With the rapid development of science and technology, artificial intelligence (AI) is gradually penetrating into all aspects of urban development, especially in the field of urban decision-making [...] 

Practice and Application of Artificial Intelligence in Urban Decision-Making

Semantic-Driven Paradigm Shift in Campus Guide Design Leveraging the KE-AIGC Framework

GIS technology can provide reasonable and sustainable data support for landscape planning and ecological development and make wetland landscape planning consider the spatial layout of landscape and the optimal allocation of resources more. The key technologies of cross media intelligence mainly focus on intelligent information retrieval, analysis and reasoning, knowledge map construction, and intelligent storage. Convolutional neural network (CNN), as one of the representative algorithms of deep learning, plays an important role in retrieving landscape data and extracting image and text features across media. Further retrieval of media data, in-depth text processing, and image feature data extraction are realized by using deep learning technology, and comprehensive in-depth analysis is carried out by combining landscape plane images, three-dimensional images, and vector information in GIS technology. Provide quantitative information for the evaluation system of human landscape, economy, history, and region, so as to formulate a scientific and reasonable performance evaluation system.

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Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation

The construction industry itself has led to a large amount of energy consumption, and this paper is based on genetic algorithm to optimize the planning and design of rural buildings with energy saving and multi-objective. Firstly, the multi-objective optimization problem and Pareto concept are discussed, the NSGA-II algorithm is applied to solve the problem of building energy saving integrated optimization, and the algorithm implementation of building energy saving integrated optimization design based on NSGA-II algorithm is given. Use incremental costs and incremental benefits as objective functions and make relevant assumptions to keep the model realistic while simplifying calculations. Second, a series of constraint functions are set up to ensure the superiority of the results. By constructing model assumptions, objective functions and constraint functions, the energy-saving optimization model is formed and solved by genetic algorithm. From the optimization results, it can be seen that the energy consumption of all the design schemes in the Pareto solution set is between 25.0 KWh/m2∼31.7 KWh/m2. Therefore, in the selection of architectural planning and design schemes, we should avoid simply pursuing low energy consumption and high comfort, and also use comprehensive consideration of economic costs to choose the most cost-effective scheme.

Multi-objective optimization of building planning energy saving based on genetic algorithm

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Semantic-Driven Paradigm Shift in Campus Guide Design Leveraging the KE-AIGC Framework

Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation

Multi-objective optimization of building planning energy saving based on genetic algorithm