Ioannis Kavouras

National Technical University of Athens

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Nikolaos Doulamis

National Technical University of Athens

Eftychios Protopapadakis

National Technical University of Athens

Emmanuel Sardis

National Technical University of Athens

Anastasios Doulamis

National Technical University of Athens

Ioannis Rallis

National Technical University of Athens

Maria Kaselimi

National Technical University of Athens

Anastasios Doulamis

National Technical University of Athens

Dimitra Zouli

National Technical University of Athens

ΠΓ

Παναγιώτα Γαλιατσάτου

Aristotle University of Thessaloniki

Nikolaos Bakalos

National Technical University of Athens

Cooperative Institutions

National Technical University of Athens

Institute of Communication and Computer Systems

National and Kapodistrian University of Athens

Aristotle University of Thessaloniki

Technical University of Crete

Exodus (Greece)

University of Crete

EYDAP (Greece)

Creative Systems Engineering (Greece)

University of the Aegean

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A Low-Cost Gamified Urban Planning Methodology Enhanced with Co-Creation and Participatory Approaches

Sustainability (2023)

Ioannis Kavouras Emmanuel Sardis Eftychios Protopapadakis Ioannis Rallis Anastasios Doulamis

Targeted nature-based small-scale interventions is an approach commonly adopted by urban developers. The public acceptance of their implementation could be improved by participation, emphasizing residents or shopkeepers located close to the areas of interest. In this work, we propose a methodology that combines 3D technology, based on open data sources, user-generated content, 3D software and game engines for both minimizing the time and cost of the whole planning process and enhancing citizen participation. The proposed schemes are demonstrated in Piraeus (Greece) and Gladsaxe (Denmark). The core findings can be summarized as follows: (a) the time and cost are minimized by using online databases, (b) the gamification of the planning process enhances the decision making process and (c) the interactivity provided by the game engine inspired the participation of non-experts in the planning process (co-creation and co-evaluation), which decentralizes and democratizes the final planning solution.

Interactivity

Participatory planning

Public Participation

10.3390/su15032297

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Multi-scale Intervention Planning based on Generative Design

arXiv (Cornell University) (2024)

Ioannis Kavouras Ioannis Rallis Emmanuel Sardis Eftychios Protopapadakis Anastasios Doulamis

The scarcity of green spaces, in urban environments, consists a critical challenge. There are multiple adverse effects, impacting the health and well-being of the citizens. Small scale interventions, e.g. pocket parks, is a viable solution, but comes with multiple constraints, involving the design and implementation over a specific area. In this study, we harness the capabilities of generative AI for multi-scale intervention planning, focusing on nature based solutions. By leveraging image-to-image and image inpainting algorithms, we propose a methodology to address the green space deficit in urban areas. Focusing on two alleys in Thessaloniki, where greenery is lacking, we demonstrate the efficacy of our approach in visualizing NBS interventions. Our findings underscore the transformative potential of emerging technologies in shaping the future of urban intervention planning processes.

10.48550/arxiv.2404.15492

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Ανάπτυξη πλατφόρμας οπτικοποίησης τριδιάστατων σημείων αρθρώσεων χορευτικών κινήσεων

Ιωάννης Κάβουρας Ioannis Kavouras

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Ocean-DC: An analysis ready data cube framework for environmental and climate change monitoring over the port areas

Ioannis Kavouras Ioannis Rallis Nikolaos Doulamis Anastasios Doulamis

The environmental hazards and climate change effects causes serious problems in land and coastal areas. A solution to this problem can be the periodic monitoring over critical areas, like coastal region with heavy industrial activity (i.e., ship-buildings) or areas where a disaster (i.e., oil-spill) has occurred. Today there are several Earth and non-Earth Observation data available from several data providers. These data are huge in size and usually it is needed to combine several data from multiple sources (i.e., data with format differences) for a more effective evaluation. For addressing these issues, this work proposes the Ocean-DC framework as a solution in data harmonization and homogenization. A strong advantage of this Data Cube implementation is the generation of a single NetCDF product that contains Earth Observation data of several data types (i.e., Landsat-8 and Sentinel-2). To evaluate the effectiveness and efficiency of the Ocean-DC implementation, it is examined a case study of an oil-spill in Saronic gulf in September of 2017. The generated 4D Data Cube considers both Landsat-8,9 and Sentinel-2 products for a time-series analysis, before, during, and after the oil-spill event. The Ocean-DC framework successfully generated a NetCDF product, containing all the necessary remote sensing products for monitoring the oil-spill disaster in the Saronic gulf.

Port (circuit theory)

Cube (algebra)

10.1145/3652037.3663913

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A Dilemma-Based Learning to Rank Approach for Generative Design in Urban Architectural Regeneration

Ioannis Kavouras Ioannis Rallis Dimitra Zouli Emmanuel Sardis Anastasios Doulamis

Continuous urbanization combined with the negative impact of climate change degrades the daily life in urban environments. The architectural and urban design based on Nature Based Solutions is a forthcoming solution to this problem. However, such designs take time and cost due to architectural limitation and difficulties in the early design phases. For this reason, it is a crucial necessity to provide architects and urban planners with innovative tools capable of suggesting inspiring and brainstorming ideas. In this work, we propose a full-fledged Generative Design System framework based on the recently introduced Artificial Intelligence Generated Images technology and learning to rank algorithms. This framework can generate a huge number of image solutions, that can inspire architects and urban planners and accelerate drastically the early design stages. However, the huge number of images is difficult to evaluate by a single architect and creates a big data analysis problem. To address this problem, in this work, we define the dilemma-based learning problem and utilize learning to rank and smart bubble sorting algorithms to rank the generated image solutions, from the most to least preferred by the user. Finally, the proposed methodology used in a real case study demonstration, which indicated useful insights about the utilization of these technologies in urban design applications and signified the possibilities and limitations.

Rank (graph theory)

Generative Design

Urban Regeneration

10.2139/ssrn.4838596

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Networking Solutions for the Evaluation of Nature-Based Interventions in Cities

Stelios Kalogridis Emmanuel Sardis Eftychios Protopapadakis Anastasios Doulamis Ioannis Kavouras

10.1109/metrolivenv60384.2024.10615392

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Effectiveness of Open-Source Solutions for Limited Scale Interventions Planning

Lecture notes in networks and systems (2022)

Ioannis Kavouras Emmanuel Sardis Eftychios Protopapadakis Anastasios Doulamis

Representation

10.1007/978-3-031-17601-2_11

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COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level

Sensors (2022)

Ioannis Kavouras Maria Kaselimi Eftychios Protopapadakis Nikolaos Bakalos Nikolaos Doulamis

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.

Pandemic

Closure (psychology)

10.3390/s22103658

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Multi-scale Intervention Planning Based on Generative Design

Lecture notes in computer science (2024)

Ioannis Kavouras Ioannis Rallis Emmanuel Sardis Eftychios Protopapadakis Anastasios Doulamis

10.1007/978-3-031-63031-6_20

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Assessing the Lockdown Effects on Air Quality during COVID-19 Era

arXiv (Cornell University) (2021)

Ioannis Kavouras Eftychios Protopapadakis Maria Kaselimia Emmanuel Sardis Nikolaos Doulamis

In this work we investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2). The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome). Available data on pollutant factors were obtained using global satellite observations. The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The second part of the analysis employed a variety of machine learning tools, utilized for estimating the concentration of each pollutant, two days ahead. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and that it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities.

Nitrogen dioxide

10.48550/arxiv.2106.13750

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