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The understanding of global climate change, agriculture resilience, anddeforestation control rely on the timely observations of the Land Use and LandCover Change (LULCC). Recently, some deep learning (DL) methods have beenadapted to make an automatic classification of Land Cover (LC) for global andhomogeneous data. However, most of these DL models can not apply effectively toreal-world data. i.e. a large number of classes, multi-seasonal data, diverseclimate regions, high imbalance label dataset, and low-spatial resolution. Inthis work, we present our novel lightweight (only 89k parameters) ConvolutionNeural Network (ConvNet) to make LC classification and analysis to handle theseproblems for the Jalisco region. In contrast to the global approaches, theregional data provide the context-specificity that is required for policymakersto plan the land use and management, conservation areas, or ecosystem services.In this work, we combine three real-world open data sources to obtain 13channels. Our embedded analysis anticipates the limited performance in someclasses and gives us the opportunity to group the most similar, as a result,the test accuracy performance increase from 73 % to 83 %. We hope that thisresearch helps other regional groups with limited data sources or computationalresources to attain the United Nations Sustainable Development Goal (SDG)concerning Life on Land.

Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data