Department of Civil Engineering, University of Calabria, 87036 Rende, CS, Italy.
Sensors (Basel). 2023 Apr 7;23(8):3805. doi: 10.3390/s23083805.
Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full spatiotemporal configuration with urban development using innovative remote sensing (RS) technologies. Focusing on this issue, the authors propose an innovative methodology for the analysis of the urban and greening changes over time by integrating deep learning (DL) technologies to classify and segment the built-up area and the vegetation cover from satellite and aerial images and geographic information system (GIS) techniques. The core of the methodology is a trained and validated U-Net model, which was tested on an urban area in the municipality of Matera (Italy), analyzing the urban and greening changes from 2000 to 2020. The results demonstrate a very good level of accuracy of the U-Net model, a remarkable increment in the built-up area density (8.28%) and a decline in the vegetation cover density (5.13%). The obtained results demonstrate how the proposed method can be used to rapidly and accurately identify useful information about urban and greening spatiotemporal development using innovative RS technologies supporting sustainable development processes.
尽管许多作者观察到，随着建成区的增加，绿化覆盖面积减少，导致生态系统和社会福祉的基本环境服务恶化，但很少有研究使用创新的遥感 (RS) 技术来衡量绿化在与城市发展完全的时空配置中的发展情况。针对这一问题，作者提出了一种创新的方法，通过将深度学习 (DL) 技术集成到从卫星和航空图像以及地理信息系统 (GIS) 技术中分类和分割建成区和植被覆盖的方法，来分析随时间推移的城市和绿化变化。该方法的核心是一个经过训练和验证的 U-Net 模型，该模型在意大利马泰拉市的一个城区进行了测试，分析了 2000 年至 2020 年的城市和绿化变化。结果表明，U-Net 模型具有非常高的准确性水平，建成区密度显著增加（8.28%），植被覆盖密度下降（5.13%）。所得结果表明，如何使用创新的 RS 技术来快速准确地识别有关城市和绿化时空发展的有用信息，从而支持可持续发展进程。
