Within Technologies (United Kingdom)

Around the globe we experience a significant biodiversity loss, mainly driven by direct anthropogenic exploitation, land use changes, and climate change. The most effective strategy to limit biodiversity loss is the designation and management of protected areas. Consequently, the European Union has adopted the EU Biodiversity Strategy for 2030, aiming to protect 30% of aquatic and terrestrial ecosystems by 2030. However, a consistent framework to designate protected areas across all EU member states is lacking. Additionally, the monitoring of biodiversity is challenged by the dynamic nature of the biological system, exacerbated by ongoing climate change, putting additional pressure on the member states in the identification of suitable areas for conservation. In contrast, the increasing amount of detailed geospatial and climatic data contains valuable information that can be used to optimise protected area designation. Recent developments in artificial intelligence and machine learning now provide us with powerful tools to best utilise these vast amounts of data. In this study, we develop a transparent and reproducible framework to prioritise protected areas in forests. Here we apply the CAPTAIN framework based on reinforcement learning (RL) to identify valuable forest habitats for conservation in the federal state of North Rhine-Westphalia (NRW), Germany. First, we model habitats of ten forest bird indicator species across the period of 2016-2024. Second, we use the changing habitat patterns to train a RL model that identifies 30% of the most valuable forest sites in the federal state. Finally, we model valuable forest sites under different policies (e.g., including or excluding opportunity costs for nature conservation) to illustrate how potential limitations of nature conservation management can be addressed. Our results indicate that forest sites in the south-east of NRW are most suitable for conservation. Furthermore, we find that including opportunity costs for nature conservation in the model predictions produces similarly strong outcomes for safeguarding the most endangered bird species. The framework makes use of open-source data and can be applied to any other region or country to support strategic nature conservation management.

In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end-to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by $10 \times$, resulting in improved generalization as compared to the existing state-of-the-art.