[1] 陈俊勇.面向数字中国建设中国的现代测绘基准——对我国"十五"大地测量工作的思考和建议[J].测绘通报, 2001(3):1-3. CHEN Junyong.Digital China-oriented construction of China's modern geodetic datum[J]. Bulletin of Surveying and Mapping, 2001(3):1-3. [2] 陈军,刘万增,武昊,等.智能化测绘的基本问题与发展方向[J].测绘学报, 2021, 50(8):995-1005. DOI:10.11947/j.AGCS.2021.20210235. CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping:fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005. DOI:10.11947/j.AGCS.2021.20210235. [3] 张永生,张振超,童晓冲,等.地理空间智能研究进展和面临的若干挑战[J].测绘学报, 2021, 50(9):1137-1146. DOI:10.11947/j.AGCS.2021.20200420. ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, et al. Progress and challenges of geospatial artificial intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9):1137-1146. DOI:10.11947/j.AGCS.2021.20200420. [4] 杨元喜,杨诚,任夏. PNT智能服务[J].测绘学报, 2021, 50(8):1006-1012. DOI:10.11947/j.AGCS.2021.20210051. YANG Yuanxi, YANG Cheng, REN Xia. PNT intelligent services[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1006-1012. DOI:10.11947/j.AGCS.2021.20210051. [5] 龚健雅,许越,胡翔云,等.遥感影像智能解译样本库现状与研究[J].测绘学报, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085. GONG Jianya, XU Yue, HU Xiangyun, et al. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085. [6] 张继贤,李海涛,顾海燕,等.人机协同的自然资源要素智能提取方法[J].测绘学报, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. ZHANG Jixian, LI Haitao, GU Haiyan, et al. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. [7] 李志林,刘万增,徐柱,等.时空数据地图表达的基本问题与研究进展[J].测绘学报, 2021, 50(8):1033-1048.DOI:10.11947/j.AGCS.2021.20210072. LI Zhilin, LIU Wanzeng, XU Zhu, et al. Cartographic representation of spatio-temporal data:fundamental issues and research progress[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1033-1048. DOI:10.11947/j.AGCS.2021.20210072. [8] 史文中,张敏.人工智能用于遥感目标可靠性识别:总体框架设计、现状分析及展望[J].测绘学报, 2021, 50(8):1049-1058. DOI:10.11947/j.AGCS.2021.20210095. SHI Wenzhong, ZHANG Min. Artificial intelligence for reliable object recognition from remotely sensed data:overall framework design, review and prospect[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1049-1058. DOI:10.11947/j.AGCS.2021.20210095. [9] 杨必胜,韩旭,董震.适用于城市场景大规模点云语义标识的深度学习网络[J].测绘学报, 2021, 50(8):1059-1067. DOI:10.11947/j.AGCS.2021.20210093. YANG Bisheng, HAN Xu, DONG Zhen. A deep learning network for semantic labeling of large-scale urban point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1059-1067. DOI:10.11947/j.AGCS.2021.20210093. [10] 张永军,万一,史文中,等.多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J].测绘学报, 2021, 50(8):1068-1083.DOI:10.11947/j.AGCS.2021.20210079. ZHANG Yongjun, WAN Yi, SHI Wenzhong, et al. Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1068-1083. DOI:10.11947/j.AGCS.2021.20210079. [11] 郝彤,王晓峰,冯甜甜,等.地球系统多尺度关键区域与关键过程的智能化测绘[J].测绘学报, 2021, 50(8):1084-1095. DOI:10.11947/j.AGCS.2021.20210109. HAO Tong, WANG Xiaofeng, FENG Tiantian, et al. Intelligent and multi-scale surveying of key areas and processes of the Earth system[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1084-1095. DOI:10.11947/j.AGCS.2021.20210109. [12] 张广运,张荣庭,戴琼海,等.测绘地理信息与人工智能2.0融合发展的方向[J].测绘学报, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021.20210200. ZHANG Guangyun, ZHANG Rongting, DAI Qionghai, et al. The direction of integration surveying and mapping geographic information and artificial intelligence 2.0[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021.20210200. [13] 吴立新,李佳,苗则朗,等.冰川流域孕灾环境及灾害的天空地协同智能监测模式与方向[J].测绘学报, 2021, 50(8):1109-1121. DOI:10.11947/j.AGCS.2021.20210107. WU Lixin, LI Jia, MIAO Zelang, et al. Pattern and directions of spaceborne-airborne-ground collaborated intelligent monitoring on the geo-hazards developing environment and disasters in glacial basin[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1109-1121. DOI:10.11947/j.AGCS.2021.20210107. [14] 陶超,阴紫薇,朱庆,等.遥感影像智能解译:从监督学习到自监督学习[J].测绘学报, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. TAO Chao, YIN Ziwei, ZHU Qing, et al. Remote sensing image intelligent interpretation:from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. [15] BROUWER T, KOEVA M, VAN OOSTEROM P, et al. Smart surveyors:developments and trends from the FIG working week 2020[J]. GIM International, 2020,232:1547-1558. [16] YANG B, LI Y, ZOU X, et al. Amarker-free calibration method for mobile laser scanning point clouds correction[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020:347-354. [17] DONG Zhen, YANG Bisheng, LIU Yuan, et al. A novel binary shape context for 3D local surface description[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:431-452. [18] LI Jianping, YANG Bisheng, CHEN Chi, et al. Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136:41-57. [19] TAO A, SAPRA K, CATANZARO B. Hierarchical multi-scale attention for semantic segmentation[J]. 2020. [20] FAN Wen, YANG Bisheng, DONG Zhen, et al. Confidence-guided roadside individual tree extraction for ecological benefit estimation[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102:102368. [21] LEPETIT V, MORENO-NOGUER F, FUA P. EPnP:an accurate O (n) solution to the PnP problem[J]. International Journal of Computer Vision, 2008, 81(2):155-166. [22] QI C R, LIU Wei, WU Chenxia, et al. Frustum PointNets for 3D object detection from RGB-D data[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA:IEEE, 2018:918-927. [23] SHAO Shuai, LI Zeming, ZHANG Tianyuan, et al. Objects365:a large-scale, high-quality dataset for object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision.Seoul, Korea (South):IEEE, 2019:8429-8438. [24] ZHU Zhe, LIANG Dun, ZHANG Songhai, et al. Traffic-sign detection and classification in the wild[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.Las Vegas, NV, USA:IEEE, 2016:2110-2118. [25] CAI Zhaowei, VASCONCELOS N. Cascade R-CNN:delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA:IEEE, 2018:6154-6162. [26] YANG B, WANG J, CLARK R, et al. Learning object bounding boxes for 3D instance segmentation on point clouds[C]//Proceedings of the Advances in neural information processing systems.Vancouver,Canada:IEEE,2019, 32. [27] QI C R, YI L, SU H, et al. Pointnet++:Deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the Advances in neural information processing systems.Long Beach,CALIFORNIA,USA:IEEE,2017, 30. [28] WENG Xinshuo, WANG Jianren, HELD D, et al. 3D multi-object tracking:a baseline and new evaluation metrics[C]//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, NV, USA:IEEE, 2021:10359-10366. |