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学者姓名：陈崇成
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在公网受限的应急环境中,利用无人机辅助物联网能促进传感数据的及时传递.当考虑无线通信距离时,无人机作为移动收集器在有限续航时间内收集尽可能多的传感数据的路径规划可建模为足够近定向问题(CEOP).现有求解CEOP的算法是逐个计算目标节点的访问顺序及其邻域内的采集点,这在节点邻域较大并覆盖周围多个节点时效率低下,这些方法也没有考虑数据传输时间和无人机遥控距离等约束.为此,建立了大邻域多约束无人机数据收集路径规划的数学模型,提出了基于贪婪随机自适应搜索过程(GRASP)的GRASP-LN算法进行求解.该算法不重复计算重合的采集点,而是维护路径每个航点采集的节点集合,无人机在每个航点悬停一段时间以收集集合内节点的数据.公开的CEOP数据集的实验结果表明,GRASP-LN比GSOA、VNS和GRASPopt具有更好的求解质量和更短的计算时间.与基线算法GRASPopt相比,GRASP-LN的路径奖励平均提高了5.86%,最大提高了14.91%,执行时间平均减少了69%,特别在节点邻域平均覆盖4.67个以上节点时,GRASP-LN的路径奖励和稳定性均优于GRASPopt.考虑数据传输时间和无人机遥控距离约束的实验验证了GRASP-LN算法对考虑这些约束的无人机数据收集路径规划问题的有效性.
Keyword ：
数据收集 数据收集 无人机 无人机 物联网 物联网 贪婪随机自适应搜索过程 贪婪随机自适应搜索过程 足够近定向问题 足够近定向问题 路径规划 路径规划
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| GB/T 7714 | 潘淼鑫 , 陈崇成 . 大邻域多约束无人机数据收集路径规划 [J]. | 计算机科学与探索 , 2025 , 19 (1) : 158-168 . |
| MLA | 潘淼鑫 等. "大邻域多约束无人机数据收集路径规划" . | 计算机科学与探索 19 . 1 (2025) : 158-168 . |
| APA | 潘淼鑫 , 陈崇成 . 大邻域多约束无人机数据收集路径规划 . | 计算机科学与探索 , 2025 , 19 (1) , 158-168 . |
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Indoor point clouds often present significant challenges due to the complexity and variety of structures and high object similarity. The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. To address these challenges, we propose a novel network architecture, PointMSGT, which includes a multi-scale geometric feature extraction (MSGFE) module and a global Transformer (GT) module. The MSGFE module consists of a geometric feature extraction (GFE) module and a multi-scale attention (MSA) module. The GFE module reconstructs triangles through each point's two neighbors and extracts detailed local geometric relationships by the triangle's centroid, normal vector, and plane constant. The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. The global Transformer employs a self-attention mechanism to capture long-range dependencies across the entire point cloud. The proposed method demonstrates competitive performance in real-world indoor scenarios, with a mIoU of 68.6% in semantic segmentation on S3DIS and OA of 86.4% in classification on ScanObjectNN.
Keyword ：
geometric feature geometric feature multi-scale attention multi-scale attention point cloud analysis point cloud analysis real-world indoor scenario real-world indoor scenario transformer transformer
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| GB/T 7714 | Chen, Yisheng , Xiao, Yu , Wu, Hui et al. Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis [J]. | MATHEMATICS , 2024 , 12 (23) . |
| MLA | Chen, Yisheng et al. "Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis" . | MATHEMATICS 12 . 23 (2024) . |
| APA | Chen, Yisheng , Xiao, Yu , Wu, Hui , Chen, Chongcheng , Lin, Ding . Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis . | MATHEMATICS , 2024 , 12 (23) . |
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Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R-2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km(2) (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the "Sickle Bend" region of China (the "Sickle Bend" policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.
