Papers Flood Inundation Mapping
“Flood Inundation Mapping” 태그가 달린 논문 9편 · 필터 해제
Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
Flooding is the most pervasive natural disaster worldwide. Timely and accurate flood inundation mapping are essential for informing disaster risk management. Optical satellite missions provide high-resolution, multispect…
Flood Inundation MappingDecision MakingCloud RemovalPrithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping
Geo-Foundation Models (GFMs), have proven effective in diverse downstream applications, including semantic segmentation, classification, and regression tasks. However, in case of flood mapping using Sen1Flood11 dataset a…
Flood Inundation MappingSemantic SegmentationAssessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users
Geo-Foundational Models (GFMs) enable fast and reliable extraction of spatiotemporal information from satellite imagery, improving flood inundation mapping by leveraging location and time embeddings. Despite their potent…
Flood Inundation MappingSensor-Adaptive Flood Mapping with Pre-trained Multi-Modal Transformers across SAR and Multispectral Modalities
Floods are increasingly frequent natural disasters causing extensive human and economic damage, highlighting the critical need for rapid and accurate flood inundation mapping. While remote sensing technologies have advan…
Flood Inundation MappingDeep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
In light of growing threats posed by climate change in general and sea level rise (SLR) in particular, the necessity for computationally efficient means to estimate and analyze potential coastal flood hazards has become …
Depth EstimationFlood Inundation MappingImage-to-Image RegressionImage-to-Image Translation+2Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery
Flood inundation mapping is a critical task for responding to the increasing risk of flooding linked to global warming. Significant advancements of deep learning in recent years have triggered its extensive applications,…
Active LearningFlood Inundation MappingAutomated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping
Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning…
Depth EstimationFlood Inundation MappingAssessment of a new GeoAI foundation model for flood inundation mapping
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery,…
Flood Inundation MappingRepresentation LearningOn the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches
Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps d…
Earth ObservationFlood Inundation MappingHyperparameter Optimization