A unified explanation for the morphology of raised peatlands.

Raised peatlands, or bogs, are gently mounded landforms that are composed entirely of organic matter and store the most carbon per area of any terrestrial ecosystem. The shapes of bogs are critically important because their domed morphology accounts for much of the carbon that bogs store and determines how they will respond to interventions to stop greenhouse gas emissions and fires after anthropogenic drainage. However, a general theory to infer the morphology of bogs is still lacking.

Promoting Connectivity of Network-Like Structures by Enforcing Region Separation.

We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted road causes two background regions, that lie on the opposite sides of a ground truth road, to touch in prediction.