Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …

Cloud‐based computing, access to big geospatial data, and virtualization, whereby users
are freed from computational hardware and data management logistics, could revolutionize …

Free and open source tools present numerous opportunities to support current urban
planning practice. However, their overview is fragmented, and the uptake among planning …

Detecting effective parameters in flood occurrence is one of the most important issues that
has drawn more attention in recent years. Remote Sensing (RS) and Geographical …

The unprecedented availability of optical satellite data in cloud-based computing platforms,
such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval …

For the last decade, Gaussian process regression (GPR) proved to be a competitive
machine learning regression algorithm for Earth observation applications, with attractive …

The fragile ecological environment and intensive human activities in the Loess Plateau
region of China have led to serious soil erosion and adverse weather disasters. To address …

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various
remote sensing problems. To fully leverage the power of AI and ML to tackle impactful …

Frequent observations of surface water at fine spatial scales will provide critical data to
support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and …

With the global upsurge in climatic extremes, disasters are causing more significant
damages. While disaster risk management (DRM) is a serious global challenge …