Ogilvie Nathaniel, Zhang Xiaohan, Kochenour Cale, Wshah Safwan
Vermont Artificial Intelligence Laboratory (VaiL), Department of Computer Science, University of Vermont, Burlington, VT 05404, USA.
Spatial Analysis Laboratory (SAL), University of Vermont, Burlington, VT 05404, USA.
Sensors (Basel). 2024 Mar 27;24(7):2134. doi: 10.3390/s24072134.
Permeable surface mapping, which mainly is the identification of surface materials that will percolate, is essential for various environmental and civil engineering applications, such as urban planning, stormwater management, and groundwater modeling. Traditionally, this task involves labor-intensive manual classification, but deep learning offers an efficient alternative. Although several studies have tackled aerial image segmentation, the challenges in permeable surface mapping arid environments remain largely unexplored because of the difficulties in distinguishing pixel values of the input data and due to the unbalanced distribution of its classes. To address these issues, this research introduces a novel approach using a parallel U-Net model for the fine-grained semantic segmentation of permeable surfaces. The process involves binary classification to distinguish between entirely and partially permeable surfaces, followed by fine-grained classification into four distinct permeability levels. Results show that this novel method enhances accuracy, particularly when working with small, unbalanced datasets dominated by a single category. Furthermore, the proposed model is capable of generalizing across different geographical domains. Domain adaptation is explored to transfer knowledge from one location to another, addressing the challenges posed by varying environmental characteristics. Experiments demonstrate that the parallel U-Net model outperforms the baseline methods when applied across domains. To support this research and inspire future research, a novel permeable surface dataset is introduced, with pixel-wise fine-grained labeling for five distinct permeable surface classes. In summary, in this work, we offer a novel solution to permeable surface mapping, extend the boundaries of arid environment mapping, introduce a large-scale permeable surface dataset, and explore cross-area applications of the proposed model. The three contributions are enhancing the efficiency and accuracy of permeable surface mapping while progressing in this field.
渗透表面映射主要是识别能够渗透的表面材料，对于各种环境和土木工程应用至关重要，如城市规划、雨水管理和地下水建模。传统上，这项任务涉及劳动密集型的人工分类，但深度学习提供了一种高效的替代方法。尽管已有多项研究致力于航空图像分割，但由于输入数据像素值难以区分以及类别分布不均衡，干旱环境下渗透表面映射的挑战在很大程度上仍未得到探索。为解决这些问题，本研究引入了一种新颖的方法，使用并行U-Net模型对渗透表面进行细粒度语义分割。该过程包括二元分类以区分完全渗透和部分渗透的表面，然后进行细粒度分类为四个不同的渗透级别。结果表明，这种新颖的方法提高了准确性，特别是在处理由单一类别主导的小的、不均衡的数据集时。此外，所提出的模型能够在不同地理区域进行泛化应用。通过探索域适应来将知识从一个地点转移到另一个地点，以应对不同环境特征带来的挑战。实验表明，并行U-Net模型在跨域应用时优于基线方法。为支持本研究并启发未来研究，引入了一个新颖的渗透表面数据集，对五个不同的渗透表面类别进行逐像素细粒度标注。总之，在这项工作中，我们为渗透表面映射提供了一种新颖的解决方案，扩展了干旱环境映射的边界，引入了一个大规模的渗透表面数据集，并探索了所提出模型的跨区域应用。这三项贡献在该领域取得进展的同时提高了渗透表面映射的效率和准确性。