Keyword ：
Crop mapping Crop mapping Cross -region Cross -region Maize index Maize index National -scale National -scale Spatiotemporal variations Spatiotemporal variations
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| GB/T 7714 | Huang, Yingze , Qiu, Bingwen , Yang, Peng et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
| MLA | Huang, Yingze et al. "National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 221 (2024) . |
| APA | Huang, Yingze , Qiu, Bingwen , Yang, Peng , Wu, Wenbin , Chen, Xuehong , Zhu, Xiaolin et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
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Tourism knowledge graphs lack cultural content, limiting their usefulness for cultural tourists. This paper presents the development of a cultural perspective-based knowledge graph (CuPe-KG). We evaluated fine-tuning ERNIE 3.0 (FT-ERNIE) and ChatGPT for cultural type recognition to strengthen the relationship between tourism resources and cultures. Our investigation used an annotated cultural tourism resource dataset containing 2,745 items across 16 cultural types. The results showed accuracy scores for FT-ERNIE and ChatGPT of 0.81 and 0.12, respectively, with FT-ERNIE achieving a micro-F1 score of 0.93, a 26 percentage point lead over ChatGPT's score of 0.67. These underscore FT-ERNIE's superior performance (the shortcoming is the need to annotate data) while highlighting ChatGPT's limitations because of insufficient Chinese training data and lower identification accuracy in professional knowledge. A novel ontology was designed to facilitate the construction of CuPe-KG, including elements such as cultural types, historical figures, events, and intangible cultural heritage. CuPe-KG effectively addresses cultural tourism visitors' information retrieval needs.
Keyword ：
ChatGPT ChatGPT Cultural tourism Cultural tourism Cultural type Cultural type Knowledge graph Knowledge graph Pretrained language models Pretrained language models Travel intelligence Travel intelligence
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| GB/T 7714 | Fan, Zhanling , Chen, Chongcheng . CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (3) . |
| MLA | Fan, Zhanling et al. "CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models" . | INFORMATION PROCESSING & MANAGEMENT 61 . 3 (2024) . |
| APA | Fan, Zhanling , Chen, Chongcheng . CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (3) . |
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Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree dense reconstruction and the exported point cloud models are compared with point clouds derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has a higher successful reconstruction rate, better reconstruction in the canopy area and requires less images as input. Compared with the photogrammetric dense reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tend to be noisy and of low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those derived from the NeRF point cloud. The results of this study illustrate the great potential of the NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes.
Keyword ：
3D reconstruction 3D reconstruction 3D tree modeling 3D tree modeling deep learning deep learning individual tree individual tree lidar lidar neural radiance field (NeRF) neural radiance field (NeRF) photogrammetry photogrammetry terrestrial laser scanning terrestrial laser scanning
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| GB/T 7714 | Huang, Hongyu , Tian, Guoji , Chen, Chongcheng . Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method [J]. | REMOTE SENSING , 2024 , 16 (6) . |
| MLA | Huang, Hongyu et al. "Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method" . | REMOTE SENSING 16 . 6 (2024) . |
| APA | Huang, Hongyu , Tian, Guoji , Chen, Chongcheng . Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method . | REMOTE SENSING , 2024 , 16 (6) . |
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元宇宙快速发展正在影响着文旅行业的变革。文旅行业强调创意与场景体验，文旅产品具有重内容、重体验、重参与及重个性化的特点，这与元宇宙的发展高度契合。现阶段，文旅元宇宙的研究还处于襁褓阶段，对文旅元宇宙的概念、核心技术和应用场景还处于探索中。首先，本文回顾了元宇宙的演化进程，并基于文旅行业特征，提出了文旅元宇宙的基本定义、概念模型及主要特征，认为文旅元宇宙是元宇宙的一个子系统，是现有信息技术在文旅行业深度融合形成的文旅互联网形态，是文旅活动在三维数字世界的一种重构和虚拟共生。其基于扩展现实技术提供文旅场景的沉浸式体验，基于数字孪生技术生成现实世界文旅场景的镜像，并依托元宇宙统一架构下的政治、经济、文化等体系，实现文旅行业虚拟世界和现实世界的全方位融合；然后，对文旅元宇宙相关关键技术在文旅行业应用的研究进展和应用进行了详细的描述；最后，展望了文旅元宇宙在文化遗产数字化与保护、景区（酒店）开发与管理、导游导览服务、文旅营销、行业监管与市场治理等方面可能的应用场景，提出了文旅元宇宙未来重点的研究方向。虽然文旅元宇宙的发展还面临着诸多的挑战，技术的发展还要不断地经历螺旋式的上升，但是元宇宙终会重塑未来的社会形态和人类的生活方式。
Keyword ：
元宇宙 元宇宙 扩展现实 扩展现实 文化旅游 文化旅游 虚实融合 虚实融合 虚拟世界 虚拟世界 虚拟地理环境 虚拟地理环境 遥感 遥感
