  We propose a general model, Temporal Cluster Matching (TCM), for detecting
building changes in time series of remotely sensed imagery when footprint
labels are only available for a single point in time. The intuition behind the
model is that the relationship between spectral values inside and outside of
building's footprint will change when a building is constructed (or
demolished). For instance, in rural settings, the pre-construction area may
look similar to the surrounding environment until the building is constructed.
Similarly, in urban settings, the pre-construction areas will look different
from the surrounding environment until construction. We further propose a
heuristic method for selecting the parameters of our model which allows it to
be applied in novel settings without requiring data labeling efforts (to fit
the parameters). We apply our model over a dataset of poultry barns from
2016/2017 high-resolution aerial imagery in the Delmarva Peninsula and a
dataset of solar farms from a 2020 mosaic of Sentinel 2 imagery in India. Our
results show that our model performs as well when fit using the proposed
heuristic as it does when fit with labeled data, and further, that supervised
versions of our model perform the best among all the baselines we test against.
Finally, we show that our proposed approach can act as an effective data
augmentation strategy -- it enables researchers to augment existing structure
footprint labels along the time dimension and thus use imagery from multiple
points in time to train deep learning models. We show that this improves the
spatial generalization of such models when evaluated on the same change
detection task.
