Cover Image

Data-Centric Machine Learning for Earth Observation: Necessary and Sufficient Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Najjar, Hiba, Nuske, Marlon, Dengel, Andreas
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Intrinsic Explainability of Multimodal Learning for Crop Yield Prediction
by: Najjar, Hiba, et al.
Published: (2025)

Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
by: Mena, Francisco, et al.
Published: (2024)

Can Multitask Learning Enhance Model Explainability?
by: Najjar, Hiba, et al.
Published: (2025)

XAI-Guided Enhancement of Vegetation Indices for Crop Mapping
by: Najjar, Hiba, et al.
Published: (2024)

Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
by: Najjar, Hiba, et al.
Published: (2024)

Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
by: Mena, Francisco, et al.
Published: (2022)

Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
by: Dembinsky, David, et al.
Published: (2025)

Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
by: Zhang, Zhen, et al.
Published: (2026)

Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach
by: Mena, Francisco, et al.
Published: (2025)

Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
by: Pathak, Deepak, et al.
Published: (2023)

On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
by: Mena, Francisco, et al.
Published: (2025)

Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
by: Mena, Francisco, et al.
Published: (2024)

Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
by: Mena, Francisco, et al.
Published: (2024)

An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
by: Miranda, Miro, et al.
Published: (2025)

Sufficient and Necessary Explanations (and What Lies in Between)
by: Bharti, Beepul, et al.
Published: (2024)

Invariant Learning via Probability of Sufficient and Necessary Causes
by: Yang, Mengyue, et al.
Published: (2023)

A Theoretical Analysis of Compositional Generalization in Neural Networks: A Necessary and Sufficient Condition
by: Li, Yuanpeng
Published: (2025)

Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
by: Mena, Francisco, et al.
Published: (2025)

Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
by: Herurkar, Dayananda, et al.
Published: (2025)

On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
by: Luttermann, Malte, et al.
Published: (2026)

Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
by: Chen, Boyu, et al.
Published: (2024)

Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Data
by: Najjar, Alameen
Published: (2024)

Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
by: Fan, Fan, et al.
Published: (2026)

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
by: Tschalzev, Andrej, et al.
Published: (2024)

In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
by: Mena, Francisco, et al.
Published: (2024)

Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
by: Miranda, Miro, et al.
Published: (2024)

From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
by: Mercier, Dominique, et al.
Published: (2023)

Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
by: Herurkar, Dayananda, et al.
Published: (2024)

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement
by: Alexander, Yotam, et al.
Published: (2023)

Assessment of Sentinel-2 spatial and temporal coverage based on the scene classification layer
by: Sanchez, Cristhian, et al.
Published: (2024)

Towards Error Centric Intelligence I, Beyond Observational Learning
by: Thomas, Marcus A.
Published: (2025)

Improving Multimodal Learning Balance and Sufficiency through Data Remixing
by: Ma, Xiaoyu, et al.
Published: (2025)

Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
by: Cheng, Yi, et al.
Published: (2024)

DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI
by: Cho, Hyowon, et al.
Published: (2024)

VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting
by: Koochali, Alireza, et al.
Published: (2024)

A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
by: Pitsiorlas, Ioannis, et al.
Published: (2024)

Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
by: Kim, Sunwoo, et al.
Published: (2025)

Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

Are Human-generated Demonstrations Necessary for In-context Learning?
by: Li, Rui, et al.
Published: (2023)