Many deep learning approaches make extensive use of backbone networks pretrained on
large datasets like ImageNet, which are then fine-tuned. In remote sensing, the lack of …

Earth observation is a fundamental tool for monitoring the evolution of land use in specific
areas of interest. Observing and precisely defining change, in this context, requires both time …

Data representativity is crucial when drawing inference from data through machine learning
models. Scholars have increased focus on unraveling the bias and fairness in models, also …

Optical and radar satellite time series are synergetic: optical images contain rich spectral
information, while C-band radar captures useful geometrical information and is immune to …

Geographic location is essential for modeling tasks in fields ranging from ecology to
epidemiology to the Earth system sciences. However, extracting relevant and meaningful …

Remote sensing datasets pose a number of interesting challenges to machine learning
researchers and practitioners, from domain shift (spatially, semantically and temporally) to …

The recent developments of deep learning models that capture complex temporal patterns of
crop phenology have greatly advanced crop classification from Satellite Image Time Series …

Abstract Analysis of compressible turbulent flows is essential for applications related to
propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 …

Earth scientists study a variety of problems with remote sensing data, but they most often
consider them in isolation from each other, which limits information flows across disciplines …

The advances in remote sensing technologies have boosted applications for Earth
observation. These technologies provide multiple observations or views with different levels …