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The garden landscape of urban residential areas has significant environmental benefits, allowing people to get closer to nature and integrate with nature to relax and have some social benefits at the same time. This article is aimed at studying the effect of urban greening remote sensing on residential landscape construction. This paper uses the greening remote sensing image processing algorithm combined with the analysis method of SURF color remote sensing image and conducts the experiment based on the urban greening remote sensing on the residential area landscape architecture research. This article combines sustainable development strategies with innovative design methods to deeply study the role of urban greening remote sensing on the construction of residential landscape architecture. At the same time, the experiment in this article also proposes that the garden landscape of urban residential areas can provide entertainment for people, soothe residents’ mood, and achieve the best use of residential buildings. However, there are still some constraints in the development process of residential industry: insufficient government support, relatively backward technology of residential industrialization, lack of support and input from enterprises, and prejudice of people’s traditional concept towards it. The experimental results of this paper show that the remote sensing image features of buildings in residential areas and public areas are obvious. In the recognition of 46 training features and 34 detection features, the number of correct remote sensing recognition features is 32, and the green building image remote sensing recognition is good. The accuracy is 94%, which can meet the identification requirements of greening in the building. Combined with the specific conditions of urban greening and garden construction in my country, it can meet people’s entertainment needs and, to a certain extent, can also improve people’s life and cultural taste.

Study on the Construction of Landscape Architecture in Residential District Based on Urban Greening Remote Sensing