iLit4GEE-AI is the accompany interactive web app for the review paper (invited feature paper), titled "Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review", which was published in the special issue (The Future of Remote Sensing: Harnessing the Data Revolution) of the open access journal Remote Sensing.

This is also one of the three case studies maps for the paper title "dciWebMapper: A data-driven and coordinated-view-enabled interactive web mapping framework for visualizing and sensing high-dimensional geospatial (big) data", the other two maps are iWURanking and iTRELISmap.

Web app team:

Web map advisor (data structure design, web design and web programming): Dr. Liping Yang (lipingyang@unm.edu)

Web map design and developer: Sarigai Sarigai (sarigaigis@gmail.com)

Data provider (including data structure design): Joshua Driscol (joshuadr@unm.edu)

Acknowledgements:

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D3

Leaflet

Web app of review paper "Intelligent Water Information Extraction Using AI"

Our web app visitor map

0:00 - 1:02: Introduction & Motivation
1:03 - 1:50: Target users
1:51 - 6:29: Brief demo (major components introduction)
6:30 - 9:18: Brief demo (examples)
9:18 - 9:39: Acknowledgements & Contact Info

GeoAIR lab advocates open source and supports open science. Any one can use/modify our web app code, commercial or personal use, BUT you are REQUIRED to give credits explicitly to GeoAIR lab.The iLit4GEE-AI web app is under the MIT license.

Are you the authors of, or are you aware of, any published paper(s) in GEE + AI that are not included in this web app? If so, please submit the data for the paper through this data entry form. Through this form, GEE+ AI scholars/practitioners can submit the data for recent publications and/or papers published in the past that our lab missed in our review (https://doi.org/10.3390/rs14143253). GeoAIR lab members will review the data submitted to our online data entry form and determine whether it should be included into our web application. Our ultimate goal for this web app is to build an up-to-date, searchable literature list for the GEE and AI community in order to advance science and to support researchers to tackle challenging problems such as global warming. Thank you for helping build an open-source data catalog for all GEE+AI literature!

If you need a detailed explanation of data fields while you are working on and submitting this online form for your GEE and AI paper, please check our data explanation web page. If you still have questions, feel free to contact the director of the GeoAIR Lab Dr. Liping Yang (lipingyang@unm.edu).