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Question 5. Mapping fire activity is an important problem for supporting climate and carbon cycle studies as well as forest management. Automated approaches to address this problem use reflectance data collected from satellites orbiting the Earth to learn a classifier that can differentiate burnt areas on the ground from the unburned ones. The algorithms output a probability of fire activity for each pixel on the day the satellite imagery was taken. The classification algorithm needs to be trained to recognize the signatures of burned pixels (and how they are different from non-burned ones). Typically, there is a huge imbalance in the number of fire pixels ($<0.1\%$ of the total area) and non-fire pixels in any given region. Keeping this in mind, answer the following (i) What would happen if one were to randomly sample some locations (pixels) for training the classifier ignoring the presence/absence of fire at the location during Sampling? (ii) How would stratified sampling help here?

          Question 5.
Mapping fire activity is an important problem for supporting climate and carbon cycle studies as
well as forest management. Automated approaches to address this problem use reflectance data
collected from satellites orbiting the Earth to learn a classifier that can differentiate burnt areas
on the ground from the unburned ones. The algorithms output a probability of fire activity for
each pixel on the day the satellite
imagery was taken.
The classification algorithm needs to be trained to recognize the signatures of burned pixels (and
how they are different from non-burned ones). Typically, there is a huge imbalance in the
number of fire pixels ($<0.1\%$ of the total area) and non-fire pixels in any given region. Keeping
this in mind, answer the following
(i) What would happen if one were to randomly sample some locations (pixels) for training the
classifier ignoring the presence/absence of fire at the location during Sampling?
(ii) How would stratified sampling help here?

Question 5.
Mapping fire activity is an important problem for supporting climate and carbon cycle studies as
well as forest management. Automated approaches to address this problem use reflectance data
collected from satellites orbiting the Earth to learn a classifier that can differentiate burnt areas
on the ground from the unburned ones. The algorithms output a probability of fire activity for
each pixel on the day the satellite
imagery was taken.
The classification algorithm needs to be trained to recognize the signatures of burned pixels (and
how they are different from non-burned ones). Typically, there is a huge imbalance in the
number of fire pixels (<0.1% of the total area) and non-fire pixels in any given region. Keeping
this in mind, answer the following
(i) What would happen if one were to randomly sample some locations (pixels) for training the
classifier ignoring the presence/absence of fire at the location during Sampling?
(ii) How would stratified sampling help here?

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