Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.

TL;DR: In this paper , the use of new machine learning techniques in mapping variation in ground levels based on ordinary spirit levelling (SL) measurements was investigated and compared in the current study to estimate the leveling through SL measurements.

TL;DR: The findings reveal that various machine-learning algorithms can be employed to map mangroves, and ensemble tree-based approaches such as random forest outperform single classifiers in remote sensing data.

TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.

TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.

TL;DR: Satellite data used to estimate global terrestrial NPP over the past decade found that the earlier trend has been reversed and that NPP has been decreasing, and combined with climate change data suggests that large-scale droughts are responsible for the decline.

TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.