2020-09-22, 09:15–09:35, Room 1
With the boom of data science comes a renewed interest in geospatial, one of the most concrete and visual solutions for different problems faced by organizations of different sizes and motivations. This is seen in increasing number of product offerings for geospatial products, along with full-blown conferences dedicated to spatial data science. With new technologies and frameworks now capable of challenging tasks such as processing and storing petabytes of data and increased availability of satellite imagery, there is an even greater need for the skills to analyze and extract insights from this potentially useful data. Currently, Python enjoys a continued rise in popularity among data scientists for its quick to start, modular and generally applicable nature. This talk aims to make data scientists interested in including satellite imagery analysis in their workflow, to be immediately productive in using GEE.
The talk will start with a case for Google Earth Engine in Python in the context of renewed interest in geospatial with popularity of Python. This part will also include an introduction to GEE, details on how it was developed, the community that uses it, the team that maintains it, its capabilities such as its dataset inventory, and limits of use.
Finally, the last part of the talk will focus on other analysis compatible with the tool, different ways to contribute, and avenues to seek help for very specific errors. The last part will also include how our company uses GEE in our work.