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Found 10 papers, 7 papers with code
Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain
Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.
Mixture of Experts with Uncertainty Voting for Imbalanced Deep Regression Problems
For regression, recent work relies on the continuity of the distribution; whereas for classification there has been a trend to employ mixture-of-expert models and let some ensemble members specialize in predictions for the sparser regions.
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages.
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
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities.
Ranked #1 on
Panoptic Segmentation
on PASTIS-R
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations.
Ranked #3 on
Cloud Removal
on SEN12MS-CR-TS
Leveraging Class Hierarchies with Metric-Guided Prototype Learning
In this paper, we propose to model the hierarchical class structure by integrating this metric in the supervision of a prototypical network.
Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike.
Ranked #1 on
Time Series Classification
on s2-agri
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.
Ranked #2 on
Time Series Classification
on s2-agri
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series.
