Acta Geodaetica et Cartographica Sinica

COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area

XIA Jizhe, ZHOU Ying, LI Zhen, LI Fan, YUE Yang, CHENG Tao, LI Qingquan

2020, 49(6): 671-680. doi:10.11947/j.AGCS.2020.20200080

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The rapid spread of the novel coronavirus (COVID-19) from late 2019 to early 2020 poses a huge challenge to the public health of China and the world. The risk assessment of COVID-19 plays an essential role in the decision making of epidemic prevention. As one of the most important metropolitan areas in China, Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is seriously affected by COVID-19. A massive number of returnees after the holidays further poses potential COVID-19 risks. Targeting on the urgent need of COVID-19 risk assessment in GBA, we combine multi-source urban spatiotemporal big data and traditional epidemiological model to design an improved model. Specifically, the improved model introduces dynamic “return-to-work” population and propagation hotspots to calibrate COVID-19 parameters in different assessment units and improve SEIR model suitability in GBA; targeting on the urgent needs of high resolution (e.g. community level) risk assessment, the model utilizes multi-source urban big data (e.g, mobile phone) to improve modelling spatial resolution from more detailed population and COVID-19 OD matrix. The simulation results show that: ① compared with the traditional SEIR model, the proposed model has better capability for risk assessment in GBA; ② the massive population flow in GBA introduces considerable COVID-19 risk in GBA; ③ a variety of epidemic prevention initiatives in China are highly effective for delaying the spread of COVID-19 in GBA.

An efficient sparse graph index method for dynamic and associated data

ZHU Qing, FENG Bin, LI Maosu, CHEN Meite, XU Zhaowen, XIE Xiao, ZHANG Yeting, LIU Mingwei, HUANG Zhiqin, FENG Yicong

2020, 49(6): 681-691. doi:10.11947/j.AGCS.2020.20190287

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In order to efficiently organize and manage the increasing real-time sensor data and associations, and satisfy the requirements of multi-level tasks for multi-dimensional feature calculation and association mining of multi-modal scene data, a spatiotemporal sparse graph index method is proposed for the bottleneck problems of disk I/O-intensive, low processing efficiency and weak support for associations existing in the tree structure based external indexing methods. Firstly, a spatiotemporal index structure based on in-memory graph model is designed, which abstracts multi-modal scene data into nodes and edges of graph and supports efficient organization of time, location and associations of multi-modal scene data. Then, a sparse matrix based method of in-memory representation and storage for spatiotemporal graph index is presented. Finally, taking the multi-dimensional tree index as an example, the index construction and multi-model query experiments are carried out. The experimental results show that the method is superior to the contrast method in several aspects, such as generation efficiency, query performance, and then supports real-time high-performance processing of dynamic and associated multi-modal scene data with low latency access.

Road learning extraction method based on vehicle trajectory data

LU Chuanwei, SUN Qun, CHEN Bing, WEN Bowei, ZHAO Yunpeng, XU Li

2020, 49(6): 692-702. doi:10.11947/j.AGCS.2020.20190305

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Road information extraction based on vehicle trajectory data is one of the hotspots and difficulties in the field of geographic information. The rapid development of depth learning provides a new idea and method for solving this problem. Aiming at the problem of roadway-level road extraction based on vehicle trajectory data, this paper introduces the generative adversarial nets in the field of deep learning, uses residual network to construct deep network and multi-scale receptive field to perceive different details of trajectory data, and constructs roadway-level road extraction model under the constraint of trajectory direction based on conditional generative adversarial nets. Firstly, the orientation-color mapping rasterization conversion method is proposed to transform the trajectory orientation information into HSV color space. Then, the parameters of the model are learned with the sample data. Finally, the trained model is applied to three experimental areas of Zhengzhou, Chengdu and Nanjing to extract the road data at the roadway level. The experimental results showed that the proposed method can effectively extract the complete road data at the roadway level.