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| GB/T 7714 | 范占领 , 陈崇成 . 文旅元宇宙：概念、关键技术及应用场景 [J]. | 遥感学报 , 2024 , 28 (05) : 1161-1176 . |
| MLA | 范占领 et al. "文旅元宇宙：概念、关键技术及应用场景" . | 遥感学报 28 . 05 (2024) : 1161-1176 . |
| APA | 范占领 , 陈崇成 . 文旅元宇宙：概念、关键技术及应用场景 . | 遥感学报 , 2024 , 28 (05) , 1161-1176 . |
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Given the ecological, cultural and psychological functions of urban vegetation, a growing number of studies have focused on daily accessible greenery visibility. Mobile laser scanning (MLS) can rapidly obtain dense point clouds that can be used to extract vegetation. To address this issue, we propose a novel method for calculating the green view index (GVI) based on MLS three-dimensional (3D) point clouds. In our study, the GVI was specified as the ratio of greenery viewing angles to the total number of viewing angles in view. The GVI calculation procedure was as follows. First, the vegetation points were extracted using the density-based spatial clustering of appli-cations with noise (DBSCAN) algorithm and the PointNet++ deep learning algorithm. Second, based on the GVI specification, a virtual camera was constructed in a 3D point scenario to estimate greenery viewing angles in view and generate depth images, and then, the GVI value was calculated. This method is flexible and can be used to calculate the GVI at any site with any direction where 3D point scene data are available, and thus, it is suitable for evaluating various types of urban greenery. We conducted a case human-centered assessment of road greenery in a partial area of Jinshan District in Fuzhou, China, based on MLS point clouds, evaluating visible greenery and analyzing the relations among the GVI, greening pattern, and road green belt mode. The results showed that the overall visible greenery in the study area was good and that the GVI value of most road sections was more than 15 %. The method has potential for urban green space planning and management.
Keyword ：
Green view index Green view index Point clouds Point clouds Three-dimensional scene Three-dimensional scene Urban greenery Urban greenery Visual perception Visual perception
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| GB/T 7714 | Tang, Liyu , He, Jianguo , Peng, Wei et al. Assessing the visibility of urban greenery using MLS LiDAR data [J]. | LANDSCAPE AND URBAN PLANNING , 2023 , 232 . |
| MLA | Tang, Liyu et al. "Assessing the visibility of urban greenery using MLS LiDAR data" . | LANDSCAPE AND URBAN PLANNING 232 (2023) . |
| APA | Tang, Liyu , He, Jianguo , Peng, Wei , Huang, Hongyu , Chen, Chongcheng , Yu, Can . Assessing the visibility of urban greenery using MLS LiDAR data . | LANDSCAPE AND URBAN PLANNING , 2023 , 232 . |
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Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite- 2 (ICESat- 2) products provide reliable global references for the accuracy evaluation and correction of Global Digital Elevation Model (GDEM). However, existing DEM correction methods mainly address the signal of vegetation in DEM errors and mostly use linear regression models. So, we first analyze the relationship between Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM v3 data accuracy and the land cover type, elevation, slope, relief amplitude, and vegetation coverage. Based on this, this paper proposes a Digital Elevation Model (DEM) error correction method that takes into account various influencing factors and combines Extreme Gradient Boosting (XGBoost) machine learning and spatial interpolation to model the errors. The analysis of the results shows that the overall error of the original ASTER GDEM has a normal distribution with a large negative offset (average error of - 3.463 m). The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of original ASTER GDEM are 12.930 m and 16.695 m, respectively, and the elevation accuracy decreases with the increase of elevation, slope, relief amplitude, and vegetation coverage. After correction, the Mean Error (ME) of ASTER GDEM is reduced to -0.233 m, which means the negative deviation is effectively removed and the overall MAE and overall RMSE are reduced by 26.04% and 23.56%, respectively. The MAE and RMSE of DEM for cultivated lands, forests, grasslands, wetlands, water bodies, and man-made surfaces are all reduced by different degrees. The DEM accuracy evaluation and correction method proposed in this paper models the non-linear relationships between multiple feature elements and terrain errors and achieves better correction results in the study area. © 2023 Journal of Geo-Information Science. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Digital instruments Digital instruments Error correction Error correction Forestry Forestry Geomorphology Geomorphology Interpolation Interpolation Landforms Landforms Mean square error Mean square error Normal distribution Normal distribution Regression analysis Regression analysis Surveying Surveying Vegetation Vegetation
