Publications

@article{paterakis_osmosis_2025,

title = {Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments},

author = {I. Paterakis and N. Manoudaki},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014516125&doi=10.1177%2F14780771251356526&partnerID=40&md5=4bbcb09440d91899cb7d2d5d0c852507},

doi = {10.1177/14780771251356526},

issn = {14780771 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Architectural Computing},

abstract = {This work contributes to the evolving discourse on biodigital architecture by examining how generative artificial intelligence (AI) and extended reality (XR) systems can be combined to create immersive urban environments. Focusing on the case study of “Osmosis”, a series of large-scale public installations, this work proposes a methodological framework for real-time architectural composition in XR using diffusion models and interaction. The project reframes the architectural façade as a semi permeable membrane, through which digital content diffuses in response to environmental and user inputs. By integrating natural language prompts, multimodal input, and AI-generated visual synthesis with projection mapping, Osmosis advances a vision for urban architecture that is interactive, data-driven, and sensorially rich. The work explores new design territories where stochastic form-making and real-time responsiveness intersect, and positions AI as an augmentation of architectural creativity rather than its replacement. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: SAGE Publications Inc.},

keywords = {Architectural design, Architectural environment, Artificial intelligence, Biodigital design, Case-studies, Computational architecture, Computer architecture, Extended reality, generative artificial intelligence, Immersive, Immersive environment, immersive environments, Natural language processing systems, Real- time, Urban environments, urban planning},

pubstate = {published},

tppubtype = {article}

}

@article{wu_geospatial_2024,

title = {Geospatial Big Data: Survey and Challenges},

author = {J. Wu and W. Gan and H. -C. Chao and P. S. Yu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200804056&doi=10.1109%2FJSTARS.2024.3438376&partnerID=40&md5=63c39a7c302e6d9ff055633efab0349a},

doi = {10.1109/JSTARS.2024.3438376},

issn = {21511535 (ISSN); 19391404 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},

volume = {17},

pages = {17007–17020},

abstract = {In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 Elsevier B.V., All rights reserved.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning},

pubstate = {published},

tppubtype = {article}

}