Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Zhu, Ling; Jin, Guangshuai; Gao, Dejun

doi:10.3390/info12060236

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…From Table 13, it can be seen that CLCD and GlobeLand30 are both based on global classification models, while GLC_FCS30 is based on a local adaptive classification model. Due to the requirement of ensuring overall accuracy across a large-scale area during training, there may be significant differences in the number of samples available in local areas when building global classification models [51,52].In contrast, the local adaptive classification strategy divides the large-scale area into different sub-regions and constructs training data within each sub-region [1]. Therefore, this mapping strategy can better balance the number of training data for each land cover in local areas [1].…”

Section: Analysis Of the Relationship Between Different Environment C...mentioning

confidence: 99%

Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area

Liu,

Ren,

Chen

2023

Remote Sensing

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With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations.

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Section: Analysis Of the Relationship Between Different Environment C...mentioning

confidence: 99%

Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area

Liu,

Ren,

Chen

2023

Remote Sensing

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“…This matrix offers a high degree of granularity for decomposing the concept of LC. Such granularity accommodates the definition of urban surface element types at varying scales [45].…”

Section: Ontology Primitives Established From the Eagle Matrixmentioning

confidence: 99%

Classification of Urban Surface Elements by Combining Multisource Data and Ontology

Zhu,

Lu,

Fan

2023

Remote Sensing

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The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven techniques, including deep learning and machine learning, necessitate a substantial number of annotated samples as prerequisites. In response, our study proposes a knowledge-driven approach that integrates multisource data with ontology to achieve precise urban surface element extraction. Within this framework, components from the EIONET Action Group on Land Monitoring in Europe matrix serve as ontology primitives, forming a shared vocabulary. The semantics of surface elements are deconstructed using these primitives, enabling the creation of specific descriptions for various types of urban surface elements by combining these primitives. Our approach integrates multitemporal high-resolution remote sensing data, network big data, and other heterogeneous data sources. It segments high-resolution images into individual patches, and for each unit, urban surface element classification is accomplished through semantic rule-based inference. We conducted experiments in two regions with varying levels of urban scene complexity, achieving overall accuracies of 93.03% and 97.35%, respectively. Through this knowledge-driven approach, our proposed method significantly enhances the classification performance of urban surface elements in complex scenes, even in the absence of sample data, thereby presenting a novel approach to urban surface element extraction.

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Current state and challenges in producing large-scale land cover maps: review based on recent land cover products

Gilić,

Gašparović,

Baučić

2023

Geocarto International

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Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Cited by 8 publications

References 40 publications

Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area

Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area

Classification of Urban Surface Elements by Combining Multisource Data and Ontology

Current state and challenges in producing large-scale land cover maps: review based on recent land cover products

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