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

arXiv: https://arxiv.org/abs/2201.10985 · HTML: https://mlsanity.com/paper/2201.10985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Survey of Dropout Methods for Deep Neural Networks](https://mlsanity.com/paper/1904.13310/) (2019)
- [LandCoverNet: A global benchmark land cover classification training dataset](https://mlsanity.com/paper/2012.03111/) (2020)
- [A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification](https://mlsanity.com/paper/2109.06926/) (2021)
- [Classification with many classes: challenges and pluses](https://mlsanity.com/paper/1506.01567/) (2015)

## Built on by (most cited first, 1 of 1)

- [Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery](https://mlsanity.com/paper/2303.11564/) (2023)

## Abstract (arXiv)

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 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. In contrast to the global approaches, the regional data provide the context-specificity that is required for policymakers to plan the land use and management, conservation areas, or ecosystem services. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes 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 this research helps other regional groups with limited data sources or computational resources to attain the United Nations Sustainable Development Goal (SDG) concerning Life on Land.