As biodiversity databases grow through citizen contributions, they provide valuable insights into species distributions. However, challenges such as sampling biases and the absence of reliable data often limit the accuracy of traditional modelling methods. To address these issues DeepMaxent combines the maximum entropy principle of Maxent with the automatic feature extraction capabilities of neural networks, enabling more robust predictions across species and regions described extensively in this preprint.

DeepMaxent outperforms traditional Maxent and other state-of-the-art SDMs, especially in regions with uneven sampling. This advantage stems from its ability to model multiple species simultaneously, allowing target group corrections to be seamlessly applied to mitigate sampling biases. The framework also scales efficiently to handle large datasets without increased memory requirements, offering a significant advantage over previous methods. Its compatibility with standardised data types further enhances its ability to integrate and process complex biodiversity datasets. 

This development represents a major step forward in biodiversity research, providing a powerful and scalable tool to improve species distribution predictions. By leveraging advanced modelling techniques, DeepMaxent opens new possibilities for conservation strategies and policy-making. Its potential to accurately process vast amounts of citizen science data paves the way for stronger, data-driven ecological insights and actions.