The slide deck for this talk → https://docs.google.com/presentation/...
More about the 2023 Geo for Good Summit → https://g.co/earth/geoforgood23

❤ DESCRIPTION:
Geospatial AI is an application of AI, particularly machine learning to geospatial big data. Geospatial AI can be used for tasks such as change detection, image classification, and object detection. However, training these models requires large amounts of data, variety, and veracity. Discover how Collect Earth Online (an open source platform) enables users to visualize and organize geospatial data, and can be used to collect reference data for training GeoAI models. We'll also discuss the process of creating a sampling design, gathering samples, and using those samples in Earth Engine to train a classifier.

#remotesensing #EarthEngine #cloudcomputing #GeographicInformationSystem #GIS #ClimateAnalysis #EarthObservation #climatechange #sustainability #ai #machinelearning

TIMESTAMPS:
0:00 Introduction to Geospatial AI
1:21 Geospatial AI Overview
5:23 Introduction to Collect Earth Online
11:28 Tutorial: Using Collect Earth Online
13:10 Project Creation in Collect Earth
19:14 Data Analysis Techniques
22:35 Data Export to Google Earth Engine
23:20 Earth Engine Classifier Tutorial
25:20 Future of Earth Observation Data
28:15 Audience Q&A Session
29:45 Handling Large Geospatial Datasets
33:10 EO Data Interpretation Standards
35:32 Using Reference Data in Projects
37:52 Crowdsourcing in Earth Observation
41:10 Grading Standards in EO Analysis
43:28 Applying Machine Learning to EO Data
45:40 Ground Data Integration Methods
47:02 Next Steps in Geospatial Analysis
47:20 Final Questions and Wrapup


SPEAKERS
Gino Miceli, Google
David Saah, Spatial Informatics Group