Microscopic trace materials, such as pollen, are an important category of forensic evidence recovered during investigations. As an environmentally ubiquitous substance that can attach to various surfaces, pollen enables the linking of objects and people in space and time. In this study, we assessed the extent to which the search space could be reduced using simulated pollen signatures. These signatures were compiled by randomly selecting pairs of geographic coordinates on the Earth's terrestrial land and querying the Global Biodiversity Information Facility (GBIF) database to identify plant taxa within 50 meters of the coordinates. These taxa were then treated as the parent taxa of the pollen, simulating the hypothetical attachment of pollen signatures to objects or individuals. For each identified pollen taxon, we modeled habitat suitability for the parent plant taxa and combined the spatial distributions to refine the geolocation search area. Since the actual coordinates for these locations of interest were known, we were able to evaluate the global performance of the search space reduction under the assumption of an extreme constraint that no other contextual information was available.
The prevalence of 3D city models in urban applications is increasing due to their lightweight and flexibility, making them adaptable to various applications. However, effective data interoperability remains an issue. Managing 3D city models within a database can improve urban data management applications such as data enrichment and efficient querying. Motivated by the need for better interoperability of 3D city models, this paper proposes a novel method for storing CityJSON using the concept of a multi-model graph database as a foundation for enriching its semantics. The proposed approach involves decomposing CityJSON objects into smaller JSON components, which are then abstracted into graph elements. Parent-child and other relationship attributes are modelled to capture the hierarchical and associative structures of the CityJSON data. A specific programme is employed to preprocess CityJSON data based on several conditions before being loaded into the graph database. Our multi-model approach allows three types of queries: document, graph, and hybrid. The latter combines both document and graph query. Comparative evaluation against relational databases demonstrates that the proposed method outperforms in terms of query performance. The improved query performance is attributed to the advantage of graph database that reduces the need for joins and the ability to efficiently index and navigate JSON data. The findings of this study establish a foundation for semantic enrichment of 3D city models to improve interoperability and support advanced urban data management.
Inundated by the rapidly expanding AI research nowadays, the research community requires more effective research data management than ever. A key challenge lies in the evolving nature of concepts embedded in the growing body of research publications. As concepts evolve over time (e.g., keywords like global warming become more commonly referred to as climate change), past research may become harder to find and interpret in a modern context. This phenomenon, known as concept drift, affects how research topics and keywords are understood, categorized, and retrieved. Beyond temporal drift, such variations also occur across geographic space, reflecting differences in local policies, research priorities, and so forth. In this work, we introduce the notion of spatio-temporal concept drift to capture how concepts in scientific texts evolve across both space and time. Using a scientometric dataset in geographic information science, we detect how research keywords drifted across countries and years using word embeddings. By detecting spatio-temporal concept drift, we can better align archival research and bridge regional differences, ensuring scientific knowledge remains findable and interoperable within evolving research landscapes.
In recent years, large pre-trained models, commonly referred to as foundation models, have become increasingly popular for various tasks leveraging transfer learning. This trend has expanded to remote sensing, where transformer-based foundation models such as Prithvi, msGFM, and SatSwinMAE have been utilized for a range of applications. While these transformer-based models, particularly the Prithvi model, exhibit strong generalization capabilities, they have limitations on capturing fine-grained details compared to convolutional neural network architectures like U-Net in segmentation tasks. In this paper, we propose a novel architecture, U-Prithvi, which combines the strengths of the Prithvi transformer with those of U-Net. We introduce a RandomHalfMaskLayer to ensure balanced learning from both models during training. Our approach is evaluated on the Sen1Floods11 dataset for flood inundation mapping, and experimental results demonstrate better performance of U-Prithvi over both individual models, achieving improved performance on out-of-sample data. While this principle is illustrated using the Prithvi model, it is easily adaptable to other foundation models.
Over the past decade, micromobility services, particularly electric vehicles for personal short-distance trips, have experienced significant growth. Major cities around the world now host extensive fleets of vehicles available for short-term public rental. While previous research has examined usage patterns within and between a few select cities, large, open, and publicly accessible data sets for analyzing mobility across multiple cities are extremely limited. I have collected, curated, and aggregated over twenty million e-scooter and e-bicycle trips across five major cities and are openly releasing aggregated data for use by mobility and sustainable transport researchers, urban planners, and policymakers. To accompany these data, I developed MODAP (Micromobility Open Data & Analytics Platform), a geovisual analytics tool that empowers researchers to explore the temporal and regional patterns of e-mobility trips within our open data set and download the data for offline analysis. My objective is to foster further research into city-scale mobility patterns and to equip researchers, community members, and policymakers with the necessary tools to conduct this work.
