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CNU-MSU聯合“遙感大數據與人工智能”系列課程
2019-05-09

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浙江快乐彩咋玩的 www.umgdu.com   CNU-MSU聯合“遙感大數據與人工智能”系列課程

 ?。ㄊ奔?月27日-6月21日)

  

  首都師范大學(Capital Normal University, CNU)與美國密歇根州立大學(Michigan State University, MSU)2017年簽訂了校級合作協議,在校級合作協議的框架下2019年成立了首都師范大學地理環境研究與教育中心。為了豐富學生的國際學習經驗,促進與來自不同國家和大學的學生之間的交流與合作,首都師范大學與美國密歇根州立大學的教授學者將于5月27日-6月21日在首都師范大學開設與“遙感大數據與人工智能”相關的全英文課程。

  

  課程分兩個時段

  A時段(5月27日-6月8日):地理空間研究設計、分析和實施

  B時段(6月10日-6月21日):地理空間研究設計、實施和交流

  允許學生自由選擇兩個時段的課程, A時段課程主要側重地理空間技術和技能培養,B時段課程主要側重研究設計和科學寫作。學生主要來自MSU、CNU以及其他高?;蜓芯吭核?,為確保與學生有足夠的交流, A時段課程的學生總數不超過30人,B時段課程的學生總數不超過15人。

  全英文授課,參加A時段課程的學生須精通英語,可無障礙交流和深入討論,有遙感、地理空間分析、或草地生態學習興趣。參加B時段課程的學生須精通英語,可無障礙交流,可閱讀和討論專業文獻,可英文寫作,并且有遙感、地理空間分析、或草地生態專業知識。

  

  課程英文簡介

  

  Segment A: Geospatial Research Design, Analysis, and Implementation

  Summer 2019 (May 27 – June 8)

  

  https://en.wikipedia.org/wiki/Image_analysis#/media/File:Object_based_image_analysis.jpg

  Instructors

  Ashton Shortridge, Raechel Portelli, & Jiaguo Qi. All are professors at Michigan State University.

  

  Course Objectives

  This course is an intensive two-week immersion in geospatial analysis of remote sensing data. The main objective of this course is to develop analytical processes for remotely sensed data to address fundamental and applied questions about grasslands ecology, modeling, or monitoring. Students will develop a machine learning workflow that can be implemented with remote sensing data, and they will develop a written description and justification for using these methods.

  

  Student Prerequisites

  This course is conducted in English. You must be proficient enough in English to follow verbal instructions and contribute to discussion. Students should also have a research focus and interest in remote sensing, geospatial analysis, and/or grassland ecology.

  

  Course Content and Hour Allocation

  Course is an intensive introduction to remote sensing and machine learning concepts and analyses. Topics are presented in the context of remote sensing use for grassland ecology applications. Remote sensing topics covered include geographic object-based image analysis (GEOBIA), implementation of GEOBIA through R statistical program, feature space reduction, and accuracy. Machine learning (ML) topics will include a general introduction to the topic, comparison of different types of ML techniques, and implementation in the R statistical program. These topics will be reinforced through participation in a series of instructor-led tutorials.

  

  This course includes 40 hours of teaching in the classroom, 20 hours of directed computer lab work, 8 hours of urban field data collection, and 8 hours of project time (76 contact hours).

  

  Grade Allocation

  30% Annotated bibliography

  30% Workflow write up

  20% Team Peer Assessment

  20% Participation

  

  Schedule

  Week 1 (May 27 – June 1): Geospatial Methods and Field Sampling

  Lecture and discussion. Topics include: GEOBIA concepts, statistical classification techniques, image pre-processing, classification of images using R and/or other geoprocessing software.

  Student assignments: Import remote sensing data into R. Read pertinent articles. Perform feature space reduction. Perform image classification. Perform post-classification accuracy assessment.

  

  Week 2 (June 3 – June 8): Geospatial Machine Learning

  Lecture and discussion. Topics include: statistical inference, machine learning, specific machine learning approaches, caveats of machine learning implementation, and its potential in the Data Science era.

  Student assignments: Perform a variety of machine learning analyses within R. Read pertinent articles. Perform post-classification accuracy assessment. Write a summarization of the techniques applied and create a plan for analytic methods for remote sensing data.

  

  Segment B: Geospatial Research Design, Implementation, and Communication

  Summer, 2019 (June 10 – June 21)

  

  https://commons.wikimedia.org/wiki/File:Bayanbulak_grassland.jpg

  Instructors

  Ashton Shortridge, Raechel Portelli, & Jiaguo Qi. All are professors at Michigan State University.

