Classification of Urban Functional Areas From Remote Sensing Images and Time-Series User Behavior Data

Chen, Chen; Yan, Jining; Wang, Lizhe; Liang, Dong; Zhang, Wanfeng

doi:10.1109/jstars.2020.3044250

Cited by 30 publications

(9 citation statements)

References 49 publications

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“…The results of our model as well as the state-of-the-arts [12,13] are shown in Table 3.2. The results indicate that adding multi-dimension features to the model significantly improves the performance of both the URFC-B and URFC-A datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Feature extraction. Former works [12,13] only consider the temporal property of the user visit data and extract timesequential features by either taking D i as the original input tors in A(u) illustrate the statistics (e.g., days, hours, etc) of u appearing in each region category. The user activity feature for the target region R i is calculated by averaging all features from all users U i appearing in this region, f A (D i ) = Urban function prediction.…”

Section: Methodsmentioning

confidence: 99%

“…Cao et al [12] propose a multi-modal fusion network (MMFN) to extract the visual feature from remote sensing images and the time-sequential feature from user visit data with a neural network, then fuse the features and use a fully connected layer to predict the land function. Instead of feature fusion, Chen et al [13] propose dual-model data classification (DMDC) network to classify the features with various machine learning classifiers and fuse the decisions to get better results. However, the user-related GBD data is usually high-dimensional data describing user activities that vary with space and time.…”

Section: Introductionmentioning

confidence: 99%

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Multi-dimension Geospatial feature learning for urban region function recognition

Xu¹,

Wang²,

Wu³

2022

Preprint

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Urban region function recognition plays a vital character in monitoring and managing the limited urban areas. Since urban functions are complex and full of social-economic properties, simply using remote sensing (RS) images equipped with physical and optical information cannot completely solve the classification task. On the other hand, with the development of mobile communication and the internet, the acquisition of geospatial big data (GBD) becomes possible. In this paper, we propose a Multi-dimension Feature Learning Model (MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition. When extracting multi-dimension features, our model considers the user-related information modeled by their activity, as well as the region-based information abstracted from the region graph. Furthermore, we propose a decision fusion network that integrates the decisions from several neural networks and machine learning classifiers, and the final decision is made considering both the visual cue from the RS images and the social information from the GBD data. Through quantitative evaluation, we demonstrate that our model achieves overall accuracy at 92.75%, outperforming the state-of-the-art by 10% percent.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-dimension Geospatial feature learning for urban region function recognition

Xu¹,

Wang²,

Wu³

2022

Preprint

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“…The work in [88] presented a new method to identify the main center and subcenters of a polycentric city using nighttime light (NTL) imagery, social media data, cluster analysis, and GWR. In [89], the light gradient boosting machine (LightGBM) was used to fuse dual-modal data of highresolution remote sensing images and user behavior data for urban functional zone classification. The study in [90] proposed a novel end-to-end deep learning-based remote and social sensing data fusion model.…”

Section: ) Disaster Monitoringmentioning

confidence: 99%

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges,

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“…For example, Qiu et al [17] proposed a simple yet effective decision-level fusion method for urban land cover prediction. Chen et al [18] used decision-level fusion method to predict urban region function. However, it ignores the use of the association information between multi-modal features, so it is suitable for multi-modal features with weak correlation and strong independence.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Fusion Network Guided by Feature Co-Occurrence for Urban Region Function Recognition

ZHANG

WANG

et al. 2022

IEICE Trans. Inf. & Syst.

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Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature cooccurrence for urban region function recognition, which leverages the cooccurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

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Classification of Urban Functional Areas From Remote Sensing Images and Time-Series User Behavior Data

Cited by 30 publications

References 49 publications

Multi-dimension Geospatial feature learning for urban region function recognition

Multi-dimension Geospatial feature learning for urban region function recognition

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

A Multi-Modal Fusion Network Guided by Feature Co-Occurrence for Urban Region Function Recognition

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