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Found 13 papers, 10 papers with code
Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting
Hurricanes and coastal floods are among the most disastrous natural hazards.
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface.
Ranked #1 on
Cloud Removal
on SEN12MS-CR
High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark
In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion.
On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions
With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving.
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
Ranked #2 on
Cloud Removal
on SEN12MS-CR
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds.
Ranked #3 on
Cloud Removal
on SEN12MS-CR-TS
Self-Supervised Multisensor Change Detection
In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors.
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
This work has been accepted by IEEE TGRS for publication.
Topic Modeling on User Stories using Word Mover's Distance
Depending on the word embeddings we use in our approaches, we manage to cluster the user stories in two ways: one that is closer to the original categorization and another that allows new insights into the dataset, e. g. to find potentially new categories.
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities.
Ranked #3 on
Cloud Removal
on SEN12MS-CR
Destination Prediction Based on Partial Trajectory Data
Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data.
Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities
Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale land cover mapping.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Beyond Cartesian Representations for Local Descriptors
We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.
