Automated Construction of Deep Learning Sample Sets for Impervious Surfaces Incorporating Eco-Geographic Partitions

Longchen Zhai

doi:10.54097/90cp7y63

Authors

Longchen Zhai

DOI:

https://doi.org/10.54097/90cp7y63

Keywords:

Impervious water surface; Deep learning; Automatically build samples; Geographical division.

Abstract

Accurate and efficient Impervious surface mapping is crucial to natural ecological protection, urban planning, land use and other fields. Currently, using deep learning methods to extract Impervious surfaces requires a large number of training samples, and visual interpretation of the samples is time-consuming and laborious. The distribution of existing labeled samples is relatively limited, which will lead to model overfitting. This paper fully considers factors such as geographical differences and efficiency, and proposes an automatic extraction technology for Impervious surfaces combined with ecological geographical zoning. Two high-precision Impervious surface products, GISA and GLC_FCS30, are fused and superimposed, and training samples are obtained through the crowd-source data OpenStreetMap mapping optimization results. A deep learning framework based on ResNeSt introduced into ASPP is used to automatically construct Impervious surface samples in different ecological geographical divisions across the country. After testing with Gaofen-1 data in the internal test areas of each partition, the overall accuracy of impermeable surface extraction exceeded 90%. The sample migration characteristic experiment shows that the model extraction accuracy within the same ecological geographical zone is the highest and the effect is significant, and the overall accuracy of models in different ecological geographical zones is increased by at least 1.44%. The method of automatically constructing Impervious surface samples proposed in this article not only achieves ideal results in terms of accuracy, but also has geographical generalization.

Downloads

Download data is not yet available.

References

[1] Tang Y , Shao Z , Huang X , et al. Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery[J]. Remote Sensing, 2021, 13(10).

[2] Feng Quan-Taki,Chen Po-an,Li G.Q. et al.2022.A review of research on sample datasets of remote sensing images. Journal of Remote Sensing,26(4):589-605

[3] He Ying. Application of office information extraction based on remote sensing technology in the third land survey [D]. Jilin University, 2019

[4] Building Qin. Application of Impervious surface extraction and space-time evolution analysis based on deep learning [D]. Zhejiang University, 2021. DOI: 10.27461/d.cnki.gzjdx.2021.002808

[5] Lv X, Shao Z, Ming D, et al. Improved object-based convolutional neural network (IOCNN) to classify very high-resolution remote sensing images[J]. International Journal of Remote Sensing, 2021, 42(21): 8318-8344.

[6] Pang Bo, Huang Zuoji, Wu Yanlan, etc. Extraction and mapping of Impervious surface from high resolution remote sensing image based on improved full convolution neural network [J]. Remote Sensing Information, 2020,35 (04): 47-55

[7] Lu Chen, Yang Xiaomei, Wang Zhihua. Remote sensing extraction method of block type rural residential areas based on automatic sample expansion [J]. Journal of Geoinformation Science, 2018,20 (09): 1306-1315

[8] Li Xin, Yang Yi, Wang Ning, etc. Automatic generation of remote sensing image samples and intelligent iterative classification method [J]. Surveying and Mapping Science, 2022,47 (08): 197-203. DOI: 10.16251/j.cnki.1009-2307.2022.08.022

[9] Lasko, K.; Maloney, M.C.; Becker, S.J.; Griffin, A.W.H.; Lyon, S.L.; Griffin, S.P. Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions. Remote Sens. 2021, 13, 4531. https://doi.org/10.3390/rs13224531

[10] Yang G, Yu W, Yao X,et al.AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine - ScienceDirect[J].International Journal of Applied Earth Observation and Geoinformation, 102[2024-04-10].DOI:10.1016/j.jag.2021.102446.

[11] Radoux, J., Lamarche, C., Van Bogaert, E: Automated training sample extraction for global land cover mapping, Remote Sens., 6, 3965–3987.

[12] Zhang, X. and Liu, L.: Development of a global 30 m Impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform, Zenodo.

[13] Zhang, X., Liu, L., Chen, X.: Fine LandCover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach, Remote Sens., 11, 1056.

[14] Gong Peng, Wang Jie, Yu Le, et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data [J]. International Journal of Remote Sensing, 2013, 34(7): 2607-2654.

[15] Liu Xiaoping, Hu Guohua, Chen Yimin, et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform [J]. Remote Sensing of Environment, 2018, 209:227-239.

[16] Pesaresi Martino, Syrris Vasileios, Julea Andreea. A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning [J]. Remote Sensing, 2016, 8(5):399.

[17] Wang P., Huang C., Brown de Colstoun E. C., et al. Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat [M]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). 2017:1-11.

[18] Zhu Ling, Jia Tao, Shi Ruming. Update and integration of global surface coverage products [M]. Science Press, 2020

[19] Huang X, Jiayi L I, Yang J, et al. 30m global immediate surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019 [J]. Science of China: Earth Sciences, 2021,64 (11): 12. (GISA)

[20] Hou Xingxing, Zhang Xinchang, Zhao Yi, et al. Research on Deep Learning to Extract Natural Environmental Impact Factors of Impervious Water Surface [J]. Surveying and Mapping Science, 2022,47 (11): 73-84. DOI: 10.16251/j.cnki.1009-2307.2022.11.010

[21] Olson., D.M.., Dinerstein., E.: Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.

[22] Chen Xu. Construction and Application of Knowledge Base for Global Eco geographical Zoning [D]. Beijing Jianzhu University, 2017

[23] Zhang X , Liu L , Wu C ,et al.Development of a global 30 m Impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform[J].Earth System Science Data, 2020, 12(3):1625-1648.DOI:10.5194/essd-12-1625-2020.

[24] Chen Guandong. Research on urban Impervious layer extraction method based on multi-source remote sensing image fusion [D]. Beijing University of Technology, 2018

[25] OpenStreetMap in GIScience; Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M., Eds.; Lecture Notes in Geoinformation and Cartography; Springer: Cham, Switzerland, 2015.

[26] Cidália, Costa, Fontesup, et al. Generating Up to Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30[J]. International Journal of Geo-Information, 2017, 4(6).

[27] Fonte C , Minghini M , Antoniou V , et al. An automated methodology for converting OSM data into a land use/cover map[C]// 6th International Conference on Cartography and GIS. 2016.