Luanyun Hu

TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.

TL;DR: In this paper, the spectral information provided by the Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, was tested with two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms.

TL;DR: Based on the same data source of Landsat TM/ETM+ in 1990s, 2000s and 2010s, all urban built-up areas in China are mapped mainly by human interpretation as mentioned in this paper.

TL;DR: This research tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years, with the same Landsat Thematic Mapper data set.

TL;DR: A global validation data-set based on interpreting Landsat Thematic Mapper and Enhanced TM Plus images for a total of 38,664 sample units pre-determined with an equal-area stratified sampling scheme, which indicates some of the homogeneous sample units in the data- set have already been used in assessing other classification results or as training data for land-cover mapping with coarser-resolution data.