Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

Xing, Hanfa; Meng, Yuan; Hou, Dongyang; Song, Jie; Xu, Hualan

doi:10.3390/rs9060602

Cited by 18 publications

(13 citation statements)

References 29 publications

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“…The Latent Dirichlet allocation (LDA) model has commonly been used for textual information analysis in existing research (Lansley & Longley, ; Xing, Meng, Hou, Song, & Xu, ; Yuan et al, ). Generally, one document

d_{i}

consists of different words w , and the aim of the LDA topic model is to extract topics that contain words with similar meanings.…”

Section: Methodsmentioning

confidence: 99%

A dynamic human activity‐driven model for mixed land use evaluation using social media data

Xing

Meng

Shi

2018

Transactions in GIS

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Massive amounts of social media data have enabled the evaluation of mixed land use from the human activities perspective. While most studies have focused on approaches to better measure mixed land use, the dynamic activity patterns used to distinguish land use types have seldom been discussed. In light of this, in this study, a mixed land use evaluation model is constructed, which is driven by dynamic human activities hidden in social media data. Specifically, temporal topics related to human activities are first extracted from Twitter messages. On this basis, these topics are clustered with successive measurements of both temporal and semantic similarities, and then, they are further transformed into dynamic features. In addition, the dynamic feature‐based Shannon entropy is employed for mixed land use evaluation. For verification, the building classes from OpenStreetMap are referenced to investigate their correlations with dynamic features in mixed land uses. Finally, a strong correlation demonstrates the effectiveness and power of the proposed method in the evaluation of mixed land uses.

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d_{i}

consists of different words w , and the aim of the LDA topic model is to extract topics that contain words with similar meanings.…”

Section: Methodsmentioning

confidence: 99%

A dynamic human activity‐driven model for mixed land use evaluation using social media data

Xing

Meng

Shi

2018

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“…(2) When generating land cover regions, we defined a radius of 30 meters in kernel density based on the resolution of GlobeLand30. However, the land cover regions generated by POIs can be various, due to the diversity of textual information in POIs [19]. For example, a POI representing "golf course" refers to much larger areas than a POI representing "shop".…”

Section: Discussionmentioning

confidence: 99%

“…In general, this CGI often exists in commercial Internet maps, such as Google Maps, Baidu Maps, and Gaode Maps. Such maps contain sufficient information about land cover classes, which is aggregated as points of interest (POIs) [19]. These POIs are provided with up-to-date data, and are usually updated on a timely basis to capture land cover changes.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

2017

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Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010-2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

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“…In particular, LDA topic model is a generative probabilistic model for collecting text data and finding topics. The extraction of semantic function is based on the previous research proposed by Xing et al [34]. Specifically, the LDA topic model defines documents as input data.…”

Section: Semantic Function Extraction Using Lda Topic Modellingmentioning

confidence: 99%

Urban Function as a New Perspective for Adaptive Street Quality Assessment

Liu

et al. 2020

Sustainability

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Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.

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Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

Cited by 18 publications

References 29 publications

A dynamic human activity‐driven model for mixed land use evaluation using social media data

A dynamic human activity‐driven model for mixed land use evaluation using social media data

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

Urban Function as a New Perspective for Adaptive Street Quality Assessment

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