Strive for ideals and persist in dreams

为理想奋斗，为梦想执着

Personal Profile（个人简介）

I am a Ph.D in Cartography and Geographic Information Engineering. At present, I am doing postdoctoral research in the State Key Laboratory of Geomatics and Remote Sensing Information Engineering, Wuhan University (forest carbon storage, LLM and AI Agent). I am engaged in research on remote sensing cloud computing, primarily focusing on the remote sensing ecosystem cloud computing of platforms such as Google Earth Engine, PIE-Engine, Planetary Computer, AI Earth, and CAS StarMap, which integrates multi-source remote sensing and machine learning. My research fields cover various disciplines including architecture, meteorology, agriculture, water conservancy, and more. To date, I have published over 10 academic papers, authored 2 monographs, obtained 2 patents, and secured 3 software copyrights. I have also participated in 5 major domestic and international projects. In 2022, I was ranked TOP 3 in the Cloud Computing Blog Star Awards, and in 2023, I was ranked TOP 13 in the CSDN Blog Star Awards. I am also a Cloud Share Expert and MVP at Huawei Cloud, as well as a Blog Expert at Alibaba Cloud Community and 51CTO. Currently, I have established in-depth collaborations with companies such as CAS StarMap, Aerospace Hongtu, and Alibaba DAMO Academy.

我是一名地图制图学与地理信息工程专业的博士，现从事遥感云计算相关工作。目前正在武汉大学测绘遥感信息工程国家重点实验室从事博士后研究（森林碳储量、LLM和AI Agent）。我的研究主要集中在Google Earth Engine、PIE-Engine、Planetary Computer、AI Earth以及中科星图等云平台的遥感生态云计算领域，通过融合多源遥感和机器学习技术，深入探索其在建筑、气象、农业、水利等多个专业领域的应用。至今，我已参与国内外5项重大项目，积累了丰富的实践经验。在学术与业界影响力方面，我于2022年荣获云计算领域博客之星TOP3称号，2023年又荣获CSDN博客之星TOP13殊荣。同时，我还被华为云授予云享专家及MVP称号，并成为阿里云社区和51CTO的博客专家博主。目前，我与中科星图股份有限公司、航天宏图股份有限公司以及阿里达摩院等多家知名企业和机构保持着深度的合作关系，共同推动遥感云计算技术的发展与应用。

Work Experience/工作经历

01-07-2018 - 01-07-2020

Employer: Guizhou Vocational and Technical college of water resources and hydropowe

工作单位：贵州水利水电职业技术学院

Post:Asisstant Professor

职位：助教

01-07-2024 - 31-12-2024

Employer: Htdrought company

工作单位：北京慧天卓特科技有限公司

Post: Remote sensing engineer

职位：技术负责人

11-02-2025 -

Employer: Zhiharmony company

工作单位：知鸿蒙科技有限公司

Post: Engineer

职位：技术负责人

07-05-2025 - now

Employer: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

工作单位：武汉大学，测绘遥感信息工程全国重点实验室

Post: PostDoc

职位：博士后

Education/教育背景

B.S.

01-09-2013 - 01-06-2015

University：Taiyuan University of Technology

学校：太原理工大学阳泉学院

M.S.

01-09-2015 - 01-06-2018

University：Guizhou University

学校：贵州大学

PH.D.

01-09-2020 - 01-06-2024

University：China University of Mining and Technology-Beijing

学校：中国矿业大学（北京）

Publication List/出版物列表

Journal Paper/期刊论文

Yan Xingguang, Li Jing, Andrew R. Smith et al. Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models[J]. International Journal of Digital Earth, 2023, 16(2): 4471-4491.

Yan Xingguang, Li Jing, Andrew R. Smith et al. Rapid Land Cover Classification Using a 36-Year Time Series of Multi-Source Remote Sensing Data[J]. Land, 2023, 12(12): 2149.Land 12.12 (2023): 2149.

Yan Xingguang, Li Jing, Yan Xiaoxiao et al. A Rapid Method for Stripe Chromatic Aberration Correction in Landsat Images[J]. Spectroscopy and Spectral Analysis, 2023, 43(11): 3483-3491.

Yan Xingguang, Li Jing, Yang Di, et al. A random forest algorithm for landsat image chromatic aberration restoration based on GEE cloud platform—a case study of yucatán peninsula, Mexico[J]. Remote Sensing, 2022, 14(20): 5154.

Li Jing; Li Shengcai; Guo Wei; Yan Xingguang; Zhang Rui. Ecological Environment Analysis of Shanxi Province and Coal Mining Areas Based on Modified Remote Sensing Ecological Index.[J]. Metal Mine, 2022,No.557(11):1-14.

Liu, Chang, Zhu, Fangfang, Yan, Xingguang*, Ma, Xiaoliang, Yang, Di and Smith, Andrew Robert. "Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023" Open Geosciences, vol. 17, no. 1, 2025, pp. 20250819. https://doi.org/10.1515/geo-2025-0819

Su, Yiting.; Li, Jing.; Wang, Dongchuan.; Yue, Jiaobao.; Yan, Xingguang. Spatio-Temporal Synergy between Urban Built-Up Areas and Poverty Transformation in Tibet. Sustainability 2022, 14, 8773.

Shao Jiahao;Li Jing;Yan Xingguang;Ma Tianyue;Zhang Rui .Analysis of Spatiotemporal Variation Characteristics and Driving Forces of　NPP in Shanxi Province from 2000 to 2020 Based on Geodetector.[J].Environmental Science:1-15.

Li, Jing;Yan Xingguang; Yan Xiaoxiao; Guo Wei.; Wang, Kewen; Qiao, Jian. Temporal and spatial variation characteristic of vegetation coverage in the Yellow River Basin based on GEE cloud platform.[J]. China Coal Society. 2021, 46, 1439-1450.

Deng shixiong, Yan Xingguang, Liu Jigeng, Zhou Yu, He Qingping.Image matching of UAV based on AKAZE algorithm in mountain area.[J].Mine surveying,2020,48(05) :105- 109.

Yan Xingguang, Wu Linna, Song Julan, Wang Dongdong, Liu Jigeng. Spatial and Temporal of Precipitation in Guizhou province from 1951 to 2013.[J].Bulletin of science and technology,2019,35(02):20-25.

Yan Xingguang, Wu Linna, Zhou Yong, Song Julan, Deng Shixiong. On the association of Co-Kriging interpolation method research based on GIS: A case study in Karst area of Guizhou Province[J].Journal of Yunnan university (natural science edition).2017,39(03):432-439.

Patant/专利

Li Jing, Yan Xingguang, Ma Tianyue, et al. A rapid repair method for color difference strip after mosaic of remote sensing images on cloud platform [P]. Beijing Municipality: CN202210718755.X, 2024-11-05.

Yan Xingguang, Li Jing, Ma Tianyue, et al. A method, device and equipment for forest aboveground biomass estimation based on multi-source remote sensing data [P]. Beijing Municipality: CN202410998362.8,2024-11-08.

Li Jing, Ma Tianyue, Huo Jiangrun, Yan Xingguang. A method for regional forest age estimation [P]. Beijing Municipality: CN202410042055.2,2024-11-12.

Soft Copyright/软件著作权

Landsat 5 NDVI (Normalized vegetation index ) image color difference repair software V1.0

Application software of annual land classification based on image spectral difference V1.0

Software for forest aboveground biomass estimation based on multi-source remote sensing and machine learning methods V1.0

Book/专著

《生态地理遥感云计算》

Remote sensing cloud computing:Ecology and Geography

[Introduction]

The book is divided into eight chapters, Implementation and account application from the Google Earth Engine code Editor Online Web, To the JavaScript base, To the analysis of GEE common functions and GEE cases, In plain language, Gradually explained the relevant operation technology of GEE remote sensing cloud computing, Including JavaScript EE objects, vector, image visualization, image, image upload and download, image to cloud, image time and boundary screening, index inversion, band operation, image mask, mosaic and cropping, vector and grid conversion, area and perimeter calculation, linear regression, correlation analysis, reducer statistics and screening, join connection, chart loading, runTask, and multiple case analysis. Each chapter of the book has not only the use of basic functions and the corresponding code equipment, but also the exploration of advanced functional knowledge points, which is an ideal book for learning Google Earth Engine technology.

Yan Xingguang, Ma Tianyue, Li Jing, et al.Remote sensing cloud computing:Ecology and Geography [M]:Beihang University Press.2015-01-15.

“揭秘生态地理奥秘，融合遥感云计算技术”大家期待依旧的新书终于来了-《生态地理遥感云计算》以同步在京东，当当发布，欢迎大家选购。

【内容简介】

本书共分为8章，书中从Google Earth Engine code Editor在线Web中的实现和账号申请，到JavaScript基础，再到GEE常用功能和GEE案例的分析，用通俗易懂的语言，渐进式讲解了GEE遥感云计算的相关操作技术，包括JavaScript中EE对象、矢量、影像、图像可视化、影像的上传和下载、影像去云、影像时间和边界筛选、指数反演、波段运算、影像掩膜、镶嵌和裁剪、矢量和栅格的转换、面积和周长的计算、线性回归、相关性分析、reducer统计和筛选、join连接、图表加载、runTask以及多个案例分析等内容。书的每章既有基础功能的使用和相应代码配备，也有高级功能知识点探究，是学习Google Earth Engine技术的理想书籍。

本书主要读者对象为测绘、生态、地理、环境和遥感等领域各层次技术及相关科研人员，以及高等院校相关专业在校师生等。

【同步视频】

此外本书的教学视频也会在我的个人知乎:（满天星），CSDN博客:(此星光明)，公众号:（生态云计算），B站:（此星光明云）等平台同步更新，欢迎大家关注。

Conference/会议

2021.10 National Doctoral Forum: Surveying and Mapping Science and Technology, Beijing, China

2024.03 Huawei MindSpore Conference, Beijing, China

2024.03 The 7th "Earth Space Big Data and Cloud Computing" Frontier Conference, Beijing, China

2024.07 Space Information Conference & Digital Earth Ecology Summit, Jiangsu, China

2025.04 第八届地球空间大数据与云计算研讨会, Beijing, China

2025.04 第二届空间信息技术及产业发展大会AIT2025, Chengdu, China

2025.07 第二届人工智能与遥感科学交叉论坛AIRS（Artificial Intelligence and Remote Sensing）2025, Kunming, China

Technical Expertise/技能

Software：ArcGIS, CASS, ENVI, SPSS

Cloud Computer platflorm: Google Earth Engine, GVE-Earth Brain, PIE-Engine, AI Earth, Planetary Computer, CODE-DE, AWS

Computer Language：Javascript, Python, R

Application software/应用软件

APP Links: https://bqt2000204051.users.earthengine.app/view/landsat-5

NDVI Image Restoration

This APP mainly irnplernents the banding probleingenerated in the image stitching process of Landsat 5seres lmages (1984-2012).The repalr method malmlyobtains the DN value and probability dislribution of ilsreference image through random forest to correct theimage chromatic aberratlon of the dlstrlbutlon to berepaired, so as to achieve unilorm chromatic aberration olthe stitched image, Concept Note: Studyarea is the studyarea you choose, target ls the study area to be restored,relerence is the study area you want to refer to forcorrecting the target.

APP Links: https://bqt2000204051.users.earthengine.app/view/landsat-5-ndwi-image-restoration

NDWI Image Restoration

This APP mainly implements the banding problem generated in the image stitching process of Landsat 5 series images (1984-2012).The repair method mainly obtains the DN value and probability distribution of its reference image through random forest to correct the image chromatic aberration of the distribution to be repaired, so as to achieve uniform chromatic aberration of the stitched image. Concept Note: Studyarea is the study area you choose, target is the study area to be restored, reference is the study area you want to refer to for correcting the target.

APP Links: https://bqt2000204051.users.earthengine.app/view/forest-agb-variable-importance-analysis

AGB Variable Importance Analysis

The role of this application is how to use multi-source remote sensing image bands with biomass separately for variable importance analysis

Note: Please ensure that the biomass band name in the multi-band image collection you are adding is 'BIO' before entering, i.e. all multi-band images in your study area need to have the corresponding sample site biomass band added to facilitate subsequent model training.

APP Links: https://bqt2000204051.users.earthengine.app/view/forest-agb-variables-correlation-analysis

AGB Variable Correlation Analysis

The role of this application is how to use multi-source remote sensing image bands with biomass separately for variable correlation analysis

The main purpose of the role of this APP is for the analysis of the correlation between sample plot biomass and various multi-source remote sensing variables.

APP Links: https://bqt2000204051.users.earthengine.app/view/forest-aboveground-biomass-prediction

AGB Predictions Model

The role of this application is how to use multi-source remote sensing imagery to participate in the prediction of biomass models

APP Links: https://bqt2000204051.users.earthengine.app/view/land-classification-of-landsat-imagery

Land Classification Worldwide

The program mainly selects the images of any year from 1984 to the present (landsat 5 / 7 / 8 / 9 SR) through the fixed sample points of land classification in one year, and performs land classification in different years according to the random forest algorithm.