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S.R.H.W. Tew
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Urban resilience is an evergrowing issue as cities face environmental, social and infrastructural challenges. Green infrastructure is currently being used as one of the main approaches in achieving urban resilience by providing multifunctional benefits, such as improving microclimate, enhancing biodiversity, and increasing accessibility to nature. However, through this process the evaluation and optimisation of green infrastructure at the street level remains a methodological challenge, particularly in the case of integrating multi dimensional indicators into predictive models using nature based solutions.
This study develops a machine learning based framework in order to assess and optimise green infrastructure in urban areas at a street level scale. Through the use of publicly available spatial datasets on environmental, biodiversity and morphological factors, the project constructs a comprehensive dataset incorporating factors such as urban heat islands, green infrastructure distribution, and green space accessibility.
Modelling techniques such as gradient boosting regression and random forest regression are employed as a regression technique to predict urban resilience related targets. These will provide insights into how resilient different areas of Rotterdam can be. By focusing on the study at a street level approach, this study aims to offer a comprehensive understanding of green infrastructure effectiveness at a high resolution, providing urban planners with data driven recommendations for optimising and implementing green infrastructure solutions to areas in need.
The findings of this research aim to contribute by bridging gaps in green infrastructure assessment by integrating geospatial data, network based spatial analysis, with machine learning, to assist in decision making to make urban areas more resilient. ...
This study develops a machine learning based framework in order to assess and optimise green infrastructure in urban areas at a street level scale. Through the use of publicly available spatial datasets on environmental, biodiversity and morphological factors, the project constructs a comprehensive dataset incorporating factors such as urban heat islands, green infrastructure distribution, and green space accessibility.
Modelling techniques such as gradient boosting regression and random forest regression are employed as a regression technique to predict urban resilience related targets. These will provide insights into how resilient different areas of Rotterdam can be. By focusing on the study at a street level approach, this study aims to offer a comprehensive understanding of green infrastructure effectiveness at a high resolution, providing urban planners with data driven recommendations for optimising and implementing green infrastructure solutions to areas in need.
The findings of this research aim to contribute by bridging gaps in green infrastructure assessment by integrating geospatial data, network based spatial analysis, with machine learning, to assist in decision making to make urban areas more resilient. ...
Urban resilience is an evergrowing issue as cities face environmental, social and infrastructural challenges. Green infrastructure is currently being used as one of the main approaches in achieving urban resilience by providing multifunctional benefits, such as improving microclimate, enhancing biodiversity, and increasing accessibility to nature. However, through this process the evaluation and optimisation of green infrastructure at the street level remains a methodological challenge, particularly in the case of integrating multi dimensional indicators into predictive models using nature based solutions.
This study develops a machine learning based framework in order to assess and optimise green infrastructure in urban areas at a street level scale. Through the use of publicly available spatial datasets on environmental, biodiversity and morphological factors, the project constructs a comprehensive dataset incorporating factors such as urban heat islands, green infrastructure distribution, and green space accessibility.
Modelling techniques such as gradient boosting regression and random forest regression are employed as a regression technique to predict urban resilience related targets. These will provide insights into how resilient different areas of Rotterdam can be. By focusing on the study at a street level approach, this study aims to offer a comprehensive understanding of green infrastructure effectiveness at a high resolution, providing urban planners with data driven recommendations for optimising and implementing green infrastructure solutions to areas in need.
The findings of this research aim to contribute by bridging gaps in green infrastructure assessment by integrating geospatial data, network based spatial analysis, with machine learning, to assist in decision making to make urban areas more resilient.
This study develops a machine learning based framework in order to assess and optimise green infrastructure in urban areas at a street level scale. Through the use of publicly available spatial datasets on environmental, biodiversity and morphological factors, the project constructs a comprehensive dataset incorporating factors such as urban heat islands, green infrastructure distribution, and green space accessibility.
Modelling techniques such as gradient boosting regression and random forest regression are employed as a regression technique to predict urban resilience related targets. These will provide insights into how resilient different areas of Rotterdam can be. By focusing on the study at a street level approach, this study aims to offer a comprehensive understanding of green infrastructure effectiveness at a high resolution, providing urban planners with data driven recommendations for optimising and implementing green infrastructure solutions to areas in need.
The findings of this research aim to contribute by bridging gaps in green infrastructure assessment by integrating geospatial data, network based spatial analysis, with machine learning, to assist in decision making to make urban areas more resilient.
Student report
(2024)
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M.M. van Arnhem, Q. YANG, S.R.H.W. Tew, X. Zhao, W.H.J. Kahn, E. Verbree, Y.Y. Yu, Florent Poux
In recent years, the need for heritage preservation and reconstruction has become evident as many mature buildings face the risk of deterioration, damage or loss due to factors such as urban development, environmental weathering as well as outdated infrastructure. This urgency has created surges of significant interest to find sustainable methods of heritage preservation. The rise of emerging digital technologies has introduced a multitude of innovative methods for storing, analysing, and showcasing building data.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels. ...
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels. ...
In recent years, the need for heritage preservation and reconstruction has become evident as many mature buildings face the risk of deterioration, damage or loss due to factors such as urban development, environmental weathering as well as outdated infrastructure. This urgency has created surges of significant interest to find sustainable methods of heritage preservation. The rise of emerging digital technologies has introduced a multitude of innovative methods for storing, analysing, and showcasing building data.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels.