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. This limits their ability to …
parameters as well as incurring a high computational cost. This limits their ability to …
Self-Supervised Pre-Training with Monocular Height Estimation for Semantic Segmentation
Monocular Height Estimation (MHE) is key for generating 3D city models, essential for swift
disaster response. Moving beyond the traditional focus on performance enhancement, our …
disaster response. Moving beyond the traditional focus on performance enhancement, our …
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining
Self-supervised pretraining on large-scale satellite data has raised great interest in building
Earth observation (EO) foundation models. However, many important resources beyond …
Earth observation (EO) foundation models. However, many important resources beyond …
Identifying high resolution benchmark data needs and Novel data-driven methodologies for Climate Downscaling
We address the essential role of information retrieval in enhancing climate downscaling,
focusing on the need for high-resolution datasets and the application of deep learning …
focusing on the need for high-resolution datasets and the application of deep learning …
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI
N Dionelis, C Fibaek, L Camilleri, A Luyts… - arXiv preprint arXiv …, 2024 - arxiv.org
When we are primarily interested in solving several problems jointly with a given prescribed
high performance accuracy for each target application, then Foundation Models should for …
high performance accuracy for each target application, then Foundation Models should for …