Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data. (arXiv:2201.10985v1 [cs.CV])

The understanding of global climate change, agriculture resilience, and
deforestation control rely on the timely observations of the Land Use and Land
Cover Change (LULCC). Recently, some deep learning (DL) methods have been
adapted to make an automatic classification of Land Cover (LC) for global and
homogeneous data. However, most of these DL models can not apply effectively to
real-world data. i.e. a large number of classes, multi-seasonal data, diverse
climate regions, high imbalance label dataset, and low-spatial resolution. In …

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