Public Health Inspectors (PHIs) in Sri Lanka are facing a problem of identifying certain mosquito breeding sites since they cannot easily reach places such as roof gutters, overhead water tanks, inaccessible rooftops and cement materials which are capable of retaining water. The goal of a such inspection of suspected sites is to reduce the number of dengue patients by eradicating dengue mosquito habitats. Due to the retention of water in the aforementioned sites for a long period of time, those places tend to be full of lichens. In general, lichens are visible in dark color. This characteristic helps to identify prolonged water retention areas. With the rapid advancement of technology, the drone has been created as one of the most cost effective apparatus to capture the places that a human cannot access. With respect to the aforesaid context, this paper presents a simple and a novel approach to identify mosquito breeding sites via drone images. The proposed approach processes images captured from a drone to identify possible sites where stagnant water may retain and highlights if such areas are apparent within the image. The evaluation process found that the proposed method, produces a satisfactory level of accuracy in identification of possible water retention areas and the final results depend on the drone camera tilt angle and the effect of shadows.

Dengue is one of the rapidly spreading and deadly diseases in Sri Lanka, transmitted by the bite of infected female Aedes mosquitoes. Public Health Inspectors (PHIs) in Sri Lanka are facing a problem of identifying certain dengue mosquito breeding sites which are capable of retaining water. The goal of such inspection of suspected sites is to reduce the number of dengue patients by eradicating dengue mosquito habitats. A drone which has been created with the rapid advancement of technology, is one of the most cost effective apparatus to capture the mosquito breeding sites. It can inspect both accessible and inaccessible places to a human. With respect to the aforesaid context, this paper presents a simple and a novel approach to identify dengue mosquito breeding sites via drone images. The proposed approach captures the images of the water retention areas via a drone and generates the map, marking those areas in the map. The field test found that the proposed method, produces a satisfactory level of accuracy in identifying possible water retention areas and the final results depend on the drone camera tilt angle and the effect of shadows.

Digital tools, especially games, are increasingly important for enabling citizen participation in urban planning. Among these, Minecraft has been widely utilized to engage children, leveraging its virtual environment to represent geospatial data. However, systematic methods for collecting and analyzing player-generated data within Minecraft remain underexplored. Playful Participatory Planning System (P-PPS) framework that transforms player actions (e.g., building, removing, planting) within Minecraft, using OpenStreetMap (OSM) data to create game environments, back into geospatial data for analysis. The framework’s applicability was demonstrated through two case studies, one with 58 schoolchildren and 18 adults in Ireland. The results reveal that schoolchildren, while highly engaged, demonstrated a high density of actions within limited areas, suggesting a need for guidance on spatial distribution and ecological considerations. In contrast, adults prioritized the urban context and exhibited greater spatial consistency in their actions. Challenges emerged in managing online interactions, emphasizing the need for clear guidelines and moderation strategies. This research demonstrates the potential of Minecraft as a platform for participatory urban planning, exploring its use as a collaborative immersive mapping tool.

AI-powered mapping technologies are uniquely positioned to address the challenges of keeping online maps up-to-date in dynamic urban environments. Modern Geospatial Artificial Intelligence (GeoAI) approaches, particularly those utilizing advanced Deep Learning (DL) techniques, have shown significant promise in automating aspects of the Volunteered Geographic Information (VGI) map updating process. This paper introduces DeepMapper, an open-source, GeoAI-based mapping platform designed to automate the VGI mapping workflow with minimal human intervention. By applying Generative Adversarial Networks (GANs) to freely available spatial datasets like OpenStreetMap, DeepMapper enhances the accuracy and completeness of VGI maps. As a web-based tool, DeepMapper is a web-based mapping tool that streamlines/automates much of the traditional VGI map updating workflow. Experiments demonstrate that DeepMapper achieves an accuracy of 92.8%, precision of 90.3%, and recall of 92.0%. The platform also shows potential for testing new GeoAI models, facilitating continuous improvements in how contemporary VGI maps are created and maintained. Despite its successes, challenges remain, particularly in adapting DeepMapper to real-world settings and different architectural styles. Further work will explore the integration of diffusion models for building detection and address non-technical challenges related to geometric accuracy and the adoption of AI-based mapping by communities like OpenStreetMap.