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Found 3 papers, 1 papers with code
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery
To achieve this, we solve real-world deep learning problems in the very specific context of agave crop segmentation such as lack of data, low quality labels, highly imbalanced data, and low model performance.
Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data
In this work, we present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis to handle these problems for the Jalisco region.
A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification
This new trainable layer is capable of coping with image classification even with large contrast variations.
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