Impact of Urban Vegetation in Sustainable Smart Cities Development: A Comprehensive Appraisal

Abstract

Urban vegetation is the crucial need for the sustainable development of green smart cities. In the current green smart cities, absence of urban vegetation causes unusual high temperature and adverse effect of human health like heat stroke, headache, dehydration, cardiovascular, respiratory etc. This article offers an extensive examination of image processing methods employed in the assessment of urban vegetation. Urban environments often suffer from a scarcity of green areas, underscoring the importance of comprehending and tracking vegetation's condition and distribution for both environmental and human welfare. Notably, high-resolution aerial and satellite imagery, especially satellite images, serve as invaluable resources for evaluating urban vegetation. Within this paper, we delve into cutting-edge image processing techniques used in urban vegetation research, with a primary focus on classification, segmentation, and change detection algorithms. The study scrutinizes a range of approaches for feature extraction and classification, encompassing methodologies like texture analysis, spectral indices, and object-based analysis. Additionally, we explore machine learning and deep learning integration, multi-sensor data fusion, and the adoption of emerging technologies such as LiDAR and hyper spectral imaging as limitations and future avenues in urban vegetation analysis. The insights derived from this review will prove beneficial to researchers, practitioners, and policymakers involved in endeavors aimed at monitoring and enhancing green spaces within urban areas.