Globalization connects cultures and economies, influencing urban structures, land use, and building functions. It has influenced the Arab world's transformation under globalized development, but the state of the art on urban typo-morphology in the Middle East remains inadequate. This paper explores changing typo-morphological characteristics under globalization through the cases of Amman and Irbid. It analyzes building use typologies for 1953, 1981, 2000, and 2024 using satellite images, interviews, and GIS-based spatial analysis across three spatial scales—city, zone, and plot. To enhance our results, the Analytic Hierarchy Process (AHP) was applied to quantify and rank factors influencing typo-morphology changes. The results show that Jordan experienced three stages of globalization: pre-globalization (1930s–1950s), early globalization (1960s–1980s), and fast globalization (1980s–present). Six key factors have driven typo-morphology changes under globalization: successive migrations; the 2000–2008 economic boom; refugee camp evolution; Middle East wars; technological development; and the 2009 global economic crisis. The first three are the key drivers for Amman and Irbid's morphological evolution. These findings demonstrate how economic and social forces shape urban form over seven decades. Typo-morphology serves as an effective analytical approach for understanding globalization's impact on city evolution. This approach, integrating the GIS–AHP framework, introduces a quantifiable dimension to urban typo-morphological analysis, providing a novel framework for examining globalization's spatial impacts in Middle Eastern cities.

Urban green space zero-waste design faces challenges such as insufficient lifecycle waste reduction, inefficient interdisciplinary knowledge integration, disconnection between site data and design decision-making, and over-reliance on individual experience in design schemes. Traditional experience-driven design models are increasingly inadequate to support the demands for refined and intelligent transformation. To address these issues, this study proposes a Perception–Knowledge–Decision-Making framework driven by multi-source sensing data for the zero-waste design of urban green spaces. The framework collects multidimensional site data through drone imaging sensors, environmental sensors, and laser scanning technology. The study constructs a knowledge graph that semantically links four core entities: strategy, performance, case, and industrial-chain information. Stored in the Neo4j graph database, it enables dynamic knowledge association and efficient retrieval. An intelligent auxiliary decision-making tool is developed by combining knowledge graph-enhanced retrieval and intelligent agent technologies, thereby improving the capabilities for identifying, reasoning, and matching strategies for complex design problems. A simulation-based case study at a community garden in Shanghai was conducted to demonstrate the feasibility of the proposed framework, revealing its potential to improve waste resource utilization matching, design efficiency, and resource utilization effectiveness. This study presents a proof-of-concept framework and provides a theoretical pathway and methodological exploration for sustainable landscape design supported by cutting-edge sensing technologies.