Abstract
Forecasting where and when new buildings will emerge is a rather unexplored niche topic, but relevant in disciplines such as urban planning, agriculture, resource management, and even autonomous flight. In this work, we present a method that accomplishes this task using satellite images and a custom neural network training procedure. In stage A, a DeepLapv3+ backbone is pretrained through a Siamese network architecture aimed at solving a building change detection task. In stage B, we transfer the backbone into a change forecasting model that relies solely on the initial input image. We also transfer the backbone into a forecasting model predicting the correct time range of the future change. For our experiments, we use the SpaceNet7 dataset with 960 km2 spatial extension and 24 monthly frames. We found that our training strategy consistently outperforms the traditional pretraining on the ImageNet dataset. Especially with longer forecasting ranges of 24 months, we observe F1 scores of 24% instead of 16%. Furthermore, we found that our method performed well in forecasting the times of future building constructions. Hereby, the strengths of our custom pretraining become especially apparent when we increase the difficulty of the task by predicting finer time windows.
Abstract (translated)
URL
https://arxiv.org/abs/2204.12875
