Deep Learning for Understanding Satellite Imagery: An Experimental Survey.

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset.

Correction: Learning efficient haptic shape exploration with a rigid tactile sensor array.

[This corrects the article DOI: 10.1371/journal.pone.

Learning efficient haptic shape exploration with a rigid tactile sensor array.

Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics.