Classification methods for urban hierarchy and analysis of robust features using nighttime light remote sensing
Zhao Zongze
Zhou Yuyao
Chen Shiyu
Abstract：Objectives Urban hierarchy classification levels is closely related to urban planning and sustain-able development.This study aims to evaluate the applicability of machine learning classification methods for urban hierarchy classification and analyze the robustness of nighttime light remote sensing features.Methods Based on the 2021 NPP-VIIRS(National Polar-orbiting Partnership-Visible Infrared Imaging Radi-ometer)nighttime light remote sensing imagery,12 statistical features were extracted from 297 cities in China.Machine learning methods were employed for urban hierarchy recognition and classification,and the SHAP(Shapley Additive exPlaneations)method was used for the model interpretation and robust feature identification.Results The results show that all machine learning methods achieved AUC values above 0.90 under ROC(Receiver Operating Characteristic)curves,demonstrating excellent classification performance.Among them,random forest exhibited the best performance.with an accuracy of 0.75 and an F1 value of 0.764 6.The recognition ability of machine learning methods varied across different city tiers:The classifi-cation accuracy for third-tier and fourth-tier cities was significantly lower than that of first-tier,second-tier,and fifth-tier cities.Classification accuracy for third-and fourth-tier cities was significantly lower than for first-,second-,and fifth-tier cities.The ensemble model combining XGBoost and random forest achieved an overall accuracy of 0.783 3,with an F1 value of 0.805 1.While feature importance varied across differ-ent models,the robust nighttime light features remained consistent.Conclusions This study demonstrates that machine learning classification methods based on nighttime light remote sensing imagery can effectively identify urban hierarchies.The identification of robust nighttime light feature variables reduces the complex-ity of urban hierarchy classification and improves classification efficiency.This approach provides a scien-tific basis for urban classification.
Keywords：nighttime light remote sensingmachine learningurban hierarchy classificationfeature impor-tanceclassification accuracy
Publication Date：2025-11-30
Online Publishing Date：2025-10-22(First online date of this platform, not the publication date of the document)
Pages：9( 156-164 )