  

  Course Objectives

  This course is an intensive two-week immersion in geospatial research design, implementation, and communication. At its core is a field trip to Inner Mongolia to address fundamental and applied questions about grasslands ecology, modeling, monitoring, or assessments, in the broad context of climate change and human activities. Students will develop and deliver a scientific presentation and work with their research team to determine the next steps.

  

  Student Prerequisites

  This course is conducted in English. You must be proficient enough in English to read and discuss academic articles, to follow and contribute to discussion, and to write. Students should also have a research focus and interest in remote sensing, geospatial analysis, and/or grassland ecology.

  

  Course Content & Hour Allocation

  Content consists of a set of interconnected topics. Some are writing and presentation related: the research process and the role of communication and writing; literature review, narrative, peer-review; targeting an audience; figures that tell a story; research questions; discussing limitations of the research; and ethics in communication. Others are research design focused, with an emphasis on geospatial approaches to environmental remote sensing: sampling; sensor issues; designing a field campaign; data collection and storage. Other processing and analysis aspects will not be covered in this course, but may be essential for successful completion of the project.

  

  This course includes 26 hours of teaching in the classroom, 28 hours of directed discussion and research planning, three days (24 hours) of guided research on a field trip to Inner Mongolia, and 6 hours of presentations and interaction (84 contact hours).

  

  Grade Allocation

  20% Annotated bibliography

  10% Field Journal

  30% Presentation

  20% Team Peer Assessment 20% Participation

  

  

  Schedule

  Week 1 (June 10 – June 16): Research Design and Implementation

  Lecture and discussion. Topics include: scientific research and communication, identifying research objectives, spatial sampling theory, planning field data collection Field trip. Multi-day field trip to Inner Mongolia. Data collection, including field and remote sensing.

  Student assignments: Join a research team. Select a topic for the project. Read and summarize relevant articles. Prepare written responses on them. Maintain a journal during the field trip.

  

  Week 2 (June 17 – June 21): Analysis and Scientific Communication

  Lecture and discussion. Topics include: scientific paper framework and targeting, plagiarism, identifying the research question(s) and hypotheses, results vs. discussion, presentations vs. papers, visualization.

  Student assignments: Prepare a detailed outline of the joint presentation. Review an outline by another team. Obtain additional data (e.g., Landsat or other imagery) for the project. Conduct initial processing and analysis. Develop presentation slides. Construct good graphics and text. Review a presentation draft by another team. Deliver presentation. Review all presentations. Write review of the course. Develop plan to continue research with the team.

  

  聯合教學團隊簡介

  Dr. Ashton Shortridge, Professor in Geography, Environment and Spatial Sciences

  Dr. Raechel Portelli, Professor in Geography, Environment and Spatial Sciences

  Dr. Jiaguo Qi, Professor in Geography, Environment and Spatial Sciences, Center for Global Change, Environmental Science and Policy Program and Office of China Programs.

  張愛武,博士,教授,資源環境與旅游學院,主要從事遙感載荷與人工智能、浮空探測與環境感知、草地遙感監測等方向研究

  朱琳,博士,教授,資源環境與旅游學院,主要從事環境遙感研究

  王艷慧,博士,教授,資源環境與旅游學院,主要從事GIS方法與應用研究

  鄧磊,博士,教授,資源環境與旅游學院,主要從事無人機遙感與攝影測量研究

  王濤,博士,副教授,資源環境與旅游學院,主要從事空間數據庫與城市信息模擬研究

  柯櫻海,博士,副研究員,資源環境與旅游學院,主要從事資源環境遙感應用研究

  謝東海,博士,副教授,資源環境與旅游學院,主要從事攝影測量與深度學習研究

  段福洲,博士,副教授,資源環境與旅游學院,主要從事航空遙感數據采集技術研究

  胡卓瑋,博士,副教授,資源環境與旅游學院,主要從事環境遙感研究

  劉曉萌,高級實驗師,資源環境與旅游學院,主要從事環境遙感研究

  田金炎,博士,資源環境與旅游學院,主要從事植被遙感研究

  

  報名參加

  填寫報名信息表,并在2019年5月20日前發至郵箱[email protected],我們會在5月22日回復郵件,請及時查看。

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  聯系人:劉曉萌、孟佳、侯焱([email protected]

  聯系電話:68907429、68903262

  報名郵箱:[email protected]

  

  

  首都師范大學資源環境與旅游學院

  首都師范大學地球空間信息科學與技術國際化示范學院

  首都師范大學地理環境研究與教育中心