Deep Learning for Very-High Resolution Land-Cover Mapping

Dear Colleagues,

In the field of remote sensing, land cover mapping has always been a popular subject, which is attributable to the fact that land cover data are essential for a multitude of applications. Thanks to their detailed resolution, very high resolution imagery is a privileged tool for studying the earth's surface, and a large number of publications on land cover mapping from such data have been published. For a long time, pixel-oriented and object-oriented approaches, through either rule-based classifications or conventional machine learning algorithms, have been well established and used by a great majority of the community to produce land cover maps from very high resolution Earth observation data. However, since 2015, deep learning techniques from computer vision have made their way into the remote sensing field and have since been successfully applied for various applications. While deep learning approaches can largely outperform conventional machine learning approaches for some tasks, several studies have shown that, instead, conventional machine learning and object-oriented classification approaches still perform better in some contexts.

For this Special Issue, we welcome state-of-the-art research or review papers that are focused on land cover mapping from very high resolution earth observation data, using deep learning methods and addressing topics including (but not limited to): architecture comparison; data fusion; comparison of conventional machine learning and deep learning approaches; deep learning model comprehension; explainable deep learning; weak supervision and semi-supervision; scene classification; semantic segmentation;  instance segmentation; change detection; existing land cover map enrichment.

Dr. Tais Grippa
Dr. Lei Ma
Dr. Claudio Persello
Dr. Arnaud Le Bris
Guest Editor

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