Modelos de dados espácio-temporais e algoritmos para as ciências da terra
Scientists and engineers working in fields such as the environmental sciences, the oceans, climate, or earth sciences have access to massive amounts of geo-referenced data. These data allow monitoring and studying the behavior of objects or events of interest over time, making diagnoses and predictions, etc. These tasks assume the existence of good quality data and methods and tools to analyze the data with little effort. Currently, there are many tools help on managing, processing, and analyzing spatial data, but the same does not happen when one intends to work with spatial data that evolves over time. The project as a whole focuses on the development of models and tools for the processing of spatiotemporal (SPT) data, based on two case studies: environmental engineering and marine ecology. The focus will be on SPT data modeled as 2D and 3D geometries that can change position, shape, or size continuously over time (moving objects). For example, we can model an iceberg as a 3D moving object (thus representing its movement and changes in size and shape over time). This model has advantages over discrete models, particularly when one intends to represent the evolution of geometrically definable objects or events, as it allows for more compact and intuitive representations, and guarantees the independence of the data from the acquisition process. The duration of the project was 12 months, and the strategy consisted of testing solutions and defining guidelines for future research. The team had mind the study of solutions for the areas of databases and GIS, as well as more recent trends, namely machine learning, data stream analysis, and digital twins. The participating institutions are the University of Aveiro, INESC TEC Porto, and the Polytechnic Institute of Leiria. The team consists of six researchers: four from the area of computer engineering and computer science, one from environmental engineering, and another from the area of marine ecology. Professor Justin Solomon, leader of the “Geometric Data Processing” group of the “Computer Science and Artificial Intelligence” laboratory at MIT, will also collaborate with this project.
