Track 1.1: Integrated Growth and Intelligent Urban Systems

Urban expansion continues to reshape cities worldwide, presenting critical challenges for sustainable development. This study investigates urban growth from 2014 to 2024 in Riyadh, London, and Seoul using Landsat 8 and 9 surface reflectance imagery. Four spectral indices (NDVI, NDBI, BUI, and IBI) were applied in Google Earth Engine to classify built-up and non-built-up areas. The selected cities represent distinct urbanization trajectories: rapid peripheral expansion in Riyadh, policy-driven infill in London, and a mix of redevelopment and sprawl in Seoul. This comparative analysis highlights how spatial, political, and environmental contexts influence patterns of urban transformation across global cities. This study contributes directly to Track 1: Sustainable Urban Growth in a World of Multiple Crises by offering a geospatial analysis of urban land change in response to rapid development, policy regulation, and densification needs. Riyadh’s extensive horizontal expansion aligns with national development strategies under Vision 2030, raising questions of infrastructure and resource sustainability. London showcases minimal sprawl due to long-standing spatial policies like the Metropolitan Green Belt, with growth occurring primarily through inner-city regeneration. Seoul reflects a hybrid model simultaneously redeveloping central zones and expanding toward its metropolitan fringes. These findings align with ISOCARP’s call to explore integrated, context-sensitive planning solutions amid volatility and complexity. By applying remote sensing and data visualization methods, the study enhances the evidence base available to planners and urban policymakers. It also underscores the importance of locally calibrated indicators and classification methods to ensure accurate urban growth monitoring. The ability to track and visualize built-up changes at scale promotes dialogue on sustainable land use, informed planning responses, and adaptive development pathways for both mature and emerging urban systems. The study provides a practical and scalable framework for long-term urban monitoring using open-access satellite data and cloud-based tools. Through consistent application of four spectral indices, annual classifications were produced for built-up and non-built-up land, enabling planners to quantify and visualize urban growth trajectories. The inclusion of gain/loss analysis and index comparison charts allows for nuanced interpretations of land transformation over time essential for identifying densification patterns, sprawl zones, and redevelopment trends. Each city’s case reveals key insights for policy and planning. Riyadh’s rapid expansion, particularly along new corridors, signals the need for integrated infrastructure planning, environmental buffering, and land-use management to mitigate unchecked sprawl. London’s infill-led growth highlights the importance of maintaining green space integrity while enabling urban intensification. Seoul’s dual pattern of vertical redevelopment and peripheral expansion demonstrates the balancing act between density, livability, and transit-oriented development. Moreover, the study highlights methodological challenges such as vegetation interference in urban classification (especially in green cities like London and Seoul) and the critical role of threshold calibration. These technical considerations are essential for practitioners aiming to deploy Earth observation tools for decision support. The results also have implications for Sustainable Development Goal 11 (Sustainable Cities and Communities), offering a reproducible method to assess urban growth against national and local development plans. Urban managers and policy developers can use such data to anticipate infrastructure demands, evaluate environmental pressures, and align land use with resilience targets. Ultimately, this remote sensing-based methodology offers a cost-effective, repeatable approach to urban diagnostics, empowering planners in both developed and developing contexts to make informed, evidence-based decisions. It encourages a shift from reactive governance to proactive planning through spatial intelligence, addressing the urgent global need for sustainable, equitable urban transformation.

