  Remote sensing benefits habitat conservation by making monitoring of large
areas easier compared to field surveying especially if the remote sensed data
can be automatically analyzed. An important aspect of monitoring is classifying
and mapping habitat types present in the monitored area. Automatic
classification is a difficult task, as classes have fine-grained differences
and their distributions are long-tailed and unbalanced. Usually training data
used for automatic land cover classification relies on fully annotated
segmentation maps, annotated from remote sensed imagery to a fairly high-level
taxonomy, i.e., classes such as forest, farmland, or urban area. A challenge
with automatic habitat classification is that reliable data annotation requires
field-surveys. Therefore, full segmentation maps are expensive to produce, and
training data is often sparse, point-like, and limited to areas accessible by
foot. Methods for utilizing these limited data more efficiently are needed.
  We address these problems by proposing a method for habitat classification
and mapping, and apply this method to classify the entire northern Finnish
Lapland area into Natura2000 classes. The method is characterized by using
finely-grained, sparse, single-pixel annotations collected from the field,
combined with large amounts of unannotated data to produce segmentation maps.
Supervised, unsupervised and semi-supervised methods are compared, and the
benefits of transfer learning from a larger out-of-domain dataset are
demonstrated. We propose a \ac{CNN} biased towards center pixel classification
ensembled with a random forest classifier, that produces higher quality
classifications than the models themselves alone. We show that cropping
augmentations, test-time augmentation and semi-supervised learning can help
classification even further.

Computer Vision and Pattern Recognition