Managed realignment is the landward relocation of flood infrastructure to re-establish tidal exchange on formerly reclaimed land. Managed realignment can be seen as a nature-based flood defence system that combines flood protection by the realigned dike (artificial) and restored saltmarshes (nature-based). So far, research on coastal managed realignment is primarily directed to saltmarsh restoration on formerly reclaimed land. This study focuses on the realigned dikes. The aim of this research is to characterize realigned dikes and to indicate the characteristics that offer opportunities for nature-based flood protection. We categorized 90 European coastal managed realignment projects into two realigned dike groups: (1) Newly built landward dikes and (2) Existing landward dikes of former multiple dike systems. The second group has two subcategories: (2a) Former hinterland dikes and (2b) Realignments within summer polders. For each group we present the realigned dike characteristics of a representative case study. We consider that the use of existing landward dikes or local construction material make realignment more sustainable. From a nature-based flood protection perspective, the presence of an artificial dike is ambiguous. Our results show that targeted and expected saltmarsh restoration at managed realignment does not necessarily result in a greener realigned dike design that suits for combined flood protection with restored saltmarshes. We recommend coastal managers to explicitly take combined flood protection into account in the realigned dike design and steer the topography of the realignment site to facilitate nature-based flood protection and promote surface elevation increase seaward of the realigned dike in response to sea level rise. This makes managed realignment a nature-based flood defence zone for now and for the future.
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In this article, we elaborate on the role of dialogical learning in identity formation in the context of environmental education. First, we distinguish this kind of learning from conditioning and reproductive learning. We also show that identity learning is not self-evident and we point out the role of emotions. Using Dialogical Self Theory, we then suggest that individuals do not have an “identity hierarchy” but a dialogical self that attaches meaning to experiences in both conscious and unconscious ways. We describe the learning process that enables the dialogical self to develop itself, and we elaborate on the characteristics of a good dialogue. We conclude with some remarks expanding room for a dialogue that would foster identity learning. https://doi.org/10.3390/resources5010011 https://www.linkedin.com/in/helenkopnina/
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Manual crack inspection is labor-intensive and impractical at scale, prompting a shift toward AI-based segmentation methods. We present a novel crack segmentation model that leverages the Segment Anything Model 2 (SAM 2) through transfer learning to detect cracks on masonry surfaces. Unlike prior approaches that rely on encoders pretrained for image classification, we fine-tune SAM 2, originally trained for segmentation tasks, by freezing its Hiera encoder and FPN neck, while adapting its prompt encoder, LoRA matrices, and mask decoder for the crack segmentation task. No prompt input is used during training to avoid detection overhead. Our aim is to increase robustness to noise and enhance generalizability across different surface types. This work demonstrates the potential of foundational segmentation models in enabling more reliable and field-ready AI-based crack detection tools.
