Land cover maps are a pivotal element in a wide range of Earth Observation (EO)
applications. However, annotating large datasets to develop supervised systems for remote …

Forests play a crucial role in the Earth's system processes and provide a suite of social and
economic ecosystem services, but are significantly impacted by human activities, leading to …

Building change detection (BCD) from remote sensing images is essential in various
practical applications. Recently, inspired by the achievement of deep learning in semantic …

Continual learning in dynamic environments is a challenge for large-scale machine learning
models. This research addresses Domain Incremental Learning (DIL), a setting where the …

Remote sensing is increasingly being used to create large-scale forest descriptions. In New
Zealand, where radiata pine (Pinus radiata) plantations dominate the forestry sector, the …

Deep learning has emerged as a promising avenue for automatic map**, demonstrating
high efficacy in land cover categorization through various semantic segmentation models …

We introduce innovative unsupervised domain adaptation (UDA) techniques that leverage
the integration of DAFomer and cross-attention mechanisms, tailored to effectively handle …

Deep networks trained in a supervised way can achieve impressive performance in the
initial distribution while performing poorly in close-but-different distributions. Although there …