The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

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 …

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 …

Accurate spatial information for agricultural field parcels is important for agricultural
production management and understanding agro-industrialization and intensification …

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain
closed boundaries when delineating agricultural parcels from remote sensing images. It …

Earthquakes and heavy rainfalls are the two leading causes of landslides around the world.
Since they often occur across large areas, landslide detection requires rapid and reliable …

This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural
fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring …

Delineation of agricultural fields is desirable for operational monitoring of agricultural
production and is essential to support food security. Due to large within-class variance of …

The cropland field (CF) is the basic unit of agricultural production and a key element of
precision agriculture. High-precision delineations of CF boundaries provide a reliable data …

Farmland segmentation scenario from remote sensing images plays an important role in
crop growth monitoring, precision agriculture, and intelligent agriculture. To achieve high …