Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion

Area of Science:

• Urban planning and environmental science
• Computer science and artificial intelligence
• Data science and analytics

Background:

• Urban landscaping projects are expanding, increasing the importance of design and management.
• The proliferation of interconnected devices (people, machines, and things) has led to multisource big data and advancements in artificial intelligence.
• Multisource data, integrating diverse information, offers richer insights than single sources, crucial for complex problem-solving.

Purpose of the Study:

• To explore the application of multisource big data intelligence and artificial intelligence in urban landscaping.
• To leverage deep learning techniques for analyzing complex urban environmental data.
• To enhance the definition and management of urban fringe areas using advanced data fusion and AI.

Main Methods:

• Utilizing multisource fusion data, combining information from various sources.
• Applying artificial intelligence (AI) technologies, specifically deep learning algorithms.
• Analyzing large datasets to extract key features and effective information for urban applications.

Main Results:

• Demonstrated the potential of multisource data for providing high-quality information for urban challenges.
• Highlighted the growing significance of AI and deep learning in academic research and practical applications.
• Showcased how AI can process complex data to inform urban planning and management decisions.

Conclusions:

• Multisource big data intelligence and AI are essential for addressing the complexities of modern urban construction and landscaping.
• Deep learning offers a powerful approach to extracting meaningful insights from fused urban data.
• The integration of AI in urban planning can lead to more effective management and definition of urban fringe areas.