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The rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data but also raises the attention of using these data to complement with remote sensing images. Many efforts have been made to utilize geospatial big data to identify human activity patterns and carry out urban and environmental researches, integrating with remote sensing images. Nonetheless, there are still many issues, including the representativeness and locality of geospatial big data, as well as the fusion methods, remain to be further explored. In this article, we first reviewed the innovation and proceedings of data mining and analyzing techniques, as well as remote sensing applications driven by geospatial big data. Besides, two popular concepts, namely, “Social Sensing” and “Urban Computing,” were briefly introduced. Then, we highlighted the role of geospatial big data in mining human activity dynamics and socioeconomic characteristics, and the feasibility of combining with remote sensing data for various studies. Lastly, we presented some empirical case studies on the confluence of remote sensing and geospatial big data in land use extraction, environmental and disaster monitoring, as well as socioeconomic dynamics sensing. The provided examples and discussion demonstrated the high efficiency and complementarity of the integration of remote sensing and geospatial big data, which benefits decision making from multiple perspectives and scales.

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, ec...

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones ...

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes., Comment: 8 pages, 8 figures, 2 tables