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Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

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Fine-Grained Permeable Surface Mapping through Parallel U-Net.

Nathaniel Ogilvie¹, Xiaohan Zhang¹, Cale Kochenour²

¹Vermont Artificial Intelligence Laboratory (VaiL), Department of Computer Science, University of Vermont, Burlington, VT 05404, USA.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study introduces a novel deep learning model for accurate permeable surface mapping in arid regions, improving stormwater management and urban planning by classifying surface permeability with enhanced precision.

Keywords:

U-Net aerial imagery arid environment cross-domain adaptation image segmentation impervious surface mapping permeable surface mapping

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Area of Science:

Environmental Engineering
Remote Sensing
Artificial Intelligence

Background:

Permeable surface mapping is crucial for urban planning, stormwater management, and groundwater modeling.
Traditional methods are labor-intensive, and deep learning approaches face challenges in arid environments due to data complexities and class imbalance.
Existing aerial image segmentation studies have not fully addressed the unique difficulties of permeable surface mapping in arid regions.

Purpose of the Study:

To develop a novel deep learning approach for fine-grained semantic segmentation of permeable surfaces, specifically addressing arid environment challenges.
To improve the accuracy and efficiency of permeable surface identification using advanced deep learning techniques.
To introduce a new, large-scale dataset for permeable surface mapping with detailed pixel-wise annotations.

Main Methods:

A novel parallel U-Net model was developed for fine-grained semantic segmentation of permeable surfaces.
The approach involved binary classification (entirely vs. partially permeable) followed by a four-level fine-grained permeability classification.
Domain adaptation techniques were employed to enhance model generalization across different geographical locations.

Main Results:

The parallel U-Net model demonstrated enhanced accuracy, especially with small, unbalanced datasets.
The model effectively distinguished between different levels of surface permeability.
Experiments confirmed the model's superior performance over baseline methods in cross-domain applications.

Conclusions:

The proposed parallel U-Net model offers an efficient and accurate solution for permeable surface mapping in arid environments.
The study advances the field by addressing data challenges and introducing a valuable new dataset.
The developed model shows strong generalization capabilities and potential for cross-area applications in environmental and civil engineering.