Understanding socio-ecological systems and the discovery, recovery and adaptation of land knowledge are key challenges for sustainable land use. The analysis of sustainable agricultural systems and practices, for instance, requires interdisciplinary and transdisciplinary research and coordinated data acquisition, data integration and analysis. However, datasets, which are acquired using remote sensing, geospatial analysis and simulation techniques, are often limited by narrow disciplinary boundaries and therefore fall short in enabling a holistic approach across multiple domains and scales. In this work, we demonstrate a new workflow for interdisciplinary data acquisition and integration, focusing on terraced vineyards in Tuscany, Italy. We used multi-modal data acquisition and performed data integration via a voxelised point cloud that we term a composite voxel model. The latter facilitates a multi-domain and multi-scale data-integrated approach for advancing the discovery and recovery of land knowledge. This approach enables integration, correlation and analysis of data pertaining to different domains and scales in a single data structure.

Although voxel models have been applied to address diverse problems in computer-aided design processes, their role in multi-domain data integration in digital architecture and planning has not been extensively studied. The primary objective of this study is to map the current state of the art and to identify open questions concerning data structuring, integration, and modeling and design of multi-scale objects and systems in architecture. Focus is placed on types of voxel models that are linked with computer-aided design models. This study utilizes a semi-systematic literature review methodology that combines scoping and narrative methodology to examine different types and uses of voxel models. This is done across a range of disciplines, including architecture, spatial planning, computer vision, geomatics, geosciences, manufacturing, and mechanical and civil engineering. Voxel-model applications can be found in studies addressing generative design, geomatics, material science and computational morphogenesis. A targeted convergence of these approaches can lead to integrative, holistic, data-driven design approaches. We present (1) a summary and systematization of the research results reported in the literature in a novel manner, (2) the identification of research gaps concerning voxel-based data structures for multi-domain and trans-scalar data integration in architectural design and urban planning, and (3) any further research questions.

Rapid urbanization and related land cover and land use changes are primary causes of climate change, and of environmental and ecosystem degradation. Sustainability problems are becoming increasingly complex due to these developments. At the same time vast amounts of data on urbanization, construction and resulting environmental conditions are being generated. Yet it is hardly possible to gain insights for sustainable plan-ning and design at the same rate as data is generated. Moreover, the complexity of compound sustainability problems requires interdisciplinary approaches that address multiple knowledge fields, multiple dynamics and multiple spatial, temporal and functional scales. This raises a question regarding methods and tools available to planners and architects for tackling these complex issues. To address this problem we are developing an interdisciplinary approach, computational framework and related workflows for multi-domain and trans-scalar modelling that integrate planning and design scales. For this article two lines of research were selected. The first focuses on understanding environments for the purpose of discovering, recovering and adapting land knowledge to different conditions and contexts. This entails an analytical data-integrated computational workflow. The second line of research focuses on designing environments and developing an approach and computational workflow for data-integrated planning and design. These two lines converge in a combined analytical and generative data-integrated computational workflow. This combined approach aims for an intense integration of architectures and environments that we call embedded architectures. In this article we discuss the two lines of research, their convergence, and further research questions.

The overarching topic of this article is land-use planning (LUP) for risk mitigation of natural hazards. In this context, landslides are one of the most destructive natural hazards, resulting in significant negative impacts on humans, ecosystems, and environments. This study presents a semi-systematic review of emerging ecogeomorphological principles for LUP to advance the mitigation of landslide risks. By integrating ecological and geomorphological systems, an ecogeomorphological approach offers a novel perspective for tackling landslide risk mitigation. This includes accounting for factors such as water flow accumulation, fractional vegetation cover, and soil erosion, using computational methods, applying artificial intelligence (AI) to process and predict risk, and integrating the internet of things (IoT) to real-time environmental data. We primarily explore the role of ecogeomorphology in fostering sustainable and risk-aware LUP, as well as how landslide research can be applied within LUP to strengthen broader management frameworks. The study reveals much evidence of ecogeomorphological factors in LUP, emphasising the integration of ecology, geomorphology, and hydrology for effective landslide mitigation. With the ongoing shift from traditional to emerging methodologies in risk management, our review addresses the existing research gap by proposing an up-to-date ecogeomorphological framework for practice.

Urban biodiversity is essential for sustainable cities, as it helps address the challenges of environmental degradation, ecosystem loss, species decline, and increased vulnerability to climate hazards, which negatively affect human health and well-being. ECOLOPES (ECOlogical building enveloPES) aims to develop a design approach for multi-species as stakeholders to achieve regenerative urban ecosystems. Integrating the diverse data required for stakeholders and beyond—spanning the life sciences, geography, and architecture—and utilising it for design presents a significant challenge. This paper introduces an ontology-driven approach that utilises ontology-based data management (OBDM) as a framework for integrating diverse data sources, enabling ecologists and architects to design sites and buildings that foster urban biodiversity. OBDM offers a unified view of multiple data sources through an ontology, enabling query and update operations to be performed directly on the integrated data. The proposed ontology, developed in collaboration with domain experts and adhering to Semantic Web and Linked Data best practices, serves as a mediator between life sciences data (e.g., species distribution and habitats) and geometric information (e.g., maps and voxel models of building structures). This integration enables the adaptation of sites, buildings, and geometries, respectively, to create habitats that attract and support urban wildlife, contributing to ecological sustainability. The paper illustrates the practical utility of the ontology through a case study, highlighting its role in guiding building designs that promote species attractiveness and urban biodiversity.

The context for this article is sustainable and ecological green city and building design; the intent is to advance architecture and ecology integration and multi-species design in architecture through the development of a conceptual framework for and computational approach to the early-stage design of ecological building envelopes, which are enclosures of buildings that make provisions for humans, plants, and animals. This entails two research questions: (1) how to integrate architectural and ecological domain knowledge into a conceptual and methodological framework and (2) how to develop a computational workflow and components for the early-stage design of ecological building envelopes. A mixed-method approach was used to develop an ontology-aided generative computational design process that combines computational ontologies, a voxel model, and rule-based processes that generate design variety. The process was developed to support two dominant design cases in architectural practice: masterplan design and building design. This article outlines the underlying key concepts, the computational workflow, and the developed key computational components and summarily indicates validation approaches during the development process. Finally, thoughts on the technical implementation of the computational workflow and components are indicated and further research questions are outlined.