Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling

Artur Sokolovsky; Bhavik Merai; Moe Jafari; Muen Chen

cs.LGAug 19, 2026

Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling

Authors: Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen

Organizations: SA Power Networks, 1 Anzac Highway, Keswick SA, Adelaide, 5035, Australia

Abstract

Electric power networks are increasingly exposed to weather-sensitive failure mechanisms that require asset-level, spatially explicit risk modelling for effective intervention planning. This study contributes a modular, robust, and explainable probability-of-failure (PoF) modelling framework for utility asset management. The central contribution is an asset-level architecture that can be scaled to new environmental data sources and additional PoF types without reworking the underlying pipeline. This is particularly relevant for industry settings, where risk models must remain operationally maintainable while adapting to changing data availability, asset-management priorities, and climate-driven hazard conditions. We demonstrate the framework for vegetation-related and lightning-related failure modes using a harmonised geospatial machine-learning pipeline. The implementation integrates multi-source predictors, including topography (SRTM), vegetation condition (MODIS Normalised Difference Vegetation Index - NDVI), lightning climatology (LIS VHRMC), OpenStreetMap-derived proximity features, and utility operational records. The resulting architecture is computationally efficient, operationally extensible, and suitable for utility-scale deployment. It provides actionable asset-level risk stratification for inspection prioritisation, vegetation management, asset hardening, and resilience planning, supporting earlier intervention and more climate-resilient network operations.

Figures & tables

Figure 1: Architecture of the data processing pipeline, illustrating the disparate handling of gridded environmental data and the reconstruction of vector geometries to assemble the final feature space.

Category	Feature	Description	Search Radius
Topographic	Elevation Std. Dev.	Measure of local terrain roughness derived from the SRTM DEM.	400 m
Topographic	Relative Elevation	Difference between asset elevation and the local neighbourhood mean.	400 m
Topographic	Absolute Elevation	Absolute elevation at the asset location from the SRTM DEM.	400 m
Topographic	Elevation Gradient	Local terrain slope calculated from nearby DEM cells.	400 m
Proximity	Distance to Coastline	Minimum geodesic distance to the nearest coastline.	15 km
Proximity	Distance to Waterbody	Minimum geodesic distance to the nearest inland waterbody.	7 km

Table 1: Full list of model features, descriptions, and spatial search constraints.

Figure 2: Schematic representation of the two-step geospatial pruning algorithm. A dynamic latitudinal/longitudinal bounding box (Panel A) efficiently filters candidate points before exact geodesic distance calculations are applied (Panel B).

Figure 3: Conceptual visualisation of multi-scale feature engineering. Concentric search radii define the spatial context for urban sheltering (200 m), localised topographic anomalies (400 m), and broader environmental exposure (up to 50 km).

Variable Name	Manuscript Feature	Category
FEATURE_1_ELEVATION_STDEV_M	Elevation Std. Dev.	Topographic
FEATURE_2_ELEVATION_DEVIATION_M	Relative Elevation	Topographic
FEATURE_3_NEAREST_ELEVATION_M	Absolute Elevation	Topographic
FEATURE_4_NEAREST_GRADIENT	Elevation Gradient	Topographic
FEATURE_5_DISTANCE_TO_SEA_M	Distance to Coastline	Proximity
FEATURE_6_DISTANCE_TO_WATER_M	Distance to Waterbody	Proximity

Table 2: Model feature names used in the analytical dataset.

Figure 4: Cross-validated ROC and Precision–Recall curves for the vegetation-related failure model.

Figure 5: Cross-validated ROC and Precision–Recall curves for the lightning-related failure model.

Figure 6: Global SHAP summary for the vegetation-related failure model.

Figure 7: Global SHAP summary for the lightning-related failure model.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: Feature distributions for the vegetation-related failure model input space.

Figure 9: Feature distributions for the lightning-related failure model input space.

Power Systems

Power systems research studies the planning, operation, control, and protection of electrical grids and their integration with energy resources.

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Open paper AskPDF arXiv

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Abstract

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