About Sessions, Resources
Day 1: Higher-Ed Track
Creating Earth Engine Classroom Resources
Deep Dive Examples
- Forest vegetation condition in India's Tiger Reserves, Pradeep Koulgi
- Applications of Google Earth Engine, Raj Bhagat
- Earth Engine in the SERVIR program, Dave Saah
- My Journey with Google Earth Engine, Ankur Shringi
- Kerala Floods Application, MD Madhusudan
- Open Water Data, Craig Dsouza
Earth Engine in the Classroom Panel
- Earth Engine and ICAR, Prof P D Sreekanth, ICAR
- Earth Engine in Teaching Geoinformatics Curriculum, Dr. Neeti, TERI University
- Google Earth Engine Japan Communities, Dr. Taichi Furuhashi
Day 2-3: Main Track
Becoming a better EE programmer
Presenter: Ujaval Gandhi
Description: This session will give participants a better understanding of client vs. server objects and when to use them. We will go through some common issues that beginners face when using the Earth Engine API (such as exporting all images in a collection) and best practices on how to implement them. We recommend not using for loops or if/else conditions when you write EE code, but it can be hard to structure your algorithms to avoid those. I will show some functional programming techniques that can help you implement your algorithm better in Earth Engine.
Content: Slides
EE104: Intro to Python API
Presenter: M D Madhusudan
Description: This session will introduce using tools from the open source Jupyter project (http://jupyter.org) and Python visualization libraries to develop algorithms with the Earth Engine Python API.
Prerequisites: 1) Earth Engine 101 or prior experience in using Earth Engine. 2) A GitHub account (https://github.com/join).
Content: Slides
Image segmentation & object based methods for image analysis
Presenter: Sai Cheemlapatti
Description:
This class is a walkthrough of how to, in Earth Engine:
- Create a per-pixel classification of an image from a source-of-truth.
- Segment an image into a set of homogenous clusters.
- Create a cluster-based classification from a source-of-truth.
- Derive useful properties from clusters (e.g., std. dev, area, perimeter, height, width, etc.).
Prerequisites: EE101 or equivalent
Content: Slides
Intro to EE UI & Apps
Presenter: Pradeep Koulgi
Description: When you finish your analysis on EE, you might want to share it with others and give them ways to easily interact with it, via interactive maps, buttons, sliders, etc. Understand how to build such user interactivity into your analysis scripts right from your Code Editor, using EE's new tools for UI and Apps.
Prerequisites: EE 101 or equivalent
Content: Slides
Classifiers and Classification
Presenter: M D Madhusudan
Description: Understand the steps for creating, using and validating a classification in Earth Engine and the basic limitations of the classifier system (table size, tileScale, etc). Learn how to do parameter optimization. Learn how to do inter-annual compositing and investigate the effects of multi-temporal feature vectors and multi-sensor fusion on classifier accuracy.
Prerequisites: EE101 or equivalent
Content: Slides
Change Detection from Composites
Presenter: Craig Dsouza
Description: A hands-on course in change detection of forest fires from Sentinel/Landsat composites. Methods include spectral index differencing and spectral distance thresholds. Participants will also discuss other use cases including change detection for landslides, sand mining, etc.
Prerequisites: EE101 or equivalent
Content: Slides
Tensorflow in Earth Engine
Presenter: Chris Brown
Description: Introduction to coding with the TensorFlow Estimator API. This course is an end-to-end walkthrough of generating training and validation data in Earth Engine, exporting to the TFRecord format, building a simple DNN model in TensorFlow, training and evaluation with the EE data, classification of an image as a TFRecord file, and import back to Earth Engine.
Prerequisites: Python programming. Google Colab is installed as a Drive service.
Content: Slides
Vector Data Visualization & Analysis
Presenter: Ujaval Gandhi
Description: This session will start with basics of importing, visualizing and exporting vector data in Earth Engine. It will also cover some common tasks that require using vector data in Earth Engine - such as exporting a time series over multiple regions - and show how you can implement those.
Prerequisites:
Content: Slides
Theme breakouts:
Plenary Sessions
Earth Engine India Community
Speakers: Craig DSouza, M D Madhusudan, Pradeep Koulgi (Cameo from Devaja)
Hear from the community on how to be a trainer, create SIGs(special interest groups) within your org/uni, organize meetups. Open floor discussion on your learning curves with Earth Engine, what areas can the community provide support in.
Lightning Talks
- Ankur Shringi - Vegetation dynamics in Mudumalai Tiger Reserve
- Guneet Mutreja - Urban Boom - a spacetime complexity
- Karun Choudhary - Crop damage assessment using earth engine
- Giriraj Amarnath - Managing climatic risks using GEE tools
- KISHORE KOWTHAM - Kerala floods: A cloud based automated flood mapping using GEE
- Rajat Shinde - Analysis of ML Classifiers for Satellite Image Classification in GEE
- Nabin Kumar Pradhan - GEE platform for monitoring Community Managed Forest
- Vaibhav raj - Time-series analysis and Interpretation of gravitational anomalies
- Abhishek Potnis - Flood Mapping using GEE: Towards Enhanced Situational Awareness