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Open AccessArticle
SquareSwish-Enabled Fuel-Station Risk Mapping from Satellite Imagery
by
Zuhal Can
Zuhal Can
Department of Computer Engineering, Eskişehir Osmangazi University, 26480 Eskişehir, Türkiye
Appl. Sci. 2026, 16(1), 369; https://doi.org/10.3390/app16010369 (registering DOI)
Submission received: 30 October 2025
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Revised: 21 December 2025
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Accepted: 25 December 2025
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Published: 29 December 2025
Featured Application
A low-cost screening tool for prioritizing inspections of urban fuel stations: satellite imagery and OpenStreetMap are fused to generate station-level risk scores and city-wide risk maps that help authorities rank sites near schools, hospitals, and dense housing for targeted safety planning.
Abstract
This study introduces SquareSwish, a smooth, self-gated activation f(x) = xσ(x)2, and benchmarks it against ten established activations (ReLU, LeakyReLU, ELU, SELU, GELU, Snake, LearnSnake, Swish, Mish, Hard-Swish) across six CNN architectures (EfficientNet-B1/B4, EfficientNet-V2-M/S, ResNet-50, and Xception) under a uniform transfer-learning protocol. Two geographically grounded datasets are used in this study. FuelRiskMap-TR comprises 7686 satellite images of urban fuel stations in Türkiye, which is semantically enriched with the OpenStreetMap context and YOLOv8-Small rooftop segmentation (mAP@0.50 = 0.724) to support AI-enabled, ICT-integrated risk screening. In a similar fashion, FuelRiskMap-UK is collected, comprising 2374 images. Risk scores are normalized and thresholded to form balanced High/Low-Risk labels for supervised training. Across identical training settings, SquareSwish achieves a top-1 validation accuracy of 0.909 on EfficientNet-B1 for FuelRiskMap-TR and reaches 0.920 when combined with SELU in a simple softmax-probability ensemble, outperforming the other activations under the same protocol. By squaring the sigmoid gate, SquareSwish more strongly attenuates mildly negative activations while preserving smooth, non-vanishing gradients, tightening decision boundaries in noisy, semantically enriched Earth-observation settings. Beyond classification, the resulting city-scale risk layers provide actionable geospatial outputs that can support inspection prioritization and integration with municipal GIS, offering a reproducible and low-cost safety-planning approach built on openly available imagery and volunteered geographic information.
Share and Cite
MDPI and ACS Style
Can, Z.
SquareSwish-Enabled Fuel-Station Risk Mapping from Satellite Imagery. Appl. Sci. 2026, 16, 369.
https://doi.org/10.3390/app16010369
AMA Style
Can Z.
SquareSwish-Enabled Fuel-Station Risk Mapping from Satellite Imagery. Applied Sciences. 2026; 16(1):369.
https://doi.org/10.3390/app16010369
Chicago/Turabian Style
Can, Zuhal.
2026. "SquareSwish-Enabled Fuel-Station Risk Mapping from Satellite Imagery" Applied Sciences 16, no. 1: 369.
https://doi.org/10.3390/app16010369
APA Style
Can, Z.
(2026). SquareSwish-Enabled Fuel-Station Risk Mapping from Satellite Imagery. Applied Sciences, 16(1), 369.
https://doi.org/10.3390/app16010369
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