Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

The recent success of attention mechanism-driven deep models, like vision transformer (ViT)
as one of the most representatives, has intrigued a wave of advanced research to explore …

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's
spheres. Accurate LC information is a fundamental parameter for the environment and …

Elevation data are fundamental to many applications, especially in geosciences. The latest
global elevation data contains forest and building artifacts that limit its usefulness for …

Monitoring agricultural systems becomes increasingly important in the context of global
challenges like climate change, biodiversity loss, population growth, and the rising demand …

Over past decades, a lot of global land-cover products have been released; however, these
still lack a global land-cover map with a fine classification system and spatial resolution …

Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

Abstract Google Earth Engine (GEE) is a cloud-based geospatial processing platform for
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …