Fine-Grained Permeable Surface Mapping through Parallel U-Net
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-intensiv...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/7/2134 |
| _version_ | 1797211968674725888 |
|---|---|
| author | Nathaniel Ogilvie Xiaohan Zhang Cale Kochenour Safwan Wshah |
| author_facet | Nathaniel Ogilvie Xiaohan Zhang Cale Kochenour Safwan Wshah |
| author_sort | Nathaniel Ogilvie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:34:55Z |
| format | Article |
| id | doaj.art-c23e5c1523c345c28934d62dfe028bab |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:55Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c23e5c1523c345c28934d62dfe028bab2024-04-12T13:26:17ZengMDPI AGSensors1424-82202024-03-01247213410.3390/s24072134Fine-Grained Permeable Surface Mapping through Parallel U-NetNathaniel Ogilvie0Xiaohan Zhang1Cale Kochenour2Safwan Wshah3Vermont Artificial Intelligence Laboratory (VaiL), Department of Computer Science, University of Vermont, Burlington, VT 05404, USAVermont Artificial Intelligence Laboratory (VaiL), Department of Computer Science, University of Vermont, Burlington, VT 05404, USASpatial Analysis Laboratory (SAL), University of Vermont, Burlington, VT 05404, USAVermont Artificial Intelligence Laboratory (VaiL), Department of Computer Science, University of Vermont, Burlington, VT 05404, USAPermeable 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.https://www.mdpi.com/1424-8220/24/7/2134permeable surface mappingimpervious surface mappingarid environmentaerial imagerycross-domain adaptationimage segmentation |
| spellingShingle | Nathaniel Ogilvie Xiaohan Zhang Cale Kochenour Safwan Wshah Fine-Grained Permeable Surface Mapping through Parallel U-Net Sensors permeable surface mapping impervious surface mapping arid environment aerial imagery cross-domain adaptation image segmentation |
| title | Fine-Grained Permeable Surface Mapping through Parallel U-Net |
| title_full | Fine-Grained Permeable Surface Mapping through Parallel U-Net |
| title_fullStr | Fine-Grained Permeable Surface Mapping through Parallel U-Net |
| title_full_unstemmed | Fine-Grained Permeable Surface Mapping through Parallel U-Net |
| title_short | Fine-Grained Permeable Surface Mapping through Parallel U-Net |
| title_sort | fine grained permeable surface mapping through parallel u net |
| topic | permeable surface mapping impervious surface mapping arid environment aerial imagery cross-domain adaptation image segmentation |
| url | https://www.mdpi.com/1424-8220/24/7/2134 |
| work_keys_str_mv | AT nathanielogilvie finegrainedpermeablesurfacemappingthroughparallelunet AT xiaohanzhang finegrainedpermeablesurfacemappingthroughparallelunet AT calekochenour finegrainedpermeablesurfacemappingthroughparallelunet AT safwanwshah finegrainedpermeablesurfacemappingthroughparallelunet |