78 citations · 130 across the 8 of their papers we have counts for
9 papers
YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Miro Miranda, Deepak Pathak, Patrick Helber +10
Crop yield prediction requires substantial data to train scalable models. However, creating yield prediction datasets is constrained by high acquisition costs, heterogeneous data q…
Can Multitask Learning Enhance Model Explainability?
Hiba Najjar, Bushra Alshbib, Andreas Dengel
Remote sensing provides satellite data in diverse types and formats. The usage of multimodal learning networks exploits this diversity to improve model performance, except that the…
Intrinsic Explainability of Multimodal Learning for Crop Yield Prediction
Hiba Najjar, Deepak Pathak, Marlon Nuske +1
Multimodal learning enables various machine learning tasks to benefit from diverse data sources, effectively mimicking the interplay of different factors in real-world applications…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…
Data-Centric Machine Learning for Earth Observation: Necessary and Sufficient Features
Hiba Najjar, Marlon Nuske, Andreas Dengel
The availability of temporal geospatial data in multiple modalities has been extensively leveraged to enhance the performance of machine learning models. While efforts on the desig…
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping
Hiba Najjar, Francisco Mena, Marlon Nuske +1
Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands,…
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
Hiba Najjar, Miro Miranda, Marlon Nuske +2
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is e…
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
Francisco Mena, Deepak Pathak, Hiba Najjar +11
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, and industry stakeholders. However, this task is complex and dep…
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
Deepak Pathak, Miro Miranda, Francisco Mena +11
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use hig…