Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

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Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

Remote Sensing ◽

10.3390/rs12071058 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1058 ◽

Cited By ~ 7

Author(s):

Ying Tu ◽

Bin Chen ◽

Tao Zhang ◽

Bing Xu

Keyword(s):

Land Use ◽

Learning Algorithm ◽

Accuracy Assessment ◽

Regional Analysis ◽

Urban Land ◽

Urban Land Use ◽

Level Ii ◽

Segmentation Approach ◽

Level I ◽

Use Categories

Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

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Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Science Bulletin ◽

10.1016/j.scib.2019.12.007 ◽

2020 ◽

Vol 65 (3) ◽

pp. 182-187 ◽

Cited By ~ 23

Author(s):

Peng Gong ◽

Bin Chen ◽

Xuecao Li ◽

Han Liu ◽

Jie Wang ◽

...

Keyword(s):

Land Use ◽

Urban Land ◽

Urban Land Use ◽

Preliminary Results ◽

Use Categories

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Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning

Remote Sensing ◽

10.3390/rs13214241 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4241

Author(s):

Ying Tu ◽

Bin Chen ◽

Wei Lang ◽

Tingting Chen ◽

Miao Li ◽

...

Keyword(s):

Land Use ◽

Big Data ◽

Ensemble Learning ◽

Driving Forces ◽

Urban Land ◽

Urban Land Use ◽

Land Uses ◽

Dominant Type ◽

Object Based ◽

Land Use Mix

Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.

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Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data

Remote Sensing ◽

10.3390/rs12152386 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2386 ◽

Cited By ~ 2

Author(s):

Jing Sun ◽

Hong Wang ◽

Zhenglin Song ◽

Jinbo Lu ◽

Pengyu Meng ◽

...

Keyword(s):

Land Use ◽

Classification Accuracy ◽

Urban Land ◽

Urban Land Use ◽

Rapid Urbanization ◽

Level Ii ◽

Training Samples ◽

Level I ◽

Use Categories ◽

Land Use Maps

High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.

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Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method

Remote Sensing ◽

10.3390/rs13030477 ◽

2021 ◽

Vol 13 (3) ◽

pp. 477

Author(s):

Xiaoting Li ◽

Tengyun Hu ◽

Peng Gong ◽

Shihong Du ◽

Bin Chen ◽

...

Keyword(s):

Land Use ◽

Urban Land ◽

Urban Land Use ◽

Fast Method ◽

Sample Collection ◽

Point Of Interest ◽

Area Of Interest ◽

Mapping Process ◽

Mapping Result ◽

Use Categories

Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

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