Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

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Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽

10.3390/s18103347 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3347 ◽

Cited By ~ 14

Author(s):

Zhishuang Yang ◽

Bo Tan ◽

Huikun Pei ◽

Wanshou Jiang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Laser Scanning ◽

Laser Scanner ◽

Region Growing ◽

Point Clouds ◽

Classification Method ◽

Multi Scale ◽

Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

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Quantitative Expressions of Spatial Similarity in Multi-scale Map Spaces

Advances in Cartography and GIScience - Lecture Notes in Geoinformation and Cartography ◽

10.1007/978-3-319-57336-6_28 ◽

2017 ◽

pp. 405-416

Author(s):

Haowen Yan ◽

Liming Zhang ◽

Zhonghui Wang ◽

Weifang Yang ◽

Tao Liu ◽

...

Keyword(s):

Multi Scale ◽

Spatial Similarity

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Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1552790 ◽

2018 ◽

Vol 34 (4) ◽

pp. 661-680 ◽

Cited By ~ 3

Author(s):

Zhou Guo ◽

Chen-Chieh Feng

Keyword(s):

Point Clouds ◽

Semantic Classification ◽

Multi Scale

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A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040239 ◽

2020 ◽

Vol 9 (4) ◽

pp. 239 ◽

Cited By ~ 1

Author(s):

Yi Xiao ◽

Tinghua Ai ◽

Min Yang ◽

Xiang Zhang

Keyword(s):

Age Groups ◽

Adaptive Strategy ◽

Adaptive Method ◽

Building Structure ◽

Multi Scale ◽

Performance Requirements ◽

Service Information ◽

Developing Area ◽

Map Scale ◽

Functional Partitioning

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.

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