# So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification

arXiv: https://arxiv.org/abs/1912.12171 · HTML: https://mlsanity.com/paper/1912.12171/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 24 of 24)

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- [EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM](https://mlsanity.com/paper/2506.01667/) (2025)
- [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://mlsanity.com/paper/2304.11619/) (2023)
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- [Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification](https://mlsanity.com/paper/2412.18235/) (2024)
- [Improving satellite imagery segmentation using multiple Sentinel-2 revisits](https://mlsanity.com/paper/2409.17363/) (2024)
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- [Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification](https://mlsanity.com/paper/2208.02613/) (2022)
- [Multispectral Contrastive Learning with Viewmaker Networks](https://mlsanity.com/paper/2302.05757/) (2023)
- [Sparse Mixture-of-Experts for Multi-Channel Imaging: Are All Channel Interactions Required?](https://mlsanity.com/paper/2511.17400/) (2025)
- [Channel-Aware Probing for Multi-Channel Imaging](https://mlsanity.com/paper/2602.12696/) (2026)
- [Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images](https://mlsanity.com/paper/2609.21629/) (2026)

## Abstract (arXiv)

Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark dataset named "So2Sat LCZ42," which consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. As rarely done in other labeled remote sensing dataset, we conducted rigorous quality assessment by domain experts. The dataset achieved an overall confidence of 85%. We believe this LCZ dataset is a first step towards an unbiased globallydistributed dataset for urban growth monitoring using machine learning methods, because LCZ provide a rather objective measure other than many other semantic land use and land cover classifications. It provides measures of the morphology, compactness, and height of urban areas, which are less dependent on human and culture. This dataset can be accessed from http://doi.org/10.14459/2018mp1483140.