Large-scale urban functional zone mapping by integrating remote sensing images and open social data

Du, Shihong; Liu, Bo; Zhang, Xiuyuan; Zheng, Zhijia

doi:10.1080/15481603.2020.1724707

Cited by 73 publications

(29 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To better integrate spatial and temporal features from adjacent contexts, some deeper CNN models have been proposed to refine EULUC classification (Bao et al, 2020;Feng, Zhu, Yang, & Li, 2019;Huang et al, 2018). Additionally, social sensing data and methods have also been integrated with deep learning approaches to derive more accurate EULUC classification (Du, Du, Liu, Zhang, & Zheng, 2020;Jia et al, 2018;Zhong et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

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

Чэн

Gong

2021

Big Earth Data

View full text Add to dashboard Cite

Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Чэн

Gong

2021

Big Earth Data

View full text Add to dashboard Cite

show abstract

“…The basic unit of urban functional zone identification mainly includes the grid, block, traffic analysis zone (TAZ), clustering unit, geoscene, and building unit (Figure 4). (1) Grid: the image is divided into a grid with square cells [43,44]; (2) block: the research area is divided into blocks with irregular geometric size using urban road network data [45,46];…”

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu

Deng

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

show abstract

“…For the currently used content-based large-scale image retrieval, most of the images are characterized by extracting the underlying features of the images, which are used as the basis for image retrieval, and can be divided into the global feature and local feature retrieval methods according to the underlying features [5]. The global features of the image are to treat the image as a whole and extract its global feature information directly by some global feature description method, which makes feature extraction convenient and facilitates retrieval, but the features extracted by this method only focus on the image as a whole and ignore the image detail information, which often has high requirements on the image quality [6].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

Song

2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using k -means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.

show abstract

Large-scale urban functional zone mapping by integrating remote sensing images and open social data

Cited by 73 publications

References 49 publications

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

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

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

Contact Info

Product

Resources

About