arXiv Machine Learning
Jun 26
Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery
arXiv:2606. 26204v1 Announce Type: new Abstract: Floods frequently impact regions around the world.
Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
arXiv:2606. 02310v1 Announce Type: cross Abstract: Flooding is the most pervasive natural disaster worldwide.
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arXiv AI
Aug 5
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
arXiv:2608. 03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation.
Hugging Face Trending Papers
Aug 4
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded regions and distort scene structures.
Hugging Face Trending Papers
Aug 3
Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction
Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations.
arXiv AI
Jun 17
Remote sensing data imputation using deep learning for multispectral imagery
arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.
Hugging Face Trending Papers
Jun 23
Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
arXiv Machine Learning
Jun 8
Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
Hugging Face Trending Papers
Jul 30
Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis
Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing across extensive and environmentally diverse regions remains challenging, as most segmentation algorithms lack the generalisation capacity required for large-scale application, while annotated flood data are scarce and unevenly distributed.
arXiv Machine Learning
Jul 23
Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation
arXiv:2607. 19522v1 Announce Type: new Abstract: While machine learning-based weather models hold significant promise, they struggle to predict the detailed structure of large-scale weather systems such as cyclonic storms.
arXiv Machine Learning
Jul 7
Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment
arXiv:2607. 01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving.
arXiv AI
Jun 29
Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution
arXiv:2606. 28039v1 Announce Type: cross Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely available missions such as Sentinel-2 and commercial systems like PlanetScope.
arXiv AI
Jul 16
Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery
arXiv:2507. 16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery, aiming to support and enhance the Emergent Value Added Product (EVAP) developed by the Taiwan Space Agency (TASA).