4 accepted papers
Super-resolving climate data is crucial for fine-grained decision-making in various domains, ranging from agriculture to environmental conservation. However, existing super-resolution approaches struggle to generate the high-frequency spatial information present in climate data, especially over regi…
Multimodal learning is crucial for ecological applications, which rely on heterogeneous data sources (e.g., satellite imagery, environmental time series, tabular predictors, bioacoustics) but often suffer from incomplete data across and within modalities (e.g., unavailable satellite image due to clo…
Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video dataset…
From optical sensors to microwave radars, leveraging the complementary strengths of remote sensing (RS) sensors is crucial for achieving dense spatio-temporal monitoring of our planet. In contrast, recent deep learning models, whether task-specific or foundational, are often specific to single senso…