Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

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 …

Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Developing similar techniques for satellite …

Unprecedented access to multi-temporal satellite imagery has opened new perspectives for
a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of …

During the last two decades, forest monitoring and inventory systems have moved from field
surveys to remote sensing-based methods. These methods tend to focus on economically …

The U-net is nowadays among the most popular deep learning algorithms for land use/land
cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar …

The aim of this paper is to map agricultural crops by classifying satellite image time series.
Domain experts in agriculture work with crop type labels that are organised in a hierarchical …

Abstract Google Earth Engine (GEE) is presently the most innovative international open-
source platform for the advanced-level analysis of geospatial big data. In this study, we used …

Mangroves are highly productive wetland ecosystems, located at the interlocking area of
tropical and subtropical coastal zones. Accurately mapping the distribution, quality and …

Rice is the most important food crop in the developing world, and more than half of the
global population consumes it as a staple food. Mapping the area of rice cultivation in a …