The field of geospatial analytics and species classification is moving towards leveraging advanced computational tools and machine learning techniques to improve accuracy and efficiency. Researchers are exploring the use of GPU acceleration to speed up simulations and models, allowing for larger system sizes and more complex analyses. Additionally, the use of pre-trained deep learning models and remote sensing data is becoming increasingly popular for tasks such as land use and land cover classification and tree species classification. These approaches have shown significant improvements in accuracy and can complement traditional methods. Noteworthy papers include: Investigating Different Geo Priors for Image Classification, which evaluates various SINR models as geographical priors for visual classification of species. GPU Acceleration for Faster Evolutionary Spatial Cyclic Game Systems, which presents a GPU-accelerated simulation framework for Evolutionary Spatial Cyclic Games. Deep Pre-trained Time Series Features for Tree Species Classification in the Dutch Forest Inventory, which demonstrates the potential of using deep AI features for data-limited applications like NFI classification.