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

TL;DR

The paper introduces 'So2Sat LCZ42', a comprehensive, expert-labeled benchmark dataset of remote sensing images for global urban climate zone classification, enabling improved machine learning analysis of urban environments.

Contribution

It provides a large, high-quality, globally distributed dataset with expert validation, facilitating unbiased urban climate zone classification using machine learning.

Findings

Dataset contains half a million image patches from 42 urban areas.

Achieved 85% overall confidence in labels through expert assessment.

Enables global urban growth monitoring with objective morphological measures.

Abstract

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.