Abstract Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense
potential towards a generic model for Earth Observation. Nevertheless these works primarily …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

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

Precise crop map** is crucial for guiding agricultural production, forecasting crop yield,
and ensuring food security. Integrating optical and synthetic aperture radar (SAR) satellite …

Remotely sensed geospatial data are critical for applications including precision agriculture,
urban planning, disaster monitoring and response, and climate change research, among …

Deep learning methods have achieved promising results in crop map** using satellite
image time series. A challenge still remains on how to better learn discriminative feature …

Accurate crop map** is of great significance for crop yield forecasting, agricultural
productivity development and agricultural management. Thanks to its all-time and all …

The diversity and complementarity of sensors available for Earth Observations (EO) calls for
develo** bespoke self-supervised multimodal learning approaches. However, current …