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学者姓名：毛政元
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Abstract ：
科学地评价人居环境有助于发现城乡建设现状存在的不足从而调整优化发展策略，是支撑城乡规划重要的基础工作.该文以马斯洛需求层次理论结合多源数据分析方法构建指标体系，集成木桶理论与互斥互吸模型融合评价指标，利用地理探测器定量剖析人居环境时序演变的驱动因素，构建城市人居环境质量综合评价模型，并以福州市2012—2021年1 km栅格的多源数据为样本开展实证研究，对于提升人居环境评价的理论与应用水平均具有参考价值.结果表明：1)福州市人居环境水平呈现出从沿海向内陆递减的地带性以及从中心城市向周边递减的圈层分布结构，“东强西弱”与“南高北低”的地域分异特征明显.2)空间上相邻的人居环境单元之间具有较强的相互作用.3)教育、医疗、购物与休闲等方面资源供给的空间分布与调整是影响人居环境质量及其变化的关键因子.
Keyword ：
人居环境 人居环境 指标融合 指标融合 木桶理论 木桶理论 非线性模型 非线性模型
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| GB/T 7714 | 李悦 , 毛政元 , 柯文岚 . 福州市人居环境质量综合评价 [J]. | 华中师范大学学报(自然科学版) , 2024 , 58 (04) : 433-442 . |
| MLA | 李悦 等. "福州市人居环境质量综合评价" . | 华中师范大学学报(自然科学版) 58 . 04 (2024) : 433-442 . |
| APA | 李悦 , 毛政元 , 柯文岚 . 福州市人居环境质量综合评价 . | 华中师范大学学报(自然科学版) , 2024 , 58 (04) , 433-442 . |
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With the popularization of smartphones, online car-hailing has become a common travel alternative and plays an important role in meeting public travel demand. Therefore, online car-hailing operation platforms have been a major component of Intelligent Transportation Systems in which passenger demand prediction is one of the core problems to be solved. However, models proposed in the existing literature usually ignore the long-term temporal correlation and multiple spatial correlations. This paper presented a Spatio-Temporal Multi-Graph Convolutional Network Fused With Global Features (GST-MGCN) to address the limitations of existing research achievements, taking full account of the unique spatiotemporal correlations of the travel demand of online car-hailing passengers. Following the Closeness, Period, and Trend (CPT) paradigm, the model fitted temporal dependencies with time series information. By identifying multiple spatial semantic correlations, the corresponding relational graph structure was constructed, and a multi-graph convolutional model was built in which the global features fusion module employed gated fusion and sum fusion methods to capture sudden and gradual changes of passenger demand, respectively. Taking the Haikou city dataset as an example, our experimental results show that the values of the three indicators, MAE, RMSE, and MAPE of the GST-MGCN model proposed in this paper were 2.269, 3.917, and 21.447, respectively, which were lower than those derived from other similar mainstream models. This study demonstrated that the proposed model GST-MGCN can effectively mine the spatio-temporal pattern of online car hailing passenger travel demand, extract the impact of global features, and accurately predict it. © 2023 Journal of Geo-Information Science. All rights reserved.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning E-learning E-learning Forecasting Forecasting Graph neural networks Graph neural networks Intelligent systems Intelligent systems Online systems Online systems Semantics Semantics
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| GB/T 7714 | Huang, Xin , Mao, Zhengyuan . Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network [J]. | Journal of Geo-Information Science , 2023 , 25 (2) : 311-323 . |
| MLA | Huang, Xin 等. "Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network" . | Journal of Geo-Information Science 25 . 2 (2023) : 311-323 . |
| APA | Huang, Xin , Mao, Zhengyuan . Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network . | Journal of Geo-Information Science , 2023 , 25 (2) , 311-323 . |
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地理对象关系网尤其是邻近关联生成的网络是客观存在的,有着基本的拓扑结构,最重要的拓扑性质是其度及度分布.在进行空间数据计算分析时,人们往往进行邻近关联操作(分析).为了进一步加深对地理对象邻近关联的认识,以生态斑块邻近关系网为案例,近似测算客观地理对象邻近关系网的度分布.结果表明:度分布在尺度、时间和地域上是保持一致的;在特定范围,度分布服从幂律分布,地理对象邻近关联网络具有无标度特性.另外,度与邻近对象的属性具有一定相关性.
