From Generative Sampling to Urban Typology: A PRTS-AI Supported Framework for Multi-Decadal Urban LULC Mapping and Cross-City Transferability Analysis

High-quality, temporally consistent training samples are the cornerstone of accurate long-term urban Land Use/Land Cover (LULC) mapping. However, traditional sample generation relies heavily on labor-intensive manual interpretation and often lacks reproducibility. To address this, we developed PRTS-AI (Primary Regulated Time-series Sampling), an open-source system that integrates OpenStreetMap (OSM) data extraction, Large Language Model (LLM)-driven semantic classification, and LandTrendr-based temporal filtering into an automated workflow. By leveraging generative AI (e.g., DeepSeek/ChatGPT/Gemini) to interpret polygon attributes and using POI-based consistency checks, the system significantly reduces manual workload while ensuring semantic accuracy.

The PRTS-AI system integrates multi-source spatial and temporal data into a streamlined workflow, including:

(1) extraction of OpenStreetMap (OSM) features for user-defined study areas;

(2) semantic classification of polygon features using large language models;

(3) detection and filtering of change pixels using the LandTrendr time-series algorithm;

(4) recommendation of city-specific sampling parameters based on a six-dimensional urban typology framework.

 

This system enables reproducible multi-temporal sample generation, spatial heterogeneity validation, and fine-scale classification support across diverse urban settings. Furthermore, this system can operate in parallel with the usual land cover sample selection and subsequent classification processes.

We applied PRTS-AI to map the urban evolution of diverse cities in Liaoning and Shandong provinces, China, from 2000 to 2020. The framework achieved an overall mapping accuracy of ~80%, with residential categories reaching 90%. Beyond mapping, we utilized the fine-grained Local Climate Zone (LCZ) metrics generated by the system to investigate the transferability of samples. Through Principal Component Analysis (PCA) of residential morphologies, we quantitatively identified that cities cluster into distinct typologies driven by macro-factors (e.g., coastal vs. resource-based industrial cities) rather than administrative hierarchies. These findings challenge the assumption of universal sample transferability, suggesting that sample migration is most effective within specific urban typologies. Consequently, PRTS-AI incorporates a typology-based parameter recommendation module to guide city-specific sampling. This study presents a scalable, AI-empowered solution for urban mapping and offers new insights into the spatiotemporal heterogeneity of urban forms.

 

However, limited sample transferability may still be achieved between cities with similar characteristics, based on a preliminary six-dimensional classification framework.

PRTS-AI provides a lightweight, reproducible, and extensible solution for urban LULC research, supporting both academic investigations and practical urban planning applications.