Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2
MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land …

Open-set semantic segmentation remains yet a challenging task, not only due to the
inherent challenges of pixel-wise classification but also the precise segmentation of …

Crop recognition in tropical regions is a challenging task because of the highly complex crop
dynamics, with multiple crops per year. Nevertheless, most automatic methods proposed …

Earth Observation data support the large-scale monitoring of croplands across South
America (SA). For instance, optical remote sensing (ORS) data are typically used to map and …

Clouds are one of the major limitations to crop monitoring using optical satellite images.
Despite all efforts to provide decision-makers with high-quality agricultural statistics, there is …

Conditional (cGAN)-based methods proposed so far for synthetic aperture radar (SAR)-to-
optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work …

The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity
exports and is changing its landscape. Hence, efforts to accurate land use and land cover …

Abstract The Sentinel-2/MultiSpectral Instrument (S2/MSI) expands the frequency of satellite
observations, which is relevant to elaborate detailed and timely land use and land cover …

The use of satellite image time series analysis and machine learning methods brings new
opportunities and challenges for land use and cover changes (LUCC) mapping over large …

We are witnessing the digitalization era, where artificial intelligence (AI)/machine learning
(ML) models are mandatory to transform this data deluge into actionable information …