  Compared to supervised deep learning, self-supervision provides remote
sensing a tool to reduce the amount of exact, human-crafted geospatial
annotations. While image-level information for unsupervised pretraining
efficiently works for various classification downstream tasks, the performance
on pixel-level semantic segmentation lags behind in terms of model accuracy. On
the contrary, many easily available label sources (e.g., automatic labeling
tools and land cover land use products) exist, which can provide a large amount
of noisy labels for segmentation model training. In this work, we propose to
exploit noisy semantic segmentation maps for model pretraining. Our experiments
provide insights on robustness per network layer. The transfer learning
settings test the cases when the pretrained encoders are fine-tuned for
different label classes and decoders. The results from two datasets indicate
the effectiveness of task-specific supervised pretraining with noisy labels.
Our findings pave new avenues to improved model accuracy and novel pretraining
strategies for efficient remote sensing image segmentation.
