scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 33-39 ◽  
Author(s):  
A. S. Anugraha ◽  
H.-J. Chu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Human Behavior ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Social Sensing ◽  
Sensing Data ◽  
The City

<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

scholarly journals Identificación de conflictos de uso de suelo para la planificación del crecimiento urbano: ciudad Cuauhtémoc, Chihuahua (México)

2019 ◽  
Vol 45 (2) ◽  
pp. 709
Author(s):  
J.D. Maldonado-Marín ◽  
L.C. Alatorre-Cejudo ◽  
E. Sánchez-Flores
Keyword(s):  
Land Use ◽  
Development Planning ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Conflict ◽  
The Status ◽  
Resolution Model ◽  
Urban Planning And Development ◽  
Land Use Policies

This research incorporates new forms of analysis for urban planning and development in Ciudad Cuauhtémoc, Chihuahua (Mexico), providing elements of reference by identifying areas with potentiality and limitations for urban land use, as well as for agricultural and conservation activities. The general objective was to identify the main conflicts between land uses and coverages to determine the areas of greatest territorial suitability for the city's growth. For this purpose, the Land Use Conflict Identification Strategy (LUCIS) model was used to understand the spatial significance of the status of land use policies, including likely urban patterns associated with agricultural and conservation trends. In the case study, a total of 149,139 inhabitants are estimated for the year 2030, which represents the need for an additional 392.42 hectares to accommodate the population growth. For that of the 16,272.21 hectares that has the population limit, 38 % were allocated to the category of agriculture, 11.95% to conservation soils and 49.67% to urban land (including the existing urban area). There is a significant portion of the area that is in conflict between the different land uses. It concludes, that the integration of a conflict resolution model for land use and land cover represents a practical solution that contributes to the improvement of processes of urban development planning.

scholarly journals Urban Land-Use Dynamics in the Niger Delta: The Case of Greater Port Harcourt Watershed

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 108 ◽  
Author(s):  
Nimi Dan-Jumbo ◽  
Marc Metzger ◽  
Andrew Clark
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Niger Delta ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Hard Copy ◽  
Land Use Classification ◽  
Depth Analysis

Cities in developing countries are urbanising at a rapid rate, resulting in substantial pressures on environmental systems. Among the main factors that lead to flooding, controlling land-use change offers the greatest scope for the management of risk. However, traditional analysis of a “from–to” change matrix is not adequate to provide information of all the land-use changes that occur in a watershed. In this study, an in-depth analysis of land-use change enabled us to quantify the bulk of the changes accumulating from swap changes in a tropical watershed. This study assessed the historical and future land-use/land-cover (LULC) dynamics in the River State region of the Niger Delta. Land-use classification and change detection analysis was conducted using multi-source (Landsat TM, ETM, polygon map, and hard copy) data of the study area for 1986, 1995, and 2003, and projected conditions in 2060. The key findings indicate that historical urbanisation was rapid; urban expansion could increase by 80% in 2060 due to planned urban development; and 95% of the conversions to urban land occurred chiefly at the expense of agricultural land. Urban land was dominated by net changes rather than swap changes, which in the future could amplify flood risk and have other severe implications for the watershed.

scholarly journals Social sensing of urban land use based on analysis of Twitter users’ mobility patterns

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181657 ◽  
Author(s):  
Aiman Soliman ◽  
Kiumars Soltani ◽  
Junjun Yin ◽  
Anand Padmanabhan ◽  
Shaowen Wang
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Mobility Patterns ◽  
Social Sensing ◽  
Twitter Users

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

2020 ◽  
Vol 12 (19) ◽  
pp. 3254
Author(s):  
Zhou Huang ◽  
Houji Qi ◽  
Chaogui Kang ◽  
Yuelong Su ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Ensemble Learning ◽  
Urban Land ◽  
Urban Land Use ◽  
Learning Approach ◽  
Land Use Classification ◽  
Remote Sensing Imagery ◽  
Sensing Data

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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