An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

Ge, Panpan; He, Jin; Zhang, Shuhua; Zhang, Liwei; She, Jinhua

doi:10.3390/ijgi8020090

Cited by 20 publications

(12 citation statements)

References 72 publications

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“…Results show a moderate entropy between the number of trips and the land use regardless of the time dimension. Taxi trips are also used in Reference [50] for land‐use discovery purposes. In particular, the incoming and outgoing flow of taxis is computed for each cell of a grid‐based tessellation of a city.…”

Section: Related Workmentioning

confidence: 99%

Land‐use dynamic discovery based on heterogeneous mobility sources

2020

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Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transportnetwork connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time-invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.

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Section: Related Workmentioning

confidence: 99%

Land‐use dynamic discovery based on heterogeneous mobility sources

2020

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“…years, geographical data with social attributes like point of interests (POI), social media log-in location, population heat map etc. were introduced into RSI analysis (Hu, Han, 2019a, Chen et al, 2018a, Chen et al, 2018b, Hu, Han, 2019b, Ge et al, 2019. With POI only, Yang et al proposed a unsupervised methods to establish UFZ, in which shared nearest neighbour (Ertöz et al, 2003) was utilised to build clusters of POI, and K-means is used to classify these clusters taking the frequency of POI in a cluster as the descriptor of this cluster (Yang et al, 2018).…”

Section: Intrioductionmentioning

confidence: 99%

Social Information Fused Urban Functional Zones Classification Network

Tao

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Fast-changing cities need efficient management. Accurate classification of urban functional zone (UFZ) can provide important reference for cities management. Remote sensing imagery (RSI) is large scale, high resolution and fast update, which can provide massive data for UFZ extraction. However, UFZ are more concerned with social attributes such as industrial production and commercial activities, while images can only provide visual features, which is not enough for an elaborate UFZ classification. To solve this problem, in this paper, we combine RSI and point of interest (POI) data together for UFZ classification, and propose a Social Information Fused Urban Functional Zones Classification Network (SIF-Net). For RSI, we simply use a Xception CNNs network extract the visual information. For POI data, we first build a coarse heatmap for each type of POI (e.g. retail, apartment…), and then combine them as a POI tensor. Afterward, we use a channel attention module (CAM) based CNN model to fuse heatmaps from each type of POI, and then build a fine distribution of UFZ as the social information. Finally, we fuse the visual information extracted from RSI and social information extracted from POI by concatenating them. By fusing this two complementary information, our method makes up for the shortcomings of extracting UFZ based on RSI and general CNNs only. Compared with current state-of-the-art methods, experiments show that the proposed SIF-Net can significantly improve the UFZ classification result.

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“…For instance, Yao et al [23] applied topic models to POI data and extracted high-dimensional thematic characteristics of each land parcel to infer urban land use types. Ge et al [24] proposed a fuzzy comprehensive evaluation (FCE) approach, as well as taxi trajectory data to integrate multiple human activity features for land use classification. By using POI data, Chen et al [25] implemented a comparative analysis with 25 major cities in China to determine their commonness/distinctiveness in the spatial organization of urban functions.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

Huang

Kang

et al. 2020

Remote Sensing

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Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

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An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

Cited by 20 publications

References 72 publications

Land‐use dynamic discovery based on heterogeneous mobility sources

Land‐use dynamic discovery based on heterogeneous mobility sources

Social Information Fused Urban Functional Zones Classification Network

An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

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