  Forecasting where and when new buildings will emerge is a rather unexplored
topic, but one that is very useful in many disciplines such as urban planning,
agriculture, resource management, and even autonomous flying. In the present
work, we present a method that accomplishes this task with a deep neural
network and a custom pretraining procedure. In Stage 1, a U-Net backbone is
pretrained within a Siamese network architecture that aims to solve a
(building) change detection task. In Stage 2, the backbone is repurposed to
forecast the emergence of new buildings based solely on one image acquired
before its construction. Furthermore, we also present a model that forecasts
the time range within which the change will occur. We validate our approach
using the SpaceNet7 dataset, which covers an area of 960 km^2 at 24 points in
time across two years. In our experiments, we found that our proposed
pretraining method consistently outperforms the traditional pretraining using
the ImageNet dataset. We also show that it is to some degree possible to
predict in advance when building changes will occur.
