A big part is that training image detection is incredibly easy today. YOLO is a great network with reasonably intuitive tooling. Anyone with a set of images can start labeling them, copy-paste a couple lines into a jupyter notebook and make a decent YOLO finetune.

The difficulty is in the training data, both acquiring it and labeling it. Hence why the readme of WALDO alludes so much to their semi-synthetic data. That's also why this commercial project is happy to give out the models, but doesn't publish their data pipeline.

If you have about 100 satellite images each of T-80s with and without Zs, and a couple other satellite images of other tanks and of landscapes without any tanks you can train a T-80 detecting model in a couple hours. And then spend a couple days in a rabbit hole where you figure out that because in your training set only images with tanks had smoke clouds the model now thinks that smoke clouds are linked to tanks, and you end up making larger and larger data sets with tanks and non-tanks from all angles.