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Found 11 papers, 7 papers with code
On the Foundations of Earth and Climate Foundation Models
Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model.
Large-scale flood modeling and forecasting with FloodCast
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost.
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings.
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML.
UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency.
DisasterNets: Embedding Machine Learning in Disaster Mapping
It consists of two stages, space granulation and attribute granulation.
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Image keypoints and descriptors play a crucial role in many visual measurement tasks.
Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams
To fit the sparse nature of events and sufficiently explore the relationship between them, we develop a novel attention-aware model named Event Voxel Set Transformer (EVSTr) for spatiotemporal representation learning on event streams.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images.
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides
Automatic recognition and segmentation methods now become the essential requirement in identifying co-seismic landslides, which are fundamental for disaster assessment and mitigation in large-scale earthquakes.
