Pub Date : 2023-04-03DOI: 10.1080/10095020.2023.2178336
Jie Shan, Zhixin Li, Damon J. Lercel, Kevan Tissue, J. Hupy, Joshua Carpenter
ABSTRACT Photogrammetry is experiencing an era of democratization mostly due to the popularity and availability of many commercial off-the-shelf devices, such as drones and smartphones. They are used as the most convenient and effective tools for high-resolution image acquisition for a wide range of applications in science, engineering, management, and cultural heritage. However, the quality, particularly the geometric accuracy, of the outcomes from such consumer sensors is still unclear. Furthermore, the expected quality under different control schemes has yet to be thoroughly investigated. This paper intends to answer those questions with a comprehensive comparative evaluation. Photogrammetry, in particular, structure from motion, has been used to reconstruct a 3D building model from smartphone and consumer drone images, as well as from professional drone images, all under various ground control schemes. Results from this study show that the positioning accuracy of smartphone images under direct geo-referencing is 165.4 cm, however, this could be improved to 43.3 cm and 14.5 cm when introducing aerial lidar data and total station surveys as ground control, respectively. Similar results are found for consumer drone images as well. For comparison, this study shows the use of the professional drone is able to achieve a positioning accuracy of 3.7 cm. Furthermore, we demonstrate that through the combined use of drone and smartphone images we are able to obtain full coverage of the entire target with a 2.3 cm positioning accuracy. Our study concludes that smartphone images can achieve an accuracy equivalent to consumer drone images and can be used as the primary data source for building facade data collection.
{"title":"Democratizing photogrammetry: an accuracy perspective","authors":"Jie Shan, Zhixin Li, Damon J. Lercel, Kevan Tissue, J. Hupy, Joshua Carpenter","doi":"10.1080/10095020.2023.2178336","DOIUrl":"https://doi.org/10.1080/10095020.2023.2178336","url":null,"abstract":"ABSTRACT Photogrammetry is experiencing an era of democratization mostly due to the popularity and availability of many commercial off-the-shelf devices, such as drones and smartphones. They are used as the most convenient and effective tools for high-resolution image acquisition for a wide range of applications in science, engineering, management, and cultural heritage. However, the quality, particularly the geometric accuracy, of the outcomes from such consumer sensors is still unclear. Furthermore, the expected quality under different control schemes has yet to be thoroughly investigated. This paper intends to answer those questions with a comprehensive comparative evaluation. Photogrammetry, in particular, structure from motion, has been used to reconstruct a 3D building model from smartphone and consumer drone images, as well as from professional drone images, all under various ground control schemes. Results from this study show that the positioning accuracy of smartphone images under direct geo-referencing is 165.4 cm, however, this could be improved to 43.3 cm and 14.5 cm when introducing aerial lidar data and total station surveys as ground control, respectively. Similar results are found for consumer drone images as well. For comparison, this study shows the use of the professional drone is able to achieve a positioning accuracy of 3.7 cm. Furthermore, we demonstrate that through the combined use of drone and smartphone images we are able to obtain full coverage of the entire target with a 2.3 cm positioning accuracy. Our study concludes that smartphone images can achieve an accuracy equivalent to consumer drone images and can be used as the primary data source for building facade data collection.","PeriodicalId":58518,"journal":{"name":"武测译文","volume":"26 1","pages":"175 - 188"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44934394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/10095020.2023.2175478
Bisheng Yang, Nobert Haala, Zhen Dong
ABSTRACT With the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery. Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science, spatial cognition, and smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial information from point clouds has become a scientific frontier, for which there is an urgent demand in the fields of surveying and mapping, as well as geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the-art of point cloud intelligence, with regard to acquisition equipment, intelligent processing, scientific research, and engineering applications. For this purpose, we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection, as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds. These projects were conducted at the Institute for Photogrammetry, the University of Stuttgart, which was initially headed by the late Prof. Ackermann. Finally, the development prospects of point cloud intelligence are summarized.
{"title":"Progress and perspectives of point cloud intelligence","authors":"Bisheng Yang, Nobert Haala, Zhen Dong","doi":"10.1080/10095020.2023.2175478","DOIUrl":"https://doi.org/10.1080/10095020.2023.2175478","url":null,"abstract":"ABSTRACT With the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery. Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science, spatial cognition, and smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial information from point clouds has become a scientific frontier, for which there is an urgent demand in the fields of surveying and mapping, as well as geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the-art of point cloud intelligence, with regard to acquisition equipment, intelligent processing, scientific research, and engineering applications. For this purpose, we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection, as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds. These projects were conducted at the Institute for Photogrammetry, the University of Stuttgart, which was initially headed by the late Prof. Ackermann. Finally, the development prospects of point cloud intelligence are summarized.","PeriodicalId":58518,"journal":{"name":"武测译文","volume":"26 1","pages":"189 - 205"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43942349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/10095020.2023.2231736
Ralf W. Schroth
PREFACE The paper contains the author’s personal account of the professional achievements of Professor Friedrich (Fritz) Ackermann based on the author’s memories and private documents which demonstrate Ackermann’s significant influence throughout various professional phases over a period of four decades. The structure of the paper is based on the path of the author’s studies, the period as a member of the Institute of Photogrammetry at Stuttgart University, and finally, a professional career in photogrammetry, geoinformation and management. The emphasis is on the character and skills of Friedrich Ackermann. Profession, team spirit and social skills are demonstrated with impressive examples.
{"title":"In memory of Friedrich Ackermann: a personal view","authors":"Ralf W. Schroth","doi":"10.1080/10095020.2023.2231736","DOIUrl":"https://doi.org/10.1080/10095020.2023.2231736","url":null,"abstract":"PREFACE The paper contains the author’s personal account of the professional achievements of Professor Friedrich (Fritz) Ackermann based on the author’s memories and private documents which demonstrate Ackermann’s significant influence throughout various professional phases over a period of four decades. The structure of the paper is based on the path of the author’s studies, the period as a member of the Institute of Photogrammetry at Stuttgart University, and finally, a professional career in photogrammetry, geoinformation and management. The emphasis is on the character and skills of Friedrich Ackermann. Profession, team spirit and social skills are demonstrated with impressive examples.","PeriodicalId":58518,"journal":{"name":"武测译文","volume":"26 1","pages":"153 - 155"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48140275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-08DOI: 10.1080/10095020.2023.2165974
Bin Jia, Guoqing Zhou
{"title":"Estimation of global karst carbon sink from 1950s to 2050s using response surface methodology","authors":"Bin Jia, Guoqing Zhou","doi":"10.1080/10095020.2023.2165974","DOIUrl":"https://doi.org/10.1080/10095020.2023.2165974","url":null,"abstract":"","PeriodicalId":58518,"journal":{"name":"武测译文","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41856989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-08DOI: 10.1080/10095020.2023.2167615
R. Sahraei, A. Ghorbanian, Y. Kanani-Sadat, S. Jamali, Saeid Homayouni
{"title":"Mangrove plantation suitability mapping by integrating multi criteria decision making geospatial approach and remote sensing data","authors":"R. Sahraei, A. Ghorbanian, Y. Kanani-Sadat, S. Jamali, Saeid Homayouni","doi":"10.1080/10095020.2023.2167615","DOIUrl":"https://doi.org/10.1080/10095020.2023.2167615","url":null,"abstract":"","PeriodicalId":58518,"journal":{"name":"武测译文","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47756599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-08DOI: 10.1080/10095020.2022.2136539
Xiao-Le Deng, W. Shen, M. Kuhn, C. Hirt, R. Pail
{"title":"Sensing the global CRUST1.0 Moho by gravitational curvatures of crustal mass anomalies","authors":"Xiao-Le Deng, W. Shen, M. Kuhn, C. Hirt, R. Pail","doi":"10.1080/10095020.2022.2136539","DOIUrl":"https://doi.org/10.1080/10095020.2022.2136539","url":null,"abstract":"","PeriodicalId":58518,"journal":{"name":"武测译文","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47408567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-03DOI: 10.1080/10095020.2023.2169199
Ruoqi Wang, Guiying Li, Yagang Lu, D. Lu
{"title":"A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LIDAR data","authors":"Ruoqi Wang, Guiying Li, Yagang Lu, D. Lu","doi":"10.1080/10095020.2023.2169199","DOIUrl":"https://doi.org/10.1080/10095020.2023.2169199","url":null,"abstract":"","PeriodicalId":58518,"journal":{"name":"武测译文","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42877458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detection of geothermal potential based on land surface temperature derived from remotely sensed and in-situ data","authors":"Fei Zhao, Zhiyan Peng, Jiangkang Qian, Chen Chu, Zhifang Zhao, Jiangqin Chao, Shiguang Xu","doi":"10.1080/10095020.2023.2178335","DOIUrl":"https://doi.org/10.1080/10095020.2023.2178335","url":null,"abstract":"","PeriodicalId":58518,"journal":{"name":"武测译文","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43602144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：