# A fine-grained evaluation framework for urban land cover change based on feature monitoring with remotely sensed imagery

## TL;DR

This paper introduces a high-precision framework for evaluating urban land cover changes using remote sensing data and the UASFNet model, offering insights into urban development and sustainability.

## Contribution

The study proposes a novel data-driven framework using UASFNet for fine-scale urban land cover change assessment with high accuracy.

## Key findings

- UASFNet achieved mIoU values of 91.52%, 93.31%, and 88.90% on benchmark datasets, outperforming existing models.
- Langfang's urban area showed a 16.86% increase in impervious surfaces and 40% decline in greenbelt from 2017–2023.
- The framework supports scalable urban planning and ecological conservation through multi-core, belt-like expansion analysis.

## Abstract

Against the backdrop of accelerating global climate change and urbanization, urban land cover change has emerged as a critical indicator for understanding the dynamic evolution of cities and the transformation of urban ecosystems. This study proposes a data-driven framework for fine-scale urban land cover change assessment based on the UASFNet model, enabling high-precision evaluation of urban land cover dynamics. The approach first performs preprocessing and co-registration of bi-temporal remote sensing images from the study area, and applies the trained UASFNet model to identify urban land cover types and extract land cover information for each temporal phase. The Analytic Hierarchy Process (AHP) is then employed to determine the weights of various indicator factors. By integrating building disturbance, greenbelt disturbance, and road disturbance indices, the framework quantitatively evaluates the intensity of land cover change at both pixel and regional scales. Experimental results across three benchmark datasets, consisting of high-resolution sub-meter RGB urban remote sensing imagery, demonstrate that UASFNet achieves superior segmentation accuracy, with mean Intersection over Union (mIoU) values of 91.52%, 93.31%, and 88.90%, substantially outperforming several state-of-the-art baseline models. Spatial analysis of the Langfang urban area (2017–2023) reveals a marked increase in impervious surface coverage (+16.86%) and a sharp decline in greenbelt (−40%), with the urban landscape exhibiting a multi-core, belt-like expansion pattern oriented toward newly developed districts. The proposed framework not only enhances the interpretability and generalization of remote sensing models in complex urban environments but also provides a scalable analytical tool to support urban spatial planning, ecological conservation, and sustainable city governance.