Background: With the acceleration of technological change, rapid urbanization, and growing regional disparities, innovation corridors have become important spatial strategies for promoting regional collaborative innovation. This study focuses on the Nanjing-Zhenjiang G312 Innovation Corridor, which covers seven municipal districts in Jiangsu Province, China. It explores how the spatial organization of innovation elements, viewed through the lens of ecological niche theory, can support resilient, inclusive, and sustainable growth in both urban and peri-urban areas. Relevance to the congress themes: This research aligns closely with the congress theme of “Sustainable Urban Growth in a World of Multiple Crises”. It integrates ecological thinking into spatial planning and offers a structured approach to reconfiguring innovation corridors in the face of volatility, uncertainty, complexity, and ambiguity (VUCA). The use of ecological niche theory reveals how innovative actors and spatial environments influence each other over time. This interaction helps cities and surrounding regions respond to rapid change while promoting equitable and balanced development. The study highlights the importance of inclusive spatial structures, efficient innovation clustering, and strong functional linkages. These elements are essential for managing growth, reducing spatial inequality, and generating shared regional value. It also emphasizes the need for collaboration at different spatial levels, from the corridor scale to the municipal and interregional levels. Well-designed innovation ecosystems can significantly improve regional resilience and enhance innovation performance. Contribution to planning or policy-making practice: This study provides a useful framework for planning and policy-making in the field of regional innovation. It applies a multidimensional ecological niche approach to analyze and guide the spatial organization of innovation corridors. Using the Nanjing–Zhenjiang G312 Innovation Corridor as a case, the research identifies five types of innovation communities. These include core hubs, production communities, application communities, nodes, and peripheral communities. Each type has distinct spatial roles and patterns of collaborative innovation. The findings show clear differences in innovation density, connectivity, and cooperation across spatial scales. The study proposes targeted spatial strategies to support more balanced development. These include promoting multi-core spatial networks, building stronger institutional support systems, and improving the connectivity of innovation platforms. Such strategies can address existing inefficiencies and improve coordination across both core and peripheral areas. In addition, the study outlines planning recommendations for improving long-distance innovation collaboration and streamlining resource flows within the corridor. It also calls for better alignment between infrastructure investment, land use planning, and innovation policy. These insights contribute to building resilient and inclusive innovation ecosystems. While based on the Jiangsu context, the findings are also relevant to international regions seeking to integrate innovation-led growth with sustainable urban and regional development.

Under the urgent need for optimizing urban spatial structure and precise allocation of public services, in-depth exploration of the spatiotemporal activity characteristics of urban populations has become an important issue in urban planning and governance. New data such as mobile signaling, social media, and body sensors provide high-precision observation methods for characterizing individual commuting, consumption, social and other behavioral patterns at the urban scale. Through spatiotemporal big data, breaking through the limitations of traditional physical spatial analysis, it helps to capture and grasp the micro trajectories of crowd activities and the dynamic characteristics of spatiotemporal activities. Based on this, this study takes the Jiangdu main urban area of Yangzhou City, China as the object, and uses LBS data to conduct digital identification of crowd activities according to three types of flexible activity point recognition: activity stop point recognition based on ST-DBSCAN algorithm, residence and employment point recognition based on spatiotemporal anchoring, and potential activity space recognition. On this basis, a multi-dimensional spatiotemporal attribute measurement index system for the population is constructed from two dimensions: "stable residence and employment" and "flexible travel". The stable residence and employment attribute reflects the stable and regular characteristics of urban population's residence and employment behavior, including indicators of residential area, employment area, residence period, and commuting distance; Flexible travel attributes are used to characterize the differentiated needs of urban populations for various public services, including travel distance, activity frequency, type diversity, and type preference indicators. Furthermore, through K-prototype clustering, 16751 effective users in the main urban area of Jiangdu were divided into 5 typical groups. Perform secondary feature analysis on the multidimensional spatiotemporal attributes of the population obtained from clustering, including representative and common features, and compare the differences in spatiotemporal activities of various populations. Research has shown that combining LBS data with multiple algorithms can effectively measure the multidimensional spatiotemporal attributes of the population and achieve accurate clustering. This research result constructs a method for dividing population groups from the perspective of spatiotemporal dynamics of crowd activities, providing data support and theoretical reference for optimizing urban work life balance, public service facility layout, etc., which has important practical significance for improving urban governance level.

