Mapping Landslides with AI: A New Approach to Hazard Prediction

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Author: Denis Avetisyan

Geospatial foundation models demonstrate superior performance in landslide mapping, even with limited data and across diverse terrains.

Landslide mapping has evolved from expert-driven analysis to geospatial foundation models—leveraging self-supervised learning and fine-tuning—offering scalable, adaptable, and data-efficient solutions poised to disrupt the reinforcing cycle of climate-driven landslides and escalating community vulnerability through proactive risk management and transferable knowledge.
Landslide mapping has evolved from expert-driven analysis to geospatial foundation models—leveraging self-supervised learning and fine-tuning—offering scalable, adaptable, and data-efficient solutions poised to disrupt the reinforcing cycle of climate-driven landslides and escalating community vulnerability through proactive risk management and transferable knowledge.

This review explores the geographical generalizability, data efficiency, and adaptability of foundation models like Prithvi-EO-2.0 for improved landslide hazard assessment.

Despite increasing efforts in disaster preparedness, accurate and transferable landslide hazard mapping remains challenging due to limitations in conventional deep learning models across diverse geographical contexts and data availability. This study, ‘Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability’, introduces and evaluates a novel approach leveraging geospatial foundation models—specifically Prithvi-EO-2.0—for improved landslide mapping performance. Results demonstrate that this model consistently outperforms task-specific convolutional and vision transformer networks, exhibiting robust generalization across datasets, resilience to spectral variation, and adaptability in data-scarce environments. Could this represent a significant step towards scalable and reliable landslide risk reduction and broader environmental monitoring capabilities?

The Limits of Empiricism in Landslide Detection

Traditional deep learning approaches, such as Convolutional Neural Networks (CNNs), struggle with the inherent variability of Earth observation data due to differing illumination, atmospheric effects, and complex geological features. Manual labeling of landslide data is a significant bottleneck, demanding substantial effort and specialized expertise, thus limiting scalability.

The Landslide Reference dataset, utilized in this study, provides sample visualizations for optical data analysis.
The Landslide Reference dataset, utilized in this study, provides sample visualizations for optical data analysis.

Existing methods often fail to generalize to new regions or sensor types, hindering practical utility. Automated landslide detection necessitates a concise expression of geological instability – a universal grammar of terrain.

Prithvi-EO-2.0: A Foundation for Geospatial Understanding

Prithvi-EO-2.0 is a geospatial foundation model (GeoFM) pre-trained on extensive Earth observation datasets using self-supervised learning. This approach enables the model to develop a foundational understanding of Earth’s surface without extensive labeled data. Its design prioritizes robust and transferable representations.

The model’s architecture utilizes the Vision Transformer (ViT) and a Masked Autoencoder (MAE) pre-training strategy for efficient learning from unlabeled data, reducing reliance on manual annotation. Prithvi-EO-2.0 incorporates Band Adaptability, allowing effective function with varying spectral bands across different sensors, enhancing versatility and transfer learning capabilities.

Two adapter strategies—linear projection and a U-Net encoder head—were implemented prior to the pretrained Prithvi encoder to align spectral bands and enable downstream prediction of 𝑌̂ from logits 𝐒 via a softmax function.
Two adapter strategies—linear projection and a U-Net encoder head—were implemented prior to the pretrained Prithvi encoder to align spectral bands and enable downstream prediction of 𝑌̂ from logits 𝐒 via a softmax function.

This adaptability facilitates transfer learning to tasks like landslide mapping and environmental monitoring.

Cross-Dataset Validation: A Test of Generalization

Prithvi-EO-2.0’s performance was rigorously evaluated using Landslide4Sense, the Landslide Reference Dataset, and GVLM-S2 Dataset to assess its generalization capabilities. The model consistently achieved high accuracy, measured by Mean Intersection over Union (mIoU) and F1 Score, outperforming traditional approaches, reaching 74% overall accuracy.

The cross-dataset generalization study utilized distinct sites, including the Landslide Reference dataset (train/val and generalizability regions) and the GVLM-S2 external sites, to evaluate model performance.
The cross-dataset generalization study utilized distinct sites, including the Landslide Reference dataset (train/val and generalizability regions) and the GVLM-S2 external sites, to evaluate model performance.

The use of Nonlinear Adapters further enhances performance across different sensors. Prithvi-EO-2.0 demonstrates strong Few-Shot Learning capabilities, achieving impressive results with limited labeled data, suggesting a robust understanding of landslide characteristics.

Towards a Standard for GeoFM Evaluation

Prithvi-EO-2.0 offers a cost-effective and scalable solution for disaster risk reduction by leveraging satellite imagery to identify landslide-prone areas with increased accuracy. Visual Prompt Tuning enables efficient adaptation to new tasks and datasets, minimizing computational costs.

A three-axis analytical framework provides a structured approach to assess AI robustness within the context of environmental monitoring and mapping applications.
A three-axis analytical framework provides a structured approach to assess AI robustness within the context of environmental monitoring and mapping applications.

The Three-Axis Framework provides a standardized approach for evaluating GeoFM adaptability across sensor, label, and domain dimensions, ensuring reliable performance. This framework moves beyond simple accuracy metrics, providing a comprehensive understanding of generalization capabilities. Like a perfectly derived theorem, a robust model reveals the underlying structure of a chaotic world.

The study’s success with Prithvi-EO-2.0 underscores a principle of algorithmic construction: elegance derived from a robust foundation. As David Marr stated, “Representation is just as important as computation.” The model’s ability to generalize across varied terrains and limited labeled data isn’t merely a matter of scale, but a consequence of its learned representation of geographical features. This echoes Marr’s focus on creating algorithms grounded in a principled understanding of the underlying structure—in this case, the spatial relationships defining landslide susceptibility. The research highlights that a well-defined representation, like that achieved through self-supervised learning, allows for efficient transfer and adaptation, bypassing the need for extensive, location-specific training data.

What’s Next?

The demonstrated success of Prithvi-EO-2.0 in landslide hazard mapping, while promising, merely shifts the locus of the fundamental problem. The model excels at transferring knowledge, but does not address the inherent limitations of the data itself. The elegance of the algorithm cannot compensate for imprecision in the initial observations. One anticipates a future preoccupation with data provenance – not simply quantity – and rigorous uncertainty quantification. The current paradigm of ‘more data is better’ will inevitably yield diminishing returns, necessitating a move towards physically-informed models that embed prior knowledge, not just statistical correlations.

A critical, and largely unaddressed, challenge lies in the verifiable generalizability of these models across disparate geological formations. The ability to perform well on a test dataset is insufficient; a truly robust solution demands mathematical proof of stability under varying conditions. Furthermore, the current reliance on remotely sensed data introduces unavoidable ambiguities. Distinguishing between a nascent landslide and mere surface erosion remains a persistent difficulty, demanding innovative approaches to feature extraction and signal processing.

Ultimately, in the chaos of data, only mathematical discipline endures. The field must move beyond empirical validation and embrace formal verification. The pursuit of ‘data efficiency’ is admirable, but the ultimate goal should be a model whose behavior is not merely observed, but understood – a solution grounded in first principles, not simply optimized for a specific benchmark.

Original article: https://arxiv.org/pdf/2511.04474.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-10 03:44