Simplification method of building polygon based on feature edges reconstruction

YIN Shuo, YAN Xiaoming, YAN Xiongfeng

2020, 49(6): 703-710. doi:10.11947/j.AGCS.2020.20190299

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This paper presents an approach to simplify building polygons with blurred outline. Definitions of main direction and feature edges that control overall structure of building polygon are proposed to maintain its regular shape. Firstly, the polygon is squared based on the main direction which is calculated by statistical weighting method. Secondly, the feature edges are grouped and abstracted into several types of local structures with corresponding reconstruction rules after they are found in the right-angled building polygon according to the definition of feature edges. Finally, the spatial relationship of the feature edges in the structure is distinguished to select the right reconstruction rule to simplify polygon. The experiments under real data shows that the method is able to restore the rectangularity of the building polygon effectively, maintain the area and the overall shape structure, and hold high reliability and practicability.

Spherical great circle arcs based indicators for evaluating distortions of map projections

YAN Jin, YANG Xuan, LI Ni, GONG Guanghong

2020, 49(6): 711-723. doi:10.11947/j.AGCS.2020.20190101

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Map projection is an important research content of cartography. However, distortions are inevitable for any map projection. To evaluate the distortions of map projections, averaged ratio between complementary profiles and spherical great circle arcs based shape and area metrics are proposed. By using Bonne projection as an example and exploiting correlation analyses, it is indicated that great circle arcs based indicators simplify the calculation process of small circle arcs based indicators (i.e., the averaged ratio between complementary profiles),and both great and small circle arcs based indicators are highly correlated with each other. Great circle arcs based indicators are also highly correlated with classical differential calculation based indicators(the Pearson product-moment correlation coefficient between them is greater than 0.988), while the proposed metrics are independent on the differential calculation. By utilizing the method of regression analyses, small regression errors are also achieved(the mean error of linear regression is less than 1.10‰). Finally, in order to reduce the number of sampling points and avoid the inconsistency of sampling points for different map projections, this paper also proposes and verifies a random sampling based calculating process. In all word,great circle arcs based indicators could effectively evaluate the distortions of map projections.

Automatic extraction and classification of pole-like objects from vehicle LiDAR point cloud

LI Yongqiang, LI Pengpeng, DONG Yahan, FAN Huilong

2020, 49(6): 724-735. doi:10.11947/j.AGCS.2020.20190220

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Aiming at the poor quality of vehicle LiDAR point cloud data and the mutual concealment of various ground objects in urban road scenes, an automatic extraction and classification algorithm for pole-like objects was proposed. Firstly, ground points in point cloud data were removed by improving the mathematical morphology algorithm. According to the morphological characteristics of the pole-like objects, preliminary extraction of pole-like objects was carried out through the longitudinal grid template.Secondly, the extracted suspected pole-like objects were regularized with point cloud data and some noise was removed by statistical analysis. Finally, SVM classification model was trained according to the previously established pole-like object samples to classify the extracted pole-like objects. The experimental results showed that the method could effectively extract the pole-like objects in urban road scenes under the condition of poor data quality, and classified the extracted pole-like objects with high precision.

Method of close-range space intersection combining multi-image forward intersection with single hidden layer neural network

LI Jiatian, WANG Congcong, A Xiaohui, YAN Ling, ZHU Zhihao, GAO Peng

2020, 49(6): 736-745. doi:10.11947/j.AGCS.2020.20180600

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Aiming at the problem that the three-dimensional coordinate solution precision is influenced by the non-linear error, a method of combining multi-image forward intersection with single hidden layer BP neural network is proposed in this paper. The steps are: ①In order to obtain the initial value of the three dimensional coordinates with higher accuracy, the external parameters of the camera are optimized by constructing the Lagrange equation about the world coordinates under the constraint of known real world coordinates of the sample point. ②The single hidden layer BP neural network is trained by using the calculated 3D coordinate and the real 3D coordinate as input and output parameters, respectively. ③The initial three-dimensional coordinate is corrected by brought into the model. Experiments show that: ①In the environmental field of view of the test device, the proposed method outperforms the space intersection, the sparse bundle adjustment and the other classic neural network methods, the maximum deviation is 0.492 7 mm. ②Compared with other classic neural network methods, the network structure of this paper is 3-6-3, the structure is simple and the calculation efficiency is high.