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| GB/T 7714 | Jiao, Huaijin , Chen, Chongcheng , Huang, Hongyu . Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data [J]. | Journal of Geo-Information Science , 2023 , 25 (2) : 409-420 . |
| MLA | Jiao, Huaijin et al. "Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data" . | Journal of Geo-Information Science 25 . 2 (2023) : 409-420 . |
| APA | Jiao, Huaijin , Chen, Chongcheng , Huang, Hongyu . Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data . | Journal of Geo-Information Science , 2023 , 25 (2) , 409-420 . |
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Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R-2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R-2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R-2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.
Keyword ：
canopy height canopy height forest age forest age Fujian Fujian GEDI GEDI LiDAR LiDAR time-series remote sensing time-series remote sensing
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| GB/T 7714 | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China [J]. | REMOTE SENSING , 2023 , 15 (2) . |
| MLA | Zhou, Xiaocheng et al. "Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China" . | REMOTE SENSING 15 . 2 (2023) . |
| APA | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping , Wang, Xiaoqin , Chen, Chongcheng , Chen, Yunzhi et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China . | REMOTE SENSING , 2023 , 15 (2) . |
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With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the reliability of these data when monitoring height and growth changes of low-growing saplings in an afforestation plot via UAV RGB images. This study focuses on an artificial Chinese fir (Cunninghamia lancelota, named as Chinese Fir) young forest plot in Fujian, China. Divide-and-conquer (DAC) and the local maximum (LM) method for extracting seedling height are described in the paper, and the possibility of monitoring young forest growth based on low-cost UAV remote sensing images was explored. Two key algorithms were adopted and compared to extract the tree height and how it affects the young forest at single-tree level from multi-temporal UAV RGB images from 2019 to 2021. Compared to field survey data, the R-2 of single saplings' height extracted from digital orthophoto map (DOM) images of tree pits and original DSM information using a divide-and-conquer method reached 0.8577 in 2020 and 0.9968 in 2021, respectively. The RMSE reached 0.2141 in 2020 and 0.1609 in 2021. The R-2 of tree height extracted from the canopy height model (CHM) via the LM method was 0.9462. The RMSE was 0.3354 in 2021. The results demonstrated that the survival rates of the young forest in the second year and the third year were 99.9% and 85.6%, respectively. This study shows that UAV RGB images can obtain the height of low sapling trees through a computer algorithm based on using 3D point cloud data derived from high-precision UAV images and can monitor the growth of individual trees combined with multi-stage UAV RGB images after afforestation. This research provides a fully automated method for evaluating the afforestation results provided by UAV RGB images. In the future, the universality of the method should be evaluated in more afforestation plots featuring different tree species and terrain.
Keyword ：
forest survey forest survey height change height change RGB images RGB images saplings saplings tree height tree height unmanned aerial vehicle unmanned aerial vehicle
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| GB/T 7714 | Zhou, Xiaocheng , Wang, Hongyu , Chen, Chongcheng et al. Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level [J]. | FORESTS , 2023 , 14 (1) . |
| MLA | Zhou, Xiaocheng et al. "Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level" . | FORESTS 14 . 1 (2023) . |
| APA | Zhou, Xiaocheng , Wang, Hongyu , Chen, Chongcheng , Nagy, Gabor , Jancso, Tamas , Huang, Hongyu . Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level . | FORESTS , 2023 , 14 (1) . |
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