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.

<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>

Abstract. Jakarta, the capital city of Indonesia, is the city with the largest population in Indonesia with a population of 10,609,681. In line with the rapid growth and the increment of the population in Jakarta lead to the increment in the number of buildings in Jakarta. However, the building increment in Jakarta leads to the emergence of buildings ownership problems. Meanwhile, the increasing urbanization promotes the need for a system that accommodates the smart city concepts which require complex interactions between governments and citizens. Therefore, the Jakarta Provincial Government established a breakthrough program called Priority Villages, a program that integrates geospatial and citizen participation using the Geographic Information System (GIS) for the smart city planning model. This program focused on the distribution acceleration of building establishment decision letters. This research aims to visualize the concept and advantages of the Priority Village program as an analysis-oriented application for the development of intelligent city concepts. The outputs of the Priority Villages program are the issuances of 112 building establishment decision letters per region and 7,534 building establishment decision letters per individual building. The integration of geospatial and citizen participation using Geographic Information System (GIS) in Priority Villages program strongly supports the sustainability of the Jakarta Smart City program, shortening the Estimated Time of Arrival (ETA) of the building establishment decision letter making from 16 days to 7 days and give the positive impact on the broader citizen in Jakarta.