A checking algorithm for pair-wise line matching based on collinearity constraint and matching redundancy

WANG Jingxue, LIU Suyan, WANG Weixi

2020, 49(6): 746-756. doi:10.11947/j.AGCS.2020.20190123

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A checking algorithm for pair-wise line matching is proposed to solve the problems of one-to-many, many-to-one and many-to-many in line segment matching by combining collinearity constraint and matching redundancy. The proposed method is performed on pair-wise line matches generated from existing method. Firstly, individual line segment matches are obtained based on pair-wise line matches. A relation matrix is constructed for pair-wise line matches and individual line segment matches, respectively. In each relation matrix, row and column numbers correspond to the indexes of matching primitives on the reference and searching images respectively. Each matrix element can be used to record the number of matches, feature similarity or some other multi-source information corresponding to the current row and column. Secondly, the corresponding relation of one-to-many, many-to-one and many-to-many matches is established on the basis of local relation matrix which is extracted from the aforementioned relation matrix. Then, the local relation matrix is combined with collinearity constraint, matching redundancy and feature similarity to identify and eliminate outliers in the matching results. Finally, the breaking lines in the results are fitted to produce one-to-one line segment correspondences. The proposed algorithm is evaluated on digital aerial images and close-images with typical texture features. The experimental results demonstrate the effectiveness of the proposed algorithm in pair-wise line matching result checking.

Multi-scale region growing point cloud filtering method based on surface fitting

ZHAN Zongqian, HU Mengqi, MAN Yiyun

2020, 49(6): 757-766. doi:10.11947/j.AGCS.2020.20190142

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Aiming at the problem of over-erosion and type II error accumulation by progressive triangle irregular network(TIN) densification(PTD), a multi-scale filtering method based on region growing is proposed. This method introduces pyramid strategy to establish different levels of point cloud, in which the low-level seed points are processed based on the high-level seed points. In the filtering process, the non-ground points are filtered by PTD first, and then the eroded ground seed points are compensated by the surface-fitting region growing algorithm with dynamic threshold determinated by local terrain, ultimately the real ground surface is gradually approached in loop iteration. By testing the 15 benchmark data sets provided by the ISPRS, Type I error, Type II error, Total error and Cohen’s kappa coefficient are 2.40%, 3.67%, 2.84% and 93.74% respectively, which shows that the proposed method has better performance to obtain the ideal ground model.

The collaborative mapping and navigation based on visual SLAM in UAV platform

WANG Chenjie, LUO Bin, LI Chengyuan, WANG Wei, YIN Lu, ZHAO Qing

2020, 49(6): 767-776. doi:10.11947/j.AGCS.2020.20190145

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Due to the limitation of its working space, ground robots have great limitations on environmental perception. Combining the advantages of aerial robots in perspective, it is the mainstream trend to realize the collaboration of aerial and ground robots. This paper proposes a collaborative mapping and navigation scheme based on visual SLAM in UAV platform, which utilizes the wide-area perception capability brought by the aerial view of unmanned aerial vehicle, to assist the ground robot to construct the environmental model quickly and improve the ability of ground robots to map and navigate in challenging and unknown environments. This scheme first constructs a real-time detection and tracking thread for salient closed boundaries, and proposes a novel visual SLAM solution proposed for mapping of UAV with combining point, line features and salient closed boundaries. Compared with the traditional scheme, the combination of closed boundaries greatly optimizes the effect of mapping. Secondly, the ground robot automatically plans the global path according to the initial global map obtained by the aerial robot. During the moving process, the initial map from UAV is updated by using the mounted laser sensor on the ground one. And the continuous re-planning of the path enables the ground robot to avoid collisions with obstacles. In order to verify the feasibility and advancement of the proposed scheme, simulation experiments and real experiments were carried out respectively. The experimental results show that the proposed scheme significantly improves the mapping effect, realizes the whole process of collaborative navigation and mapping scheme, which improves the ability of ground robots to perform autonomous navigation and mapping in challenging unknown areas. However, the proposed method is not effective in complex situations such as dense obstacles distribution, high and low ground level, and the implementation of 2D navigation has large limitations. Based on the fusion of multi-sensors such as Lidar, IMU, etc.. Future work needs to improve the effect of tasks such as depth estimation and pose estimation to build accurate three-dimensional occupancy grid maps, and further designs a three-dimensional air-ground collaborative mapping and navigation scheme.

