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Training strategy over architecture: A systematic ablation study for Spartina detection from aerial imagery with small geospatial datasets

Author

Listed:
  • Adrien Le Guillou
  • Manon Brehier
  • Jérôme Ammann
  • Xavier Dauvergne
  • Pierre Stéphan

Abstract

In operational coastal monitoring of spatially fragmented intertidal habitats, labelled datasets are structurally constrained by the limited spatial extent of target species, yet the relative influence of training strategy versus architecture on segmentation performance remains poorly characterised. We present a systematic ablation study disentangling these contributions for automated detection of the invasive cordgrass Spartina from very high resolution aerial imagery. Three segmentation architectures, U-Net, DeepLabV3 + , and SegFormer-B2, were trained under twelve configurations spanning four training-strategy axes, each evaluated by five-fold cross-validation on 810 patches derived from aerial colour-infrared imagery and elevation data, yielding 180 training runs. Training strategy dominates architectural design: the gap between the best and worst strategy (~10 pts of overlap accuracy) exceeds the inter-architecture spread (

Suggested Citation

  • Adrien Le Guillou & Manon Brehier & Jérôme Ammann & Xavier Dauvergne & Pierre Stéphan, 2026. "Training strategy over architecture: A systematic ablation study for Spartina detection from aerial imagery with small geospatial datasets," PLOS ONE, Public Library of Science, vol. 21(9), pages 1-33, September.
    • Handle: RePEc:plo:pone00:0358464
    DOI: 10.1371/journal.pone.0358464
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