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学生对 加州大学戴维斯分校 提供的 Imagery, Automation, and Applications 的评价和反馈

4.9
399 个评分
76 个审阅

课程概述

Welcome to the last course of the specialization (unless your continuing on to the capstone project, of course!). Using the knowledge you’ve learned about ArcGIS, complete technical tasks such raster calculations and suitability analysis. In this class you will become comfortable with spatial analysis and applications within GIS during four week-long modules: Week 1: You'll learn all about remotely sensed and satellite imagery, and be introduced to the electromagnetic spectrum. At the end of this week, you'll be able to find and download satellite imagery online and use it for two common types of analysis: NDVI and trained classification. Week 2: You'll learn how to use ModelBuilder to create large processing workflows that use parameters, preconditions, variables, and a new set of tools. We'll also explore a few topics that we don't really have time to discuss in detail, but might whet your appetite for future learning in other avenues: geocoding, time-enabled data, spatial statistics, and ArcGIS Pro. Week 3: In week three, we'll make and use digital elevation models using some new, specific tools such as the cut fill tool, hillshades, viewsheds and more. We'll also go through a few common algorithms including a very important one: the suitability analysis. Week 4: We'll begin the final week by talking about a few spatial analyst tools we haven't yet touched on in the specialization: Region Group to make our own zones, Focal Statistics to smooth a hillshade, Reclassify to change values, and Point Density to create a density surface. Finally, we'll wrap up by talking about a few more things that you might want to explore more as you start working on learning about GIS topics on your own. Take Geospatial and Environmental Analysis as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first, second, and third courses in this specialization, "Fundamentals of GIS," "GIS Data Formats, Design, and Quality", and "Geospatial and Environmental Analysis," respectively, before taking this course. By completing the fourth class you will gain the skills needed to succeed in the Specialization capstone....

热门审阅

SM

Jul 17, 2017

The course contents are up to date and equip the learner to face the real world of Remote sensing and GIS. I encourage those who still hesitate to get enrolled to make their decision soon.....

LH

Feb 19, 2019

Si quieres aprender acerca de detección remota, este es el curso indicado. He aprendido muchísimo acerca de imagen y creación de modelos para correr procesos en SIG

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51 - Imagery, Automation, and Applications 的 74 个评论(共 74 个)

创建者 Leydi N R M

Feb 28, 2017

Excellent course

创建者 Abdulkadir D R

May 15, 2017

very good and well prepared course

创建者 karmvir s r

Apr 23, 2017

Best of the specialization!!!!!! Amazing instructor, mentors and course material!!!

创建者 MA X

Oct 03, 2016

Very Helpful.

创建者 Mark H

Apr 28, 2017

Great course, actually quite difficult, and good to get it finished and realise I've actually learnt a lot from having to do the assignment.

创建者 Jesús A A E

May 16, 2017

Outstanding. A must for any remote sensing analyst.

创建者 Hisham T A H K

Sep 16, 2016

I Love every thing about GIS & Coursera

创建者 Shwahibu M

Jul 17, 2017

The course contents are up to date and equip the learner to face the real world of Remote sensing and GIS. I encourage those who still hesitate to get enrolled to make their decision soon.....

创建者 Mohamed H

Apr 09, 2018

Thank you for this powerful content

创建者 Kevin B

Feb 17, 2017

great course, seems to provide a bunch of real world applications!

创建者 iliasmax

Sep 16, 2017

Great course. It offers you advance skills (model builder creation) so that you can accomplish your GIS work quicker and methodically.

创建者 Kareem_Essayyed_Ali

Oct 28, 2017

Here you'ill start feel that you are control

创建者 Benjamin S

Jun 19, 2018

Like the other classes in this specialization, this class does an excellent job of explaining a range of concepts and methods in ArcGIS.

创建者 Aurora H

Sep 19, 2016

Very helpful. I learned a lot!

创建者 Sanaullah K

Nov 01, 2018

Great

创建者 Amira E T

Jul 14, 2018

Thank you for all your efforts and the amazing informative course

创建者 Donny B

Jun 03, 2019

Good course!

创建者 Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

创建者 Osmar C T

Jul 14, 2019

It is interesting

创建者 Ricardo J F

Jul 24, 2019

So much to learn....this course was great, the focus is not for everyone involved in GIS, but makes you wonder how many things can be improved in your own workflow

创建者 Jayanta B

Aug 01, 2019

Thanks for the opportunity. I have learned a lot of this from fundamental of gis to this imagery, automation and application course. thanks again.

创建者 Henry M M

Mar 01, 2017

Good

创建者 Phoukhong P

Jun 30, 2019

I like it very much. The web interface for learning are upgraded for better navigation and view!

创建者 April H

Dec 26, 2017

This course contains a good amount of information for intermediate GIS users, but it repeats too much material from the previous courses and the presentation of the material could sometimes be organized better. The remote sensing section is actually information-dense and overwhelming, and I'm disappointed that spatial statistics wasn't covered in more detail. Overall, I think the people who would get the most out of this class are those who are interested specifically in learning basic information about remote sensing and/or automation in ArcGIS.

As a sidenote, while the instructor does a very good job of simplifying concepts that not all students may have experience with, he for some reason feels the need to reassure his audience that he isn't going to delve into the difficulties of math. I think he should mention the bounds of the class without implying that math is prohibitively difficult; people only think math is hard because others make it seem so, and we shouldn't discourage people from learning more.