Machine Learning and Landsat Applications

The video discusses the Landsat mission's role in Earth observation and the integration of artificial intelligence, particularly machine learning, to enhance data analysis. It highlights the use of AI in cloud detection, land-use change mapping in Belize, and wildfire severity prediction in Australia. The collaboration between Landsat data and AI has led to significant advances in various scientific fields, offering insights into climate change, urban planning, and more. The video emphasizes the potential for future applications as satellite data becomes more accessible and AI techniques improve.

What year marked the launch of the first Landsat satellite, initiating a new era of Earth observation?

• 1990

• 1980

• 1972

• 1969

How much data do Landsats 8 and 9 collect each day?

• 1 TB

• 5 TB

• 500 GB

• 2 TB

Which AI tool is particularly useful for analyzing Landsat data?

• Machine Learning

• Deep Learning

• Natural Language Processing

• Robotics

What accuracy rate did the neural network achieve in detecting clouds in satellite imagery?

• 90.5%

• 99.9%

• 97.1%

• 85.3%

Which machine learning tool was used to map land-use changes in Belize?

• K-Means Clustering

• Neural Network

• Random Forest Classifier

• Support Vector Machine

What was the main finding regarding tropical forests in Belize?

• They are expanding rapidly.

• They are stable.

• They are unaffected by agriculture.

• They are vulnerable to deforestation.

What type of machine learning algorithm was used to analyze wildfire severity in Australia?

• Unsupervised Learning

• Supervised Classifier

• Reinforcement Learning

• Deep Learning

What period does the 16-year history of wildfire severity cover?

• 2010-2026

• 1990-2006

• 2000-2016

• 2005-2020

Which field has NOT been mentioned as benefiting from the fusion of Landsat data and machine learning?

• Urban Planning

• Forestry

• Agriculture

• Medicine

What is the potential benefit of combining Landsat data with machine learning for future applications?

• Decreased data accuracy

• Reduced data availability

• Limited application fields

• Enhanced decision-making