Big data - extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions .
Cloud computing - the practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or a personal computer.
Artificial intelligence - the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Machine learning - the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.
Source: Oxford Languages
The WRO will be making use of the Google Cloud Platform (GCP) for its data storage and analytics needs. Google's Earth Engine (EE) already contains many sources of satellite imagery in its database and various tools to analyse and process spatial and temporal data.
Other cloud platforms and resources:
IBM Environmental Intelligence Suite / Geospatial Analytics
Getting into big data analytics? Here are some recommendations from the team:
ML practical examples with code: https://www.analyticsvidhya.com/blog/category/machine-learning/?utm_source=blog_navbar&utm_medium=machine_learning_button
Open Geo Blog: https://mygeoblog.com/
Google Earth Engine for Water Resources Management: https://courses.spatialthoughts.com/gee-water-resources-management.html
Other cloud tools:
Natural language API - analyse unstructured text, for example, sentiment analysis from social media streams
Speech API - speech to text conversion
Vision API- read and analyse photographs and other images
Data catalog - metadata management services
BigTable - store unstructured data such as photographs