A simple, strong baseline for building damage detection on the xBD dataset

We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away components. This way, we obtain a much simpler method, while retaining adequate performance. We expect the simplified solution to be more widely and easily applicable. This expectation is based on the reduced complexity, as well as the fact that we choose hyperparameters based on simple heuristics, that transfer to other datasets. We then re-arrange the xView2 dataset splits such that the test locations are not seen during training, contrary to the competition setup. In this setting, we find that both the complex and the simplified model fail to generalize to unseen locations. Analyzing the dataset indicates that this failure to generalize is not only a model-based problem, but that the difficulty might also be influenced by the unequal class distributions between events. Code, including the baseline model, is available under https://github.com/PaulBorneP/Xview2_Strong_Baseline

Contrastive pretraining for semantic segmentation is robust to noisy positive pairs

Domain-specific variants of contrastive learning can construct positive pairs from two distinct images, as opposed to augmenting the same image twice. Unlike in traditional contrastive methods, this can result in positive pairs not matching perfectly. Similar to false negative pairs, this could impede model performance. Surprisingly, we find that downstream semantic segmentation is either robust to the noisy pairs or even benefits from them. The experiments are conducted on the remote sensing dataset xBD, and a synthetic segmentation dataset, on which we have full control over the noise parameters. As a result, practitioners should be able to use such domain-specific contrastive methods without having to filter their positive pairs beforehand.