Digital Surface Model $($DSM$)$ can be obtained using different sensors and techniques. Independent of how DSM is constructed, it is a $2-$dimensional array holding floating values for each cell which represents the height for the location of that cell. For example, height value is zero in sea$-$level and $8848$ meters in top of mountain Everest. So$,$ you can think of DSM as a single$-$channel image with $32$ bits floating data type pixels. Note that pre$-$trained models $($i$.$e$.,$ AlexNet, VGG$,$ GoogLeNet, ResNet, DenseNet$)$ generally accept RGB images which have $3$ channels that are generally normalized between $0$ and $1($from $0$ and $255).$ Given this information solve one of the below problems $($solve only one$)$:

Develop a classifier which will classify a $6464$ DSM patch with pixel resolution with $0\mathrm{.}5$ meters $(32$ meters $32$ meters area$)$ as unknown, terrain $($only terrain$),$ building $($has building but can contain terrain as well$),$ and vegetation $($has vegetation but can contain terrain as well$).$

Given DSM data $($Figure $1$b bottom$)$ you want to generate orthophoto $($raster$)$ data $($Figure $1$b top$).$

Explain why you choose $(1)$ or $(2).$ Assume there exists no training data or very limited training data. Draw your architecture and explain it $($including its input and output$).$ What are your assumptions? What are the limitations and how do you overcome them $($for training and$/$or inference$)?$ How will you train it $($give details such ac antimizor Incal? Hnus will un$$ mako an inforoneo ucino it$?$

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Figure $1.$ Illustration of an DSM

Figure $2.3$D scene of the Drone$-$derived Digital Surface Model $($DSM$)$ and orthophoto.