Greening the city: an ecosystem-based framework to support planning towards urban sustainability and resilience

Cities are small-scale complex socio-ecological systems, that host around 60% of world population. Ecosystem Services (ES) provided by urban ecosystems offer multiple benefits necessary to cope with present and future urban challenges. These ES include microclimate regulation, runoff control, as well as opportunities for mental and physical recreation, affecting citizen’s health and wellbeing. Creating a balance between urban development, land take containment, climate adaptation and availability of Urban Green Areas and their related benefits, can improve the quality of the lives of the inhabitants, the economic performance of the city and the social justice and cohesion aspects. This work starts analysing current literature around the topic of Ecosystem Services (ES), Green and Blue Infrastructure (GBI) and Nature-based Solutions (NBS) and their integration within current European and International sustainability policies. Then, the thesis focuses on the role of ES, GBI and NBS towards urban sustainability and resilience setting the basis to build the core methodological and conceptual approach of this work. The developed ES-based conceptual approach provides guidance on how to map and assess ES, to better inform policy making and to give the proper value to ES within urban context. The proposed interdisciplinary approach navigates the topic of mapping and assessing ES benefits in terms of regulatory services, with a focus on climate mitigation and adaptation, and cultural services, to enhance wellbeing and justice in urban areas. Last, this thesis proposes a trans-disciplinary and participatory approach to build resilience over time around all relevant urban ES. The two case studies that will be presented in this dissertation, the city of Bologna and the city of Barcelona, have been used to implement, tailor and test the proposed conceptual framework, raising valuable inputs for planning, policies and science.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.