Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels

Li, Zhuohong; Zhang, Hongyan; Lu, Fangxiao; Xue, Ruoyao; Yang, Guangyi; Zhang, Liangpei

doi:10.1016/j.isprsjprs.2022.08.008

Cited by 31 publications

(34 citation statements)

References 54 publications

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“…In the experiments, L2HNet has shown outstanding low-tohigh mapping performance on sufficient experiments in the USA. [34].…”

Section: A Related Workmentioning

confidence: 99%

“…• Optimization strategies: In Li's experiment [34], in order to calculate the loss and further tweak the prediction outcomes, they separated the acquired prediction maps of the RP-backbone output into confidence areas and vague areas. In the simplified L2HNet method, the CAS module and the L2H loss module are removed, leaving only the RP-backbone.…”

Section: A Related Workmentioning

confidence: 99%

“…The output of the RP-backbone is then used as the actual prediction result. • Reasons for the improvements: In Li's experiment [34], a 30m-resolution training label was used for the 1mresolution land-cover mapping product. The resolution span between 30m and 10m is large and there is a big difference in the products, therefore the prediction of the same part as the label will have a greater degree of confidence.…”

Section: A Related Workmentioning

confidence: 99%

“…In Dong et al's study, they proposed a solution that could produce 3meter resolution land-cover maps across the country without involving human effort, a major breakthrough in the lowto-high task [33]. In the context of the demand for higher resolution land-cover mapping, a Low-to-High network for large-scale high-resolution (L2HNet) [34] is used in this paper, which breaks the barrier of resolution between the HR remote sensing images and the LR training labels. Technically speaking, HR land-cover maps can be created just by using LR labeled data and HR remote images by using L2HNet.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Update of High-Resolution Land-Cover Mapping Attempt: Wuhan and the Surrounding Satellite Cities Cartography Using L2HNet

Huang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land-cover mapping is important for urban planning and management, and current land-cover mapping products are unable to meet the needs of cities due to frequent land surface changes. In this study, based on the L2HNet network, we generate a high-resolution land-cover mapping product for Wuhan and its surrounding areas. In this paper, we adopt a simplified L2HNet by removing the CAS and the L2H loss module to shorten the cycle time of entire mapping process. The mapping process used ESA LandCover (2021) as LR labels and Google Maps as high-resolution remote sensing images. In the course of the experiment, we also calculate the four indicators MIoU, Oa, FWIoU, and Kappa, evaluate the accuracy of our product in predicting fine feature structure using a point-based test method, and compare it with six mainstream land-cover mapping products. The product achieves a 1m-resolution landcover product in study areas while maintaining an over 75.21% MIoU. Oa, FWIoU and Kappa all maintain values above 85.00%, showing excellent prediction results. In quantitative analysis, compared to ESA LandCover( 2021), the L2HNet product has a significant improvement in mapping accuracy for build-up and permanent water, including an exciting 21.08% improvement in permanent water accuracy and an amazing improvement in build-up. The comparison with mainstream products also shows the credibility and practicality of the product. The end result of this research fills a gap in Wuhan and its surrounding areas' 1m-resolution land-cover mapping product. While significantly improving the product's resolution, L2HNet makes time-and labor-saving periodic mapping a reality.

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“…In the experiments, L2HNet has shown outstanding low-tohigh mapping performance on sufficient experiments in the USA. [34].…”

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous Update of High-Resolution Land-Cover Mapping Attempt: Wuhan and the Surrounding Satellite Cities Cartography Using L2HNet

Huang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…In detail, multi-source 10-m GLC products and OSM data are integrated to generate the coarse labels. Then, about 30% areas of China are selected to train the low-to-high network (L2HNet), which was proposed in our previous work [8]. By combining a multi-scale backbone, a weakly supervised module, and a self-supervised loss function in the L2HNet, the labeled noise caused by the mismatched resolution between VHR images and coarse labels is resolved during the training.…”

Section: Introductionmentioning

confidence: 99%

National-scale 1-m resolution land-cover mapping for the entire China based on a low-cost solution and open-access data

Li¹,

Wang²,

Zhang³

2023

Preprint

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Nowadays, many large-scale land-cover (LC) products have been released, however, current LC products for China either lack a fine resolution or nationwide coverage. With the rapid urbanization of China, there is an urgent need for creating a very-high-resolution (VHR) national-scale LC map for China. In this study, a novel 1-m resolution LC map of China covering 9, 600, 000km 2 , called SinoLC-1, was produced by using a deep learning framework and multi-source open-access data. To efficiently generate the VHR nationalscale LC map, firstly, the reliable LC labels were collected from three 10-m LC products and Open Street Map data. Secondly, the collected 10-m labels and 1-m Google Earth imagery were utilized in the proposed low-to-high (L2H) framework for training. With weak and self-supervised strategies, the L2H framework resolves the label noise brought by the mismatched resolution between training pairs and produces VHR results. Lastly, we compare the SinoLC-1 with five widely used products and validate it with a sample set including 10,6852 points and a statistical report collected from the government. The results show the SinoLC-1 achieved an OA of 74% and a Kappa of 0.65. Moreover, as the first 1-m national-scale LC map for China, the SinoLC-1 shows overall acceptable results with the finest landscape details.

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Integrating species distribution and piecewise linear regression model to identify functional connectivity thresholds to delimit urban ecological corridors

Yu,

Xiao,

2024

Computers, Environment and Urban Systems

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Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels

Cited by 31 publications

References 54 publications

Simultaneous Update of High-Resolution Land-Cover Mapping Attempt: Wuhan and the Surrounding Satellite Cities Cartography Using L2HNet

Simultaneous Update of High-Resolution Land-Cover Mapping Attempt: Wuhan and the Surrounding Satellite Cities Cartography Using L2HNet

National-scale 1-m resolution land-cover mapping for the entire China based on a low-cost solution and open-access data

Integrating species distribution and piecewise linear regression model to identify functional connectivity thresholds to delimit urban ecological corridors

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