Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods
Abstract
:1. Introduction
2. Data Sources and Preprocessing
2.1. Study Area
2.2. Research Data
2.3. Data Preprocessing
3. Methods and Technical Route
3.1. Defining City Boundaries
3.2. Determination and Delimitation of Objectives
3.3. Principles of Urban Boundary Delimitation
- (1)
- The principle of administrative divisions. The intention was to delimit urban boundaries within each administrative region.
- (2)
- The principle of urban boundary direction. When sketching an urban boundary, priority was given to sketching linear features such as roads and rivers, and it was forbidden to cross houses, residential areas, large structures, parks, green spaces, sites under construction, farmland, forest land, or other large features. This involved drawing along the boundary of block features in areas without obvious linear features.
- (3)
- The principle of centralized connection. As a whole, the concentrated contiguous area was divided into the interior of the urban boundary. Where the block features were separated by small areas of agricultural land or non-construction land, this part of the partition plot was included in the interior of the urban area to keep the whole city a centralized and contiguous area.
- (4)
- The principle of judging urban landscape. The aim was to judge whether ground objects belonged to the urban area according to the urban landscape. The urban landscape within the urban boundary mainly included housing construction areas, structures, urban roads, urban squares, parks, parking lots, stadiums, urban green spaces, urban waters, etc. [40,41].
- (5)
- The principle of judging the enclaved urban area. In the process of urbanization, urban plots may have become spatially disconnected from the central urban area but remain functionally connected with the central city. Their characteristics are (a) they are connected with the urban regional center through trunk roads; (b) they have obvious urban landscape characteristics; and (c) the administrative departments, residential areas, large communities, colleges and universities, scientific research institutions, high-tech development zones, industrial and mining land, and other special areas are located in large, concentrated areas.
- (6)
- The principle of connecting adjacent urban areas. Where urban spatial integration was observed to connect adjacent cities, it was divided between the two main urban areas along lines according to the connectivity of roads, urban buildings, and rivers and water bodies.
- (7)
- The principle of farmland differentiation. A large area of regular farmland can be used as an important marker to distinguish urban and nonurban areas. Generally, the internal promotion was carried out with a piece of regular farmland as the bottom line to find the urban boundary line. No urban landscape within 50 hectares of regular farmland boundary was divided into nonurban areas.
3.4. Urban Boundary Delimitation Steps
3.4.1. Determination of Urban Points and Delimitation of Urban Units
3.4.2. Initial Delimitation of Urban Boundaries
3.4.3. Urban Boundary Inspection and Correction
3.4.4. Accuracy Evaluation and Applicability Analysis
4. Results and Discussion
4.1. City Boundary Delimitation Results
4.2. Precision Evaluation and Comparison of the Urban Boundary Results
4.3. Discussion of Urban Boundary Datasets
4.4. Applicability of the Data Sets in Deep-Learning Classification
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Image Name | Phase | Resolution (m) |
---|---|---|
T49SGA_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T49SGV_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T50SKE_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T50SKF_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T50SLE_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50SLF_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50SME_20180907T025541_TCI.jp2 | 7 September 2018 | 10 |
T50SMF_20181017T025711_TCI.jp2 | 17 October 2018 | 10 |
T49SGU_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50SKD_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T49SFU_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T49SFV_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T49SGU_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T49SEU_20181003T031541_TCI.jp2 | 3 October 2018 | 10 |
T49SEV_20181003T031541_TCI.jp2 | 3 October 2018 | 10 |
T49SFU_20180928T031539_TCI.jp2 | 28 September 2018 | 10 |
T49SFV_20180928T031539_TCI.jp2 | 28 September 2018 | 10 |
T49SET_20181028T031829_TCI.jp2 | 28 October 2018 | 10 |
