The emigration of residents following the Fukushima nuclear accident has resulted in aging and depopulation problems in radiation-contaminated areas. The recovery of affected areas, and even those areas with low radioactive pollution levels, is still heavily affected by this problem. This slow recovery consequently affects immigration patterns. This review aims to present possible factors that have contributed to this dilemma. We first present an overview of the evacuation protocol that was administered in the study area following the Fukushima accident. We then analyze characteristics of the subsequent exodus by comparing population data for both before and after the accident. Based on the findings of existing literature, we identify three causes of emigration: (1) The health risks of living in a low radiation zone are still unknown; (2) The post-disaster psychological disturbance and distrust of government information promotes the emigration of evacuees; (3) an absence of economic vitality and of a leading industry renders the area less attractive to individuals residing outside of the city. Further research is needed on this issue, especially with respect to countermeasures for addressing this problem.

Urban communities are particularly vulnerable to the future demand for food, energy and water, and this vulnerability is further exacerbated by the onset of climate change at local. Solutions need to be found in urban spaces. This article based around urban design practice sees urban agriculture as a key facilitator of nexus thinking, needing water and energy to be productive. Working directly with Urban Living Labs, the project team will co-design new food futures through the moveable nexus, a participatory design support platform to mobilize natural and social resources by integrating multi-disciplinary knowledge and technology. The moveable nexus is co-developed incrementally through a series of design workshops moving around living labs with the engagement of stakeholders. The methodology and the platform will be shared outside the teams so that the knowledge can be mobilized locally and globally.

The evolving concept of ‘Smart Cities’ (SC) is today gaining global momentum in scientific and policy arenas. With the rising potential for jobs and improved lifestyle, these fast-growing cities are witnessing an ever-increasing concentration of populations and economic activities. However, the core aspect of sustainability is often overshadowed in SC by the components of economic growth. In view of the rising water, energy, and food (collectively referred to as WEF) demands in cities and the interlinkages between WEF systems, this study aims to highlight the role of SC in enhancing WEF resource efficiency from a nexus perspective. To study the current trends of SC developments first, a specific case of proposed Nagpur smart city (India) is reviewed based on document analysis and preliminary discussions with local government officials. Thereafter, bibliometric analysis (based on Scopus data) is conducted to establish a comprehensive understanding of existing SC research in varying domains of interlinked WEF systems. While the proposed Nagpur smart city is observably focused on city-centric goals with a high emphasis on infrastructure development, this study underlines the need for environmental resource conservation at a transboundary level. The key opportunities for optimizing the WEF nexus in Nagpur are then highlighted in reference to the bibliometric analysis.

This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and $F_1$ score ($F_1$) = 0.918), and a fair positional accuracy ($\mathrm{root} \mathrm{mean} \mathrm{square} \mathrm{error}$$\left(RMSE\right)$ = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.