Keyword ：
地理对象 地理对象 度 度 度分布 度分布 邻近关系网络 邻近关系网络
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| GB/T 7714 | 韦思亮 , 毛政元 . 地理对象邻近关联网络度分布测算及其意义 [J]. | 测绘地理信息 , 2023 , 48 (1) : 107-111 . |
| MLA | 韦思亮 等. "地理对象邻近关联网络度分布测算及其意义" . | 测绘地理信息 48 . 1 (2023) : 107-111 . |
| APA | 韦思亮 , 毛政元 . 地理对象邻近关联网络度分布测算及其意义 . | 测绘地理信息 , 2023 , 48 (1) , 107-111 . |
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Models based on convolutional neural networks (CNNs) have achieved remarkable advances in high-resolution remote sensing (HRRS) images scene classification, but there are still challenges due to the high similarity among different categories and loss of local information. To address this issue, a multigranularity alternating feature mining (MGA-FM) framework is proposed in this article to learn and fuse both global and local information for HRRS scene classification. First, a region confusion mechanism is adopted to guide network's shallow layers to adaptively learn the salient features of distinguishing regions. Second, an alternating comprehensive training strategy is designed to capture and fuse shallow local feature information and deep semantic information to enhance feature representation capabilities. In particular, the MGA-FM framework can be flexibly embedded in various CNN backbone networks as a training mechanism. Extensive experimental results and visualization analysis on three remote sensing scene datasets indicated that the proposed method can achieve competitive classification performance.
Keyword ：
Convolutional neural network (CNN) Convolutional neural network (CNN) feature mining feature mining local detailed information local detailed information remote sensing image remote sensing image scene classification scene classification
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| GB/T 7714 | Weng, Qian , Huang, Zhiming , Lin, Jiawen et al. Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 318-330 . |
| MLA | Weng, Qian et al. "Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 318-330 . |
| APA | Weng, Qian , Huang, Zhiming , Lin, Jiawen , Jian, Cairen , Mao, Zhengyuan . Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 318-330 . |
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随着智能手机的普及,网约车成为常用的出行替代方式。网约车运营平台因此成为智能交通系统的主要组成部分,在满足公众出行需求中发挥重要作用。乘客需求预测是网约车系统需要解决的核心问题,现有文献中提出的模型忽略了长期时间相关性及多种空间相关性,本文针对现有研究成果存在的局限性,在充分考虑网约车乘客出行需求时空相关独特性的基础上,提出一种融合全局特征的时空多图卷积网络(Spatio-Temporal Multi-Graph Convolutional Network Fused With Global Features,GST-MGCN)模型。该模型遵循临近性、周期性和趋势性(Closeness, Period and Trend,CPT)范式,利用时序信息拟合时间依赖关系;通过识别多种空间语义相关性构建对应的关系图结构、建立多图卷积模型;模型中的全局特征融合模块,使用门控融合和总和融合方法分别捕捉乘客需求的突变和渐变。以海口市数据集为样本的实验结果表明,本文提出的GSTMGCN模型MAE、RMSE和MAPE指标的值分别是2.269、3.917、21.447,优于其他同类主流模型。本研究证明提出的模型GST-MGCN可以有效挖掘网约车乘客出行需求的时空模式,提取全局特征的影响,对其进行准确的预测。
Keyword ：
全局特征 全局特征 图卷积神经网络 图卷积神经网络 城市计算 城市计算 外部因素融合 外部因素融合 时空数据 时空数据 深度学习 深度学习 网约车需求预测 网约车需求预测
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| GB/T 7714 | 黄昕 , 毛政元 . 基于时空多图卷积网络的网约车乘客需求预测 [J]. | 地球信息科学学报 , 2023 , 25 (02) : 311-323 . |
| MLA | 黄昕 et al. "基于时空多图卷积网络的网约车乘客需求预测" . | 地球信息科学学报 25 . 02 (2023) : 311-323 . |
| APA | 黄昕 , 毛政元 . 基于时空多图卷积网络的网约车乘客需求预测 . | 地球信息科学学报 , 2023 , 25 (02) , 311-323 . |
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Relationship networks of geo-objects，especially the networks generated by adjacent associations，exist objec⁃ tively and have a basic topological structure. The most impor⁃ tant topological properties are degree and degree distribution. When calculating and analyzing spatial data，researchers often carry out adjacent association operations（analysis）. To deepen the understanding of the adjacent relationships of geo-objects，we take the relationship networks of ecological patches as an example to approximately estimate the degree distribution of the adjacent networks of objective geo-objects. The result shows that the degree distribution is consistent in scales，times，and zones. The degree distribution follows the power-law distribution in a certain range. And the adjacent relation⁃ ship networks have scale-free properties. In addition，a corre⁃ lation exists between the degree of a geo-object and the prop⁃ erties of its neighbors. © 2023 Wuhan University. All rights reserved.
