This paper identifies the uncertain areas and figures out the problematic land cover types in the MODIS Collection 5 global land cover dataset without ground validation samples.

First paper of a series on the City Health Outlook (CHO) project, which aims to establish multi-scale, long-lasting, real-time urban environment and health monitoring networks. This paper is targeted at illustrating the characteristics of the participants and examining the effects of different covariates on personal exposure at various air pollution exposure levels.

This article uses three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT).

Article describes how the low-cost sensor has changed the air quality monitoring paradigm with the capacity for efficient network expansion and community engagement. This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine.