Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …

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 …

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in
several application scenario. Accurate state of health (SOH) forecast is essential for …

Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …

Recently, supervised deep learning has achieved a great success in remote sensing image
(RSI) semantic segmentation. However, supervised learning for semantic segmentation …

The amount of available Earth observation data has increased dramatically in recent years.
Efficiently making use of the entire body of information is a current challenge in remote …

The spatial resolution of land cover map** has been increasing with the evolution of Earth
observation technology. However, the higher spatial resolution makes it more laborious to …

Segmentation is a crucial phase in image processing because it simplifies the
representation of an image and facilitates its analysis. The multilevel thresholding method is …

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …