Urbanization presents significant challenges to disaster management as cities grow and develop, hence increasing their vulnerability to disasters. Disaster resilience is crucial for protecting lives and infrastructure, ensuring economic stability, promoting equality and cohesion, and ensuring the long-term viability of metropolitan regions in these rapidly growing cities. This paper investigates contemporary approaches to creating smart and resilient urban environments through disaster management that emphasize community-based solutions in prioritizing advanced technologies. The key findings of the research include three factors to be accomplished in utilizing technology in community-based disaster management, trust in the crowd, digital divide, and cultural sensitivity. Moreover, the review highlights the significance of the use of smart technologies in improving urban resilience, including but not limited to real-time data-sharing platforms and ML algorithms. Furthermore, it emphasizes the challenges regarding reliability and accuracy in crowdsourced information, stressing the importance of user awareness.

The increasing frequency and severity of disasters in urban areas demand sustainable, smart disaster management strategies to leverage technological advancements. This study provides a comprehensive review of mobile apps for disaster awareness available in the Google Play Store, with a particular emphasis on addressing flood disaster readiness and response. Mobile apps have become indispensable tools for disseminating immediate notifications, facilitating emergency communication, and coordinating response activities. A total of 77 mobile apps in the Google Play Store were identified and evaluated using a systematic search. The evaluation criteria included user ratings, download counts, and key crisis management functionalities such as real-time alerts, emergency contact directories, preparedness checklists, and user reporting capabilities. The findings emphasised the following: (a) the importance of integrating cutting-edge technologies, i.e., AI and IoT, to enhance functionality, accuracy, and capacity in mobile applications; (b) the use of crowdsourcing as a valuable mechanism for enriching inclusive and responsible data; (c) enabling timely updates and fostering community engagement; and (d) establishing agency engagements, gamified elements, and real-time reciprocal communication tools, i.e., push-to-talk features to ensure the long-term sustainability of mobile apps. By incorporating these insights, disaster management apps can significantly enhance community resilience and improve the effectiveness of responding to natural disasters in this digital age.

Mostly, public places are planned and designed by professionals rather engaging the community in the design process. Even if the community engaged, the engagement process was limited to hand drawings, manual mappings, or public discussions, which limited the general public to visualize and well-communicate their aspirations with the professionals. Against this backdrop, this study intends to develop a mobile application called “CityBuildAR”, which uses Augmented Reality technology that allows the end user to visualize their public spaces in a way they want. CityBuildAR was developed by the authors using the Unity Real-Time Development Platform, and the app was developed for an Android Operating System. The app was used to assess community interests in designing open spaces by categorizing participants into three groups: those with limited, average, and professional knowledge of space design. The open cafeteria of the University of Moratuwa, Sri Lanka served as the testbed for this study. The study findings revealed that: (a) Mobile Augmented Reality is an effective way to engage people with limited knowledge in space design to express their design thinking, (b) Compared to professionals, the general public wanted to have more green elements in the public space; (c) Compared to the professionals, the general public who were not conversant with the designing skills found the app more useful to express their ideas. The study guides urban authorities in their placemaking efforts by introducing a novel approach to effectively capture community ideas for creating inclusive public spaces.

Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot playmate for kids intended to nurture the engagement in STEM (Science, Technology, Engineering and Mathematics) related activities through playing. SiFEB's simplified and interactive hardware and software modules present users without prior experience, with the ability to build robots without going deep in either aspect. Kids can build robots of their interest using attachable hardware modules and related programming components using an easy to understand visual programming language that resembles natural commands. The potential developers may utilize the framework provided, to build hardware and software components to the system expanding the existing capabilities. Preliminary results from the prototypes show that SiFEB has the potential of being an effective learning platform for young children in STEM related areas.