GEE Juli 2023.pptx
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This document provides an introduction to Google Earth Engine (GEE), including what GEE is, why it is useful, the types of data available through GEE, how to get started with GEE, and examples of using GEE and JavaScript for geospatial analysis and machine learning. It demonstrates how to access petabytes of satellite imagery through GEE, use the JavaScript API to perform tasks like supervised classification of Landsat 8 imagery over Java, Indonesia, and export processed data. The document also discusses concepts like understanding big data in GEE and using GEE to analyze trends over multiple years.
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GEE Juli 2023.pptx
- 1. What is GEE?
- 2. Why GEE?
- 3. GLOBAL COMPOSITE Can your software do this? WORLD-WIDE LANDSAT 8 TRUE COLOR COMPOSITE WITH <40% CLOUD COVER https://code.earthengine.google.com/98da8a9f20115cc63f7c7fae770bcaba
- 4. 25 PETABYTE LANDSAT 1 – 8 MODIS SENTINEL 1 – 2 LEVEL 3 & SO ON IMAGERY FROM START – NOW https://developers.google.com/earth-engine/datasets
- 6. Interface
- 8. 1st Lesson - JS print command: print ('Kita Belajar GEE'); Using variable: var belajar = 'kita sedang ikut pelatihan'; var angka = 42; print ('belajar'); print ('angka yang ditulis adalah :' , angka); Comment tag : var belajar = 'kita sedang ikut pelatihan'; //print ('belajar');
- 9. 1st Lesson - JS Comment tag : /* var belajar = 'kita sedang ikut pelatihan'; print ('belajar'); */ Matrix: var MatriksAngka = [0, 1, 9, 3, 2, 5]; var MatriksHuruf = ['a', 'k', 'r', 'a', 'm'] print('Cetak Matriks Angka:', MatriksAngka);
- 10. 1st Lesson - JS Using Object: var akram = { kabupaten : 'Sleman', nomor : 628999792614, saudara : ['raras', 'ansita', 'anindya', 'anditya'] }; print ('plis liatin data diri akram:' , akram); print ('akram orang yang berasal dari:' , akram['kabupaten']); print ('berapa nomornya?', akram.nomor); print ('siapa saja saudaranya?', akram.saudara);
- 11. 1st Lesson - JS Using Function: var panggil= function (hai){ return hai + ' kalo kamu?'; }; var eksekusi = panggil('baik!'); print('apa kabarmu?', eksekusi);
- 16. Understanding Classification lawtomated.com/supervised-vs-unsupervised-learning-which-is-better/
- 17. Yes! We're going to Learn Maching Learning Learning by Doing Learning by Project
- 18. 1st of All Imagery Selection: Imagery type (ground resolution, revisit time, & band) Location (availability & needs) Time (availability & needs) Cloud cover (the lower the better) Also, decide the algorithm first.
- 20. Supervised Classification https://s.id/DIYProv Create Landsat 8 Collection over Jogja
- 21. Supervised Classification Gather the samples to a collection
- 22. Supervised Classification Landsat 8 Band Composites List
- 23. Supervised Classification Train the machine with CART and show the result Ghasemian, Ebrahim & Tavassoly, M K. (2021)
- 26. Yeay! We Did it!
- 27. Exporting Data
- 28. Any Questions?
- 29. Yes! We're going to Learn Big Data Computing Learning by Doing Learning by Project
- 30. Understanding Big Data Landsat 1 - 8 Revisit time : 16 Day 1 Year = approx. 22 imagery 1 raw imagery scene (11 Bands) = approx. 2 GB From 1984 – 2020 = 22 imagery x 2 GB x 37 year = 1628 GB Just in a specific Location!
- 31. Understanding GEE Chart Prepare at least 2 to 3 things : What, Where, How
- 32. 10-Year Precipitation Prepare at least 2 to 3 things : What, Where, How
- 35. Any Questions?