The contemporary urban environment is confronted with numerous challenges stemming from rapid urban expansion and substantial population growth. The capacity of megacities to evolve into sustainable urban centres is contingent upon their ability to procure, distribute and administer energy and material resources. The identification and anticipation of the mechanisms underpinning trends in urban extent and energy consumption patterns is a pivotal concern for sustainable urban development. The analysis of this study proposes a novel perspective on the recognition of urban energy use patterns: The open-source data, including demographic and economic data, was analysed to establish the law and evolution of urban development scale. This was then coupled with energy use data to initially explore the correlation factors of energy use and prepare for the establishment of the conceptual framework of urban-scale energy use. In accordance with Zipf's law, the objective is to expand the scale law and establish a framework for the urban building scale and energy use scale. This framework is intended to directly reflect the law of urban building scale and its energy use scale, and to visually characterise the energy use and its direct influencing factors. The scale law is expanded upon based on Zipf's law, thereby establishing a framework of urban building scale-energy use. This framework is capable of directly reflecting the law of urban building scale and its energy use scale, and visually characterising the energy use and its direct influence factors. The data required for the study should be easily accessible, authoritative and credible, and should originate from China's statistical yearbooks, building footprints, land use and other geospatial data from the past ten years. The extant data and analysis indicate a convergence in the development of city size, with cities of similar size demonstrating analogous scalar law size. However, the energy use pattern of cities exhibits heterogeneity, attributable to factors such as differences in climate zone and economic conditions. This heterogeneity may also be attributed to policy differences, particularly in cities of different sizes within the same climatic zone. This phenomenon may be attributed to variations in economic conditions and policies. Subsequent studies will explore the feasibility and credibility of the framework in depth, and select a sufficient sample of cities to validate the practical value of the concept.

Amid a convergence of climate crises, social inequalities, and resource pressures, urban growth is accelerating. In cities such as Bengaluru and Berlin, which are each facing unique yet interconnected challenges, digital technologies are emerging as powerful tools for aligning urban development with sustainability goals. While Bengaluru faces acute stress from rapid urban expansion, environmental degradation and infrastructural gaps, Berlin is navigating post-growth dynamics, ecological restoration and social integration. This paper explores how digital urbanism — using data, AI and smart governance — can help cities to rethink how they create ecological value and to build inclusive, adaptive strategies for long-term resilience. ISOCARP 2025 advocates bold, integrated urban planning approaches in a VUCA world. This paper contributes to Track 1 by examining how digital urbanism can enhance sustainable growth strategies in regions facing intense urbanisation and climate stress. By connecting Bengaluru's megacity pressures with Berlin's ecological and participatory planning innovations, the study offers a comparative perspective on how digital tools, such as urban digital twins, ecological footprint mapping and participatory data platforms, can inform better spatial planning and decision-making. It specifically addresses how digital ecosystems can promote compact, liveable density, protect biodiversity, and reinforce urban-rural connections. This directly contributes to ISOCARP’s agenda for resilience, inclusivity, and balanced regional development. The paper outlines a policy-oriented framework for incorporating digital ecological intelligence into urban growth strategies. It presents three key interventions: 1. Digital Ecological Zoning: Using AI and real-time data, planners can map and monitor ecological assets like urban forests, wetlands, agricultural edges, and align growth corridors to minimize environmental impacts. Bengaluru’s peri-urban expansion zones offer a testbed for these models, while Berlin’s Spree corridor provides insights on urban nature restoration. 2. Urban-Rural Synergy Platforms: By leveraging open data and decentralized digital platforms, cities can coordinate resource flows (food, water, labour and energy) between urban cores and surrounding rural areas. This helps create circular, self-reliant regions and reduces dependency on extractive growth models. 3. Inclusive Digital Participation: Smart urban governance must prioritize transparency and inclusion. Tools such as community mapping apps, participatory digital budgeting, and spatial equity dashboards empower marginalized communities to shape urban futures. These approaches are tested in both Berlin’s civic tech ecosystem and Bengaluru’s emerging data commons movement. The paper concludes with a strategic roadmap that integrates digital tools into regional master plans, climate action frameworks, and zoning policies. It argues that cities must not merely digitize but ecologise their digital infrastructures to build adaptive, just, and regenerative urban futures. Drawing lessons from both the contexts, the study advocates for translocal knowledge-sharing and digital governance as a cornerstone of sustainable, multi-scalar urban planning.