[chmhsm]

Creating a 3d model out of 2d images requires computer vision to extract objects in the images and estimate their dimensions (including elevation). This will most likely require implementing an end-to-end deep learning model that's gonna need training, validation and test. Given the amount of data it'll have to deal with (100ks to millions of images) it'll need to load (high dimensional?) images in batch for them to get processed. This can still be done arguably on aws or Azure (or or...) with TensorFlow and HPC, but two things here, HPC bring a bit more overhead to the table, and a supercomputer could do better since none of the current cloud service providers have supercomputers that can compete especially in terms of cpu performance.

[acollins1331]

Theres no reason it needs a DL model. There's a lot of software that calculates tie points and creates point clouds from pictures, which is what they are almost certainly going to do here. DL to go from orthoimages to point cloud, if it is a thing, is probably still in the feasibility steps.

[salty_biscuits]

The steps are all fairly easily parallelizable until you get to a final large scale nonlinear least squares refinement step, and even then there are tricks to make the decomposition tractable. It usually just involves single images or pairs of images with no need for communication between processes until the last bit.