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Found 7 papers, 5 papers with code
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation
Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical.
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Ranked #2 on
Crop Classification
on CropHarvest - Togo
On 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.
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques.
