CropNet, a lightweight CNN jointly convolving spectral and temporal dimensions, learns invariant crop signatures from multispectral time series and outperforms larger models under geographic domain shifts on the new CropGlobe benchmark spanning eight countries.
Deep residual learning for image recognition,
3 Pith papers cite this work. Polarity classification is still indexing.
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Multi-source EO models' robustness to missing data depends on task nature, source complementarity, and design, sometimes improving when certain sources are removed.
Iterative temporal differencing with fixed random synaptic feedback can replace the activation function derivative in error backpropagation.
citing papers explorer
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Invariant Features for Global Crop Type Classification
CropNet, a lightweight CNN jointly convolving spectral and temporal dimensions, learns invariant crop signatures from multispectral time series and outperforms larger models under geographic domain shifts on the new CropGlobe benchmark spanning eight countries.
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On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
Multi-source EO models' robustness to missing data depends on task nature, source complementarity, and design, sometimes improving when certain sources are removed.
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Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning
Iterative temporal differencing with fixed random synaptic feedback can replace the activation function derivative in error backpropagation.