Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures

Effective protected area management requires frequent habitat monitoring to respond to rapid climate change and disturbances, yet traditional manual mapping methods cannot provide the temporal resolution needed for evidence-based policy decisions. We present a practical implementation of AI-driven change detection developed in collaboration with Gesaeuse National Park administration, Austria, to support operational habitat monitoring and management planning.

We address critical challenges in deploying AI technologies for complex environmental contexts: fuzzy class boundaries in natural habitats, highly imbalanced classes, and limited training data typical of protected areas. Using 15 years of high-resolution multimodal data (RGB, NIR, LiDAR, terrain attributes) covering 4,480 documented habitat changes across 15.3 km², we compare emerging geospatial foundation models (Clay v1.0, Prithvi-EO-2.0) against established U-Net architectures to identify the most robust approach for real-world application.

Results demonstrate that foundation models show superior cross-temporal robustness (Clay: 33% accuracy vs U-Net: 23% on unseen temporal data), a critical factor for operational monitoring systems. Integrating LiDAR improves detection accuracy from 30% to 50%. While overall accuracies are lower than in homogeneous agricultural landscapes, they reflect realistic performance for complex alpine environments and provide actionable information for park management.

To further enhance practical applicability for environmental agencies, we integrate object-based post-processing and physical constraints to filter misclassifications, making outputs directly usable for management decisions. This case study demonstrates practical strategies for implementing AI technologies in complex environmental monitoring contexts where traditional approaches face significant challenges. Building upon this work from the Habitalp 2.0 project, the BioDivAI project will extend these habitat mapping approaches to predict biodiversity impacts under various land use and land cover change scenarios, providing decision-makers with tools to assess trade-offs between economic activities and ecosystem protection.