Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image - Arid Land Ecology and Resources Science Data Center

English name

Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image

10.6084/m9.figshare.28210715.v1

Share type

Category

Land Use & Land Cover

contributors

wang wei sheng , wu wei

Datasets description

<p>By integrating Swin Transformer multi-scale semantic analysis with DiffusionEdge edge refinement algorithm, high-precision cultivated land plots were successfully extracted from 10-meter resolution Sentinel-2 images. The dataset is provided in Shapefile (SHP) format and is based on the CGCS2000 coordinate system. It contains more than 670,000 plots in 2019 and more than 690,000 plots in 2023. Compared with existing raster data, it provides more accurate plots. Boundary and accurate topological relationships can reflect the spatial distribution and structural characteristics of Xinjiang cultivated land in more detail and reliably. This reliable data set provides a solid foundation for analyzing the fragmentation and scale of cultivated land, formulating agricultural strategies, optimizing irrigation and fertilization, land use planning decisions, determining the direction of cultivated land consolidation and optimization, and supporting the transformation and upgrading of agricultural production structure. </p>

Base information

data size	142.2 MiB
data format	.shp format
Coordinate system	CGCS2000
Projection	CGCS2000

Data source description

10-meter Sentinel-2 image data.

Data processing method

(1) Obtain Sentinel-2 images of the study area. (2)Preprocess Sentinel-2 images. Used to ensure the quality of remote sensing images. (3)Train the model and build a Xinjiang land-level vector dataset. (4)Apply a parcel level vector dataset.

Data quality description

Pixel-level accuracy comparison The precision rate (Precision) is 91.40%, the recall rate (Recall) of the test set is 97.26%, the inter-merge ratio (IoU) is 89.11%, and the overall accuracy (OA) reaches 94.69%.
Object-level accuracy comparison We demonstrated the performance of the model using three global indicators: GOC, GUC, and GTC. Specifically, a GOC value of 0.12 indicates a very low degree of over-segmentation in the test set. A GUC value of 0.15 indicates that there are few instances of undersegmentation across the dataset. In addition, the GTC value of 0.17 is also relatively low. Overall, these results highlight the model's ability to reduce over-segmentation and under-segmentation, and achieve high classification accuracy and accurate division of cultivated land plots. However, we acknowledge that certain types of errors still exist. For example, excessive segmentation can occur in irregularly shaped or slender plots, especially when there is significant spectral heterogeneity within a single field. On the contrary, in adjacent areas with similar spectral characteristics or weak boundary contrast, insufficient segmentation is more likely to occur.
Prediction deviation: The analysis shows that there is a certain deviation in the prediction model when estimating the total area of cultivated land. Specifically, the predicted cultivated land area is 70.5 thousand hectares, while the actual sample area is 65.3 thousand hectares, with a prediction deviation of 7.79%. In terms of the average area of cultivated land, the predicted value is 14.05 hectares, and the actual sample value is 13.13 hectares, with a prediction deviation of 7.01%. In terms of the number of cultivated land plots, the predicted value is 5016. In comparison, the actual sample number is 4,973, so there is a slight deviation of 0.8% between the prediction results and the sample results. Overall, the prediction results of cultivated land quantity, total area and average cultivated land area are slightly higher than the sample results, indicating that the prediction model shows a relatively optimistic bias.

Citations and annotations

In order to protect the rights and interests of the platform's scientific and technological resources, expand the services of the platform center, and enhance the application potential of scientific and technological resources, resource users are requested to mark and quote the research results (including published papers, treatises, data products, unpublished research reports, data products, etc.) generated by the use of resources in the following manner.

Wu, Xiande; Wu, Wei; Zhao, Yikai; Yang, Liao; Zeng, You; Liu, Rui; et al. (2025). Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image. figshare. Dataset. https://doi.org/10.6084/m9.figshare.28210715.v1

For the achievements published in English, please refer to the following specifications： The dataset is provided by Arid Land Ecology and Resources Science Data Center. (https://data.dcxjegi.cn).

Data reference

wang wei sheng, wu wei. Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image. Arid Land Ecology and Resources Science Data Center(https://data.dcxjegi.cn), 2025. https://www.doi.org/10.6084/m9.figshare.28210715.v1.

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license agreement

This work is licensed under a Creative Commons Attribution 4.0 International License.

Relevant data
File list

#	Dataset title
1	Data of 0.45-meter cultivated land in the Aral Sea Basin in 2018
2	Spatial distribution products of planting intensity of 30m cultivated land in Xinjiang and five Central Asian countries in 2020 (GCI30_2020)
3	CropLayer-2m crop cultivated land data for cities and counties in Xinjiang, China in 2020
4	GlobeLand30 Xinjiang Range Data Set for Phase III of 2000, 2010 and 2020
5	Xinjiang 1: 250,000 land use data (2000)
6	Elevation Classification Data Set of Aksu River Basin
7	Xinjiang 1: 250,000 land use data (1990)
8	Xinjiang 1: 250,000 land use data (2005)
9	Land use data in the Aral Sea Basin in 2018
10	Xinjiang 1: 250,000 land use data (2010)

#	title	file size
1	Xinjiang_2019and2023_parcel-level_vector_data.zip	137.1 MiB
2	s41597-025-05359-0.pdf	5.0 MiB

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Theme:
cultivated land plot
Subject:
Cartography
Time:
2019 2023
Place:
Xinjiang

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Service information

contacts:: Li jin
phone:: 0991-7823121
mailbox:: datacenter@ms.xjb.ac.cn

Contact information

contributors:: wang wei sheng , wu wei
author:: wang wei sheng
manager:: li jin