Qu Cheng

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: 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.

TL;DR: The Tsinghua–Lancet Commission on Healthy Cities in China aimed to characterise, understand, and address urban health challenges in the unique context of China’s rapid and dynamic urban development.

TL;DR: In this paper, the authors identify the spatial temporal patterns and spatial hotspots of land-cover mapping research around the world and find that there is a significant mismatch between hotspot areas of landcover mapping and areas that are either hard to map or rich in biodiversity.

TL;DR: Transportation-related risk factors are a major source of morbidity and mortality in China, where the expansion of road networks and surges in personal vehicle ownership are having profound effects on public health.