ACCURACY ANALYSIS OF MAPPING BASED ON PHOTOS AND GCPs COLLECTED FROM GOOGLE EARTH

被引:0
|
作者
Ahmed, Ramzi [1 ]
机构
[1] Bulgarian Acad Sci, Space Res Inst, Sofia, Bulgaria
来源
关键词
D O I
暂无
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The photoic maps available on Google Earth come primarily from two sources: satellites and aircraft. Google gets this imagery and other digital mapping information from sources such as TeleAtlas and EarthSat, both of which compile photos and maps into digital form for commercial applications. Because the data comes from different sources, it is provided at different resolutions, which is why some areas of the globe appear crisp even at street level while others are blurry from a great distance. The selected test area is located in Egypt. The test area is covered by photos collected from Google Earth with an overlap and side-lap between them ranging between 15%-25%. All GCPs and CPs are collected from Google Earth, based on Universal Transverse Mercator (UTM). The minimum number of GCPs was 5 well distributed GCPs for each photo. Only two ground control points were measured from maps covering the study area on Egyptian Transverse Mercator (ETM). After collecting the required data, the methodology procedures included: firstly, geo-referencing of each photo; secondly, generating a mosaic from the geo-referenced photos; and finally, map conversion from UTM to ETM for the produced mosaic followed by linear transformation using only 2 GCPs measured from maps. In the present research, the accuracy test includes calculations of the discrepancies of (E, N) coordinates for 27 test points (CPs) located on the corrected mosaic. The (E, N) coordinates of check points CPs are compared with the corresponding ones derived from the existing map, which are considered as a reference in this research. The results of this study concluded that the photos of Google Earth can be used successfully for producing maps with suitable scale in similar study area in case of lacking remotely sensed data and field observations. They also concluded that the worries of numerous countries about the level of detail available in the Google Earth must be taken into consideration.
引用
收藏
页码:70 / 78
页数:9
相关论文
共 50 条
  • [31] Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model
    Li, Qian
    Guo, Huadong
    Luo, Lei
    Wang, Xinyuan
    REMOTE SENSING, 2022, 14 (14)
  • [32] Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine
    Lin, Jinhuang
    Jin, Xiaobin
    Ren, Jie
    Liu, Jingping
    Liang, Xinyuan
    Zhou, Yinkang
    REMOTE SENSING, 2021, 13 (07)
  • [33] Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China
    Li, Changchun
    Chen, Weinan
    Wang, Yilin
    Wang, Yu
    Ma, Chunyan
    Li, Yacong
    Li, Jingbo
    Zhai, Weiguang
    REMOTE SENSING, 2022, 14 (02)
  • [34] High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine
    Sun, Zhongchang
    Xu, Ru
    Du, Wenjie
    Wang, Lei
    Lu, Dengsheng
    REMOTE SENSING, 2019, 11 (07)
  • [35] Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine
    Liu, Xinkai
    Zhai, Han
    Shen, Yonglin
    Lou, Benke
    Jiang, Changmin
    Li, Tianqi
    Hussain, Sayed Bilal
    Shen, Guoling
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2020, 13 : 414 - 427
  • [36] Mangrove Extent and Change Mapping of Muaragembong from 1990 to 2020 using Google Earth Engine (GEE)
    Ditian
    Meganatha, R. D. Melinda
    Prasetyo, Widodo Eko
    Hartanto, Sanjaya
    Hartono, Rudi
    2021 IEEE OCEAN ENGINEERING TECHNOLOGY AND INNOVATION CONFERENCE: OCEAN OBSERVATION, TECHNOLOGY AND INNOVATION IN SUPPORT OF OCEAN DECADE OF SCIENCE (OETIC), 2021, : 35 - 38
  • [37] Optimizing hybrid models for canopy nitrogen mapping from Sentinel-2 in Google Earth Engine
    De Clerck, Emma
    Kovacs, David D.
    Berger, Katja
    Schlerf, Martin
    Verrelst, Jochem
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2024, 218 : 530 - 545
  • [38] A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series
    Yue, Linwei
    Li, Baoguang
    Zhu, Shuang
    Yuan, Qiangqiang
    Shen, Huanfeng
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2023, 16 (01) : 210 - 233
  • [39] High-accuracy continuous mapping of surface water dynamics using automatic update of training samples and temporal consistency modification based on Google Earth Engine: A case study from Huizhou, China
    Li, Kewei
    Xu, Erqi
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 179 : 66 - 80
  • [40] Accuracy analysis of terrain point cloud acquired by structure from motion using aerial photos
    Wei, Zhan-Yu
    Ramon, Arrowsmith
    He, Hong-Lin
    Gao, Wei
    Dizhen Dizhi, 2015, 37 (02): : 636 - 648