In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-
attentional model for general Satellite Image Time Series (SITS) processing based on the …

Convolutional neural networks can achieve remarkable performance in semantic
segmentation tasks. However, such neural network approaches heavily rely on costly pixel …

Earth observation (EO) satellite missions have been providing detailed images about the
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …

As fuelled by the advancement of deep learning for computer vision tasks, its application in
other fields has been boosted. This technology has been increasingly applied to the …

Field boundaries are at the core of many agricultural applications and are a key enabler for
the operational monitoring of agricultural production to support food security. Recent …

In training machine learning models for land cover semantic segmentation there is a stark
contrast between the availability of satellite imagery to be used as inputs and ground truth …

Machine Learning (ML) is continuously permeating a growing amount of application
domains. Generative AI such as Large Language Models (LLMs) also sees broad adoption …

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for
better agricultural management, land planning, and environmental sustainability. However …

This paper presents DeepSatData a free and open source pipeline for automatically
generating satellite imagery datasets for training machine learning models. The …

Deep learning has significantly improved the accuracy of crop classification using
multispectral temporal data. However, these models have complex structures with numerous …