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
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);
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.
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!