T49SFT_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T49SDT_20181003T031541_TCI.jp2 | 3 October 2018 | 10 |
T49SDU_20181003T031541_TCI.jp2 | 3 October 2018 | 10 |
T49SGT_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T49SGR_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T49SGS_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T49SGS_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50RKV_20181012T025639_TCI.jp2 | 12 October 2018 | 10 |
T50RLV_20181101T025839_TCI.jp2 | 1 November 2018 | 10 |
T50SKA_20181101T025839_TCI.jp2 | 1 November 2018 | 10 |
T50SKB_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50SLA_20181012T025639_TCI.jp2 | 12 October 2018 | 10 |
T50SLB_20181012T025639_TCI.jp2 | 12 October 2018 | 10 |
T49SFS_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T50SKC_20180930T030541_TCI.jp2 | 30 September 2018 | 10 |
T49SDS_20181102T031901_TCI.jp2 | 2 November 2018 | 10 |
T49SDT_20180928T031539_TCI.jp2 | 28 September 2018 | 10 |
T49SES_20181102T031901_TCI.jp2 | 2 November 2018 | 10 |
T49SFR_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T49SFU_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T49SGT_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T49SGU_20181005T030549_TCI.jp2 | 5 October 2018 | 10 |
T50SKC_20181010T030631_TCI.jp2 | 10 October 2018 | 10 |
T50SLD_20181012T025639_TCI.jp2 | 12 October 2018 | 10 |
T50SMC_20181027T025811_TCI.jp2 | 27 October 2018 | 10 |
T50SMD_20181027T025811_TCI.jp2 | 27 October 2018 | 10 |
T50SLC_20181027T025811_TCI.jp2 | 27 October 2018 | 10 |
Serial Number | Data Name | Data Type | Phase | Range |
---|---|---|---|---|
1 | Provincial Administrative Region Boundary | Vector polygon data | 2018 | Henan Province |
2 | Municipal Administrative Region Boundary | Vector polygon data | 2018 | Henan Province |
3 | Resident of prefecture level municipal government | Vector point data | 2018 | Henan Province |
4 | Resident of district and county government | Vector point data | 2018 | Henan Province |
Division Type | |||||||||
---|---|---|---|---|---|---|---|---|---|
HNUB2018 | GUB (Henan) | ||||||||
Urban | Nonurban | Total | UA | Urban | Nonurban | Total | UA | ||
Verification type | Urban | 1229 | 20 | 1249 | 98.4% | 1030 | 352 | 1382 | 74.53% |
Nonurban | 151 | 980 | 1131 | 86.65% | 43 | 955 | 998 | 95.69% | |
Total | 1380 | 1000 | 2380 | — | 1073 | 1307 | 2380 | — | |
PA | 89.06% | 98.00% | — | — | 95.99% | 73.07% | — | — | |
OA | 92.82% | 83.40% | |||||||
Kappa | 0.8553 | 0.6732 |
LINKnet | FPN | U-Net | |||||||
---|---|---|---|---|---|---|---|---|---|
Accuracy | F1-Score | m-Iou | Accuracy | F1-Score | m-Iou | Accuracy | F1-Score | m-Iou | |
Jiyuan | 0.981 | 0.990 | 0.748 | 0.980 | 0.989 | 0.771 | 0.982 | 0.991 | 0.779 |
Jiaozuo | 0.822 | 0.894 | 0.554 | 0.933 | 0.963 | 0.721 | 0.891 | 0.937 | 0.641 |
Kaifeng | 0.849 | 0.915 | 0.520 | 0.961 | 0.979 | 0.719 | 0.939 | 0.967 | 0.653 |
Luoyang | 0.981 | 0.990 | 0.732 | 0.982 | 0.991 | 0.738 | 0.976 | 0.988 | 0.686 |
Pingdingshan | 0.959 | 0.978 | 0.682 | 0.973 | 0.986 | 0.729 | 0.957 | 0.977 | 0.666 |
Puyang | 0.962 | 0.980 | 0.687 | 0.982 | 0.991 | 0.772 | 0.985 | 0.992 | 0.796 |
Sanmenxia | 0.995 | 0.997 | 0.772 | 0.995 | 0.997 | 0.735 | 0.992 | 0.996 | 0.685 |
Shangqiu | 0.866 | 0.926 | 0.499 | 0.964 | 0.981 | 0.664 | 0.960 | 0.979 | 0.649 |
XuChang | 0.658 | 0.777 | 0.394 | 0.895 | 0.941 | 0.627 | 0.803 | 0.883 | 0.511 |
Xinxiang | 0.875 | 0.929 | 0.579 | 0.963 | 0.980 | 0.761 | 0.943 | 0.969 | 0.699 |
Xinyang | 0.995 | 0.997 | 0.799 | 0.996 | 0.997 | 0.817 | 0.995 | 0.997 | 0.804 |
Zhumadian | 0.948 | 0.972 | 0.605 | 0.958 | 0.978 | 0.628 | 0.972 | 0.985 | 0.676 |
average | 0.908 | 0.946 | 0.631 | 0.965 | 0.981 | 0.724 | 0.950 | 0.972 | 0.687 |
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
---|---|---|---|---|---|---|---|---|---|---|
Accuracy | 0.939 | 0.956 | 0.876 | 0.942 | 0.863 | 0.909 | 0.889 | 0.938 | 0.892 | 0.909 |
F1-score | 0.963 | 0.973 | 0.931 | 0.969 | 0.924 | 0.951 | 0.939 | 0.967 | 0.941 | 0.951 |
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Li, X.; Zheng, K.; Qin, F.; Wang, H.; Zhao, C. Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods. Remote Sens. 2022, 14, 3752. https://doi.org/10.3390/rs14153752
Li X, Zheng K, Qin F, Wang H, Zhao C. Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods. Remote Sensing. 2022; 14(15):3752. https://doi.org/10.3390/rs14153752
Chicago/Turabian StyleLi, Xiaojia, Kang Zheng, Fen Qin, Haiying Wang, and Chunhong Zhao. 2022. "Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods" Remote Sensing 14, no. 15: 3752. https://doi.org/10.3390/rs14153752