#Learn-GEE learning ML using GEE (Google Earth Engine) javascript API as a beginner
learning results: ( the repo contains more than what is presented here )
Part I: Programming and Remote Sensing Basics
Objective: To gain a fundamental understanding of remote sensing data and programming.
Activities:
Programming Basics: Learned the basics of programming, including defining variables, printing values, and other fundamental steps.
Displaying Remote Sensing Data: Acquired skills to display remote sensing data using software tools and basic programming scripts.
Earth Engine Data Catalog: Explored the extensive data catalog of Earth Engine, understanding the types and sources of data available.
Remote Sensing Vocabulary: Familiarized myself with the key vocabulary and concepts in remote sensing, which is essential for both novices and those starting with Earth Engine.
Outcome: Developed a strong foundation in programming and remote sensing, enabling me to efficiently handle remote sensing data and perform basic operations.
RGB composite of stable nighttime lights (2013, 2003, 1993)
Global Forest Change 2000–2020 tree cover loss (yellow–red) and 2000 tree cover (black-green)
3 NAIP color-IR composite over the San Francisco airport
Part II: Interpreting Images Objective: To understand and apply basic operations on individual satellite images. Activities: Manipulating Image Bands: Learned how to manipulate bands of remote sensing images to form indices. Image Classification: Applied various supervised and unsupervised classification techniques to categorize remote sensing images. Result Assessment and Adjustment: Gained skills in assessing classification results and making necessary adjustments to improve accuracy. Outcome: Enhanced my ability to interpret and analyze satellite images, leading to more accurate and meaningful insights from remote sensing data.
Thresholded water, forest, and non-forest image based on NDVI for Seattle, Washington,
CART classification
CART regression
Random forest classified image
K-means classification
Chart showing accuracy per number of random forest trees
Part III: Advanced Image Processing
Objective: To apply advanced image processing techniques to remote sensing data.
Activities:
Band Regression: Learned to find relationships between two or more bands using regression analysis.
Tasseled Cap and Principal Components Transformations: Applied linear combinations of bands to produce tasseled cap and principal components transformations.
Morphological Operations: Performed morphological operations on classified images to highlight or de-emphasize spatial characteristics.
Object-Based Image Analysis: Conducted object-based image analysis, grouping pixels into spectrally similar and spatially contiguous clusters.
Outcome: Acquired advanced image processing skills, enabling me to perform complex analyses and derive sophisticated insights from remote sensing data.
True-color Sentinel-2 image
Pixel-based unsupervised classification using four-class k-means unsupervised classification and bands from the visible spectrum
Pixel-based unsupervised classification using four-class k-means unsupervised classification and bands from outside of the visible spectrum
SNIC clusters