Data Science problem: Develop a predictive tool to streamline forest inventory processes for cork oak and holm oak species identification and assessment

Forest management plays a vital role in ensuring sustainable ecosystems and resources, yet traditional forest inventory processes are often time-consuming and resource-intensive. Forest inventories, which involve gathering and analyzing data on forest areas, are central to sustainable forest management efforts.

While modern inventories have begun integrating advanced technological tools such as Geographic Information Systems (GIS), there remains a critical need to optimize these processes, particularly in regions where resources for extensive fieldwork are limited. The AGROGES company faces significant challenges in efficiently managing forest inventories for cork oak (Quercus suber) and holm oak (Quercus ilex) species.

Traditional methods rely heavily on manual fieldwork and statistical analysis, making the process costly and time-consuming. The need for accurate tree counting, species differentiation, crown area calculation, and health assessment requires a more efficient and scalable approach. In this Hackathon, the challenge was to develop a Convolutional Neural Network (CNN) deep learning model capable of analyzing geospatial data to achieve multiple forest management objectives: assessing tree numbers in plots, accurately mapping the presence of both oak species, differentiating between cork oak and holm oak trees, calculating individual tree crown areas for resource assessment, and evaluating tree health and condition to inform sustainable management practices.