1 citations · 1 across the 2 of their papers we have counts for
7 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…
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…
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…
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,…