Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

Microgrid (MG) represents a promising opportunity for integrating renewable energy systems
with the electric power grid. However, numerous complexities need to be addressed in the …

Magnetic anomaly detection is becoming increasingly prevalent for detecting and locating
the buried pipelines. The detection performance is often hindered by adjacent pipeline, near …

In recent years, with the development of geological prospecting from shallow ore to deep
and hidden ore, the difficulty of prospecting is increasing day by day, so the application of …

Quantitative precipitation prediction is essential for managing water-related disasters,
including floods, landslides, tsunamis, and droughts. Recent advances in data-driven …

The diversity of geophysical methods and datatypes, as well as the isolated nature of
various specialties (eg, electromagnetic, seismic, potential fields) leads to a profusion of …

Recently, the geospatial semantic information of remote sensing (RS) has attracted attention
due to its various applications. This paper introduces a model for ontology based geospatial …

Data model is a key challenge for crowd-powered data applications. However, current data
model cannot effectively express semantic relations to support big data management …

This study assesses the Martian ionopause using MAVEN datasets between periapsis and
150-600 km. Ionopause is an abrupt reduction of the electron density with increasing …