Keyword ：
adjacent network adjacent network degree degree degree distribution degree distribution geo-object geo-object
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| GB/T 7714 | Wei, S. , Mao, Z. . Calculation of Degree Distribution of Adjacent Networks Among Geo-objects and Its Significance; [地 理 对 象 邻 近 关 联 网 络 度 分 布 测 算 及 其 意 义] [J]. | Journal of Geomatics , 2023 , 48 (1) : 107-111 . |
| MLA | Wei, S. et al. "Calculation of Degree Distribution of Adjacent Networks Among Geo-objects and Its Significance; [地 理 对 象 邻 近 关 联 网 络 度 分 布 测 算 及 其 意 义]" . | Journal of Geomatics 48 . 1 (2023) : 107-111 . |
| APA | Wei, S. , Mao, Z. . Calculation of Degree Distribution of Adjacent Networks Among Geo-objects and Its Significance; [地 理 对 象 邻 近 关 联 网 络 度 分 布 测 算 及 其 意 义] . | Journal of Geomatics , 2023 , 48 (1) , 107-111 . |
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距离是空间位置的函数,定量、精确地揭示空间位置不确定性向距离不确定性传递的函数关系具有重要的理论与现实意义,是测绘与地理信息领域亟待解决的重大科学问题.针对该问题现有解决方案的局限性,在满足与不确定点观测位置对应的实际位置在误差圆内服从完全空间随机分布的前提下,推导了二维空间中一个确定点与一个不确定点间以及两个不确定点间距离不确定性的概率分布函数和对应的概率密度函数,并利用后者研究了点位不确定性向距离不确定性传递的规律,为研究与解决距离不确定性问题开辟了新的途径.研究结果表明,确定点与不确定点间以及两个不确定点间的距离不确定性均服从如下规律:(1)当误差圆半径(对应点位精度)与点间观测距离同时改变时,前者与后者之比与距离不确定性正相关.(2)当误差圆半径保持不变时,距离不确定性与点间观测距离负相关.(3)当点间观测距离保持不变时,距离不确定性与误差圆半径正相关.当误差圆半径与点间观测距离一致时,两个不确定点间距离的不确定性大于确定点和不确定点间距离的不确定性;当该条件不成立时,涉及不确定点数不同的距离不确定性不具可比性.
Keyword ：
不确定性 不确定性 二维空间 二维空间 测度方法 测度方法 点位 点位 距离 距离
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| GB/T 7714 | 毛政元 , 范琳娜 , 李霖 . 二维空间中距离不确定性的测度方法研究 [J]. | 武汉大学学报（信息科学版） , 2023 , 48 (12) : 1969-1977 . |
| MLA | 毛政元 et al. "二维空间中距离不确定性的测度方法研究" . | 武汉大学学报（信息科学版） 48 . 12 (2023) : 1969-1977 . |
| APA | 毛政元 , 范琳娜 , 李霖 . 二维空间中距离不确定性的测度方法研究 . | 武汉大学学报（信息科学版） , 2023 , 48 (12) , 1969-1977 . |
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Objectives: Distances are functions of spatial positions. Precisely revealing the functional relationship which quantitatively embodies the transmission of uncertainty from spatial positions to their distance, a key scientific problem in need of being solved urgently in geomatics, has important theoretical and practical significance. Methods: Aiming at the limitation of presently available solution of the above mentioned problem, under the premise of that the real position corresponding with the observed one of an uncertain point follows the complete spatial random distribution within the error circle, we have derived the probability distribution function of the distance uncertainty and the corresponding density function containing an uncertain point and those between two uncertain points respectively in two-dimensional space. The latter has been employed to explore the transmission law of point uncertainties to distance uncertainties, opening up a new way for studying and solving the problem of distance uncertainties. Results: The results show that for all cases: (1) When the radius of the error circle (corresponding to the point position accuracy) and the observed distance between points change simultaneously, their ratio has a significant positive correlation with the level of distance uncertainties. (2) When the former remains constant, the distance uncertainty has a significant negative correlation with the latter. (3) When the latter remains constant, the distance uncertainty has a significant positive correlation with the former. Conclusions: As far as the distance uncertainty of cases containing an uncertain point and the one of those between two uncertain points are concerned, the latter is obviously greater than the former when the radius of the error circle and the observed distance between points are consistent for both of them. Otherwise they are not comparable. © 2023 Wuhan University. All rights reserved.
Keyword ：
distance distance measurement methods measurement methods point position point position two-dimensional space two-dimensional space uncertainty uncertainty
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| GB/T 7714 | Mao, Z. , Fan, L. , Li, L. . Methodological Research on Measuring Distance Uncertainties in Two-Dimensional Space; [二 维 空 间 中 距 离 不 确 定 性 的 测 度 方 法 研 究] [J]. | Geomatics and Information Science of Wuhan University , 2023 , 48 (12) : 1969-1977 . |
| MLA | Mao, Z. et al. "Methodological Research on Measuring Distance Uncertainties in Two-Dimensional Space; [二 维 空 间 中 距 离 不 确 定 性 的 测 度 方 法 研 究]" . | Geomatics and Information Science of Wuhan University 48 . 12 (2023) : 1969-1977 . |
| APA | Mao, Z. , Fan, L. , Li, L. . Methodological Research on Measuring Distance Uncertainties in Two-Dimensional Space; [二 维 空 间 中 距 离 不 确 定 性 的 测 度 方 法 研 究] . | Geomatics and Information Science of Wuhan University , 2023 , 48 (12) , 1969-1977 . |
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Semantic segmentation in high-resolution aerial images is a fundamental and challenging task with a wide range of applications. Although many segmentation methods with convolutional neural networks have achieved inspiring results, it is still difficult to distinguish regions with similar spectral features only using high-resolution data. Besides, the traditional data-independent upsampling methods may lead to suboptimal results. This letter proposes a multisensor data fusion model (MSDFM). Following the classical encoder-decoder structure, MSDFM regards colored digital surface models (colored-DSMs) data as a complementary input for further detailed feature extraction. A data-dependent upsampling (DUpsampling) method is adopted in the decoder stage instead of the common upsampling approaches to improve the classification accuracy of pixels of the small objects. Extensive experiments on Vaihingen and Potsdam datasets demonstrate that our proposed MSDFM outperforms most related models. Significantly, segmentation performance for the car category surpasses state-of-the-art methods over the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset.
Keyword ：
Automobiles Automobiles Decoding Decoding Deconvolution Deconvolution Digital surface model (DSM) Digital surface model (DSM) Feature extraction Feature extraction high-resolution aerial images high-resolution aerial images Image segmentation Image segmentation Semantics Semantics semantic segmentation semantic segmentation Vegetation Vegetation
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| GB/T 7714 | Weng, Qian , Chen, Hao , Chen, Hongli et al. A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Weng, Qian et al. "A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Weng, Qian , Chen, Hao , Chen, Hongli , Guo, Wenzhong , Mao, Zhengyuan . A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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Short-term traffic flow prediction with high accuracy and efficiency plays an important role in Intelligent Transportation Systems, which is a prerequisite for traffic guidance, management, and control. Due to the time-varying and non-stationary characteristics of the dynamic change of traffic flow, it is difficult to predict traffic flow with high accuracy, which needs to be resolved urgently in the transportation field. In order to improve the accuracy and efficiency of short-term traffic flow prediction, the paper develops a short-term traffic flow predicting algorithm based on adaptive time slice and the improved KNN model (A-TS-KNN), which is then implemented successfully in short-term traffic flow predicting experiments. In the first, the Dynamic Time Warping (DTW) algorithm is used to dynamically slice the daytime sequence of traffic flow into different traffic patterns. Secondly, the mutual information method is used to solve the maximum threshold of the time delays of traffic flow at each time in different traffic patterns. Then the traffic flow state vectors of different time delays is constructed, which generates a history database of traffic flow. Thirdly, the method of ten times ten-fold cross-validation is used to solve the orthogonal error distribution of different time delays and K values of traffic flow at each time. The orthogonal result with the smallest error is selected, and the parameters combination of adaptive time delay and K value are obtained. In the end, the weighted value of the reciprocal Euclidean distance of the K most similar neighbors is used for predicting traffic flow of next time. The forecasting accuracies of the improved A-TS-KNN and other four models including K-Nearest Neighbors (KNN) model, Support Vector Regression (SVR) model, Long-Short Term Memory (LSTM) neural networks, and Gate Recurrent Unit (GRU) neural networks are compared. The experimental results indicate that the improved A-TS-KNN model is more appropriate for short-term traffic flow forecasting than the other models. In addition, the A-TS-KNN algorithm is used for short-term traffic flow predicting at other four different intersections in the urban road network of Fuzhou, which has been shown good generalization ability. © 2022, Science Press. All right reserved.
Keyword ：
Efficiency Efficiency Forecasting Forecasting Intelligent systems Intelligent systems Long short-term memory Long short-term memory Nearest neighbor search Nearest neighbor search Street traffic control Street traffic control Time delay Time delay Timing circuits Timing circuits
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| GB/T 7714 | Qi, Duo , Mao, Zhengyuan . Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN [J]. | Journal of Geo-Information Science , 2022 , 24 (2) : 339-351 . |
| MLA | Qi, Duo et al. "Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN" . | Journal of Geo-Information Science 24 . 2 (2022) : 339-351 . |
| APA | Qi, Duo , Mao, Zhengyuan . Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN . | Journal of Geo-Information Science , 2022 , 24 (2) , 339-351 . |
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