Summary of Increasing the Robustness Of Model Predictions to Missing Sensors in Earth Observation, by Francisco Mena et al.
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
by Francisco Mena, Diego Arenas, Andreas Dengel
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes two novel methods for improving the prediction accuracy of Multi-Sensor Machine Learning (ML) models used for Earth Observation (EO). The proposed methods, Input Sensor Dropout (ISensD) and Ensemble Sensor Invariant (ESensI), are designed to address the challenge posed by missing data in non-persistent sensors. By integrating data from various sources, these methods aim to enhance the robustness of model predictions to missing sensors. The paper demonstrates the effectiveness of these methods through experimentation on three multi-sensor temporal EO datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve the accuracy of Multi-Sensor ML models for Earth Observation by addressing the challenge posed by missing data in non-persistent sensors. Two new methods are proposed: Input Sensor Dropout (ISensD) and Ensemble Sensor Invariant (ESensI). These methods aim to enhance prediction accuracy by integrating data from various sources. The paper shows how these methods can increase the robustness of model predictions to missing sensors through experimentation on three EO datasets. |
Keywords
» Artificial intelligence » Dropout » Machine learning