Robust estimation algorithm of active contour model for river extraction in SAR images

HAN Bin, WU Yiquan

2020, 49(6): 777-786. doi:10.11947/j.AGCS.2020.20180423

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To deal with the problem that the existing active contour models are unable to extract rivers in SAR images accurately, this paper presents an new active contour model with L₁ norm and Laplacian energies. First, the external energy constraint in form of the L₂ norm in the CV model is replaced by the external energy constraint in form of the L₁ norm and then the novel energy functional is obtained. Second, an external energy constraint based on the Laplacian kernel function is proposed and added to the above energy functional. Meanwhile, the different adjustment coefficients are assigned to these two external energy constraints. Finally, the mean of median absolute deviations of pixel grayscale values inside and outside the curve is introduced to replace the constant energy weights inside and outside the curve of the model and then the completed proposed model is developed. River extraction is carried out on real SAR images and the results reveal the superiority of the proposed model on both accuracy and efficiency of the river extraction, when compared with the existing active contour models.

A deformable feature pyramid network for ship detection from remote sensing images

DENG Ruizhe, CHEN Qihao, CHEN Qi, LIU Xiuguo

2020, 49(6): 787-797. doi:10.11947/j.AGCS.2020.20190117

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As a carrier of maritime transportation, the accurate detection of ships is of great significance and value in marine environmental protection, marine fishery production management, maritime traffic and emergency disposal, and national defense security applications. In recent years, the remote sensing ship detection method based on CNN (convolutional neural network) is facing big challenges to adapt to small-scale ships with random orientation and morphological characteristics due to insufficient resolution of the final layer features and convolution fixed geometry, thus reducing the accuracy of object detection. In order to tackle this problem, a remote sensing ship detection method based on deformable feature pyramid network with multi-scale feature fusion. First, the architecture of feature pyramid network is adopted to detect small-scale ship object by using a bottom-up refinement process and multi-scale feature fusion. Then, by introducing the deformable convolution and RoI (region of interest) pooling module to adapt to the ship object with random orientation and morphological characteristics, the ship detection accuracy is further improved. Experiments on 40 000 remote sensing images and over 67 280 ship objects demonstrate that the proposed method performs better than CNN. The rate of recall, accuracy, and F₁-Score are 85.8%, 97.9% and 91.5%, respectively.

Research on methodology and the key technology of uncombined ambiguity resolution for multi-frequency and multi-GNSS precise point positioning

LIU Gen

2020, 49(6): 798-798. doi:10.11947/j.AGCS.2020.20200116

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Remote sensing image segmentation with regionalized fuzzy clustering based on Voronoi tessellation

LI Xiaoli

2020, 49(6): 799-799. doi:10.11947/j.AGCS.2020.20190328

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Atmospheric mass density model calibration using tracking data of space objects

CHEN Junyu

2020, 49(6): 800-800. doi:10.11947/j.AGCS.2020.20190217

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Research on change detection technology in multi-temporal remote sensing images

HUANG Liang

2020, 49(6): 801-801. doi:10.11947/j.AGCS.2020.20190236

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Structuring of local space-time flow field and precise estimation of real-time discharge in the Yangtze Estuary

CHEN Zhigao

2020, 49(6): 802-802. doi:10.11947/j.AGCS.2020.20190256

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BeiDou/GPS combined precise positioning theory and algorithm

YAN Li

2020, 49(6): 803-803. doi:10.11947/j.AGCS.2020.20190260

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Cloud computing method and application of large scale global GNSS network

LI Linyang

2020, 49(6): 804-804. doi:10.11947/j.AGCS.2020.20190281

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