has good generalization ability and robustness, and has good application prospects for future traditional village conservation.

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“…Therefore, how to extract buildings with algorithms rather than human experts is an immediate challenge to be addressed. Traditional extraction methods can generally be divided into feature detection-based methods [5][6][7], area segmentationbased methods [8][9][10][11] and auxiliary information-combined methods [12][13][14][15][16][17][18]. However, based on handcrafted features such as spectral, shadow, and texture features, these traditional methods can only process the low-or mid-level information contained in images, and their building extraction results usually have poor accuracy and integrity [19].…”

Section: Introductionmentioning

confidence: 99%

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

Qiu

Yin

et al. 2022

Remote Sensing

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There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks.

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Building Extraction from Airborne Laser Point Cloud Using NDVI Constrained Watershed Algorithm

Cited by 4 publications

References 0 publications

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Traditional Village Building Extraction Based on Improved Mask R-CNN: A Case Study of Beijing, China

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

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