Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

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Based on 11004 satellite images from CBERS CCD and Landsat TM/ETM, changes in the spatial characteristics of all lakes in China were determined following pre-established interpretation rules. This dataset was supported by 6843 digital raster images (1:100000 and 1:50000), a countrywide digital vector dataset (1:250000), and historical literature. Comparative data were corrected for seasonal variations using precipitation data. There are presently 2693 natural lakes in China with an area greater than 1.0 km 2 , excluding reservoirs. These lakes are distributed in 28 provinces, autonomous regions and municipalities and have a total area of 81414.6 km 2 , accounting for ~0.9% of China's total land area. In the past 30 years, the number of newly formed and newly discovered lakes with an area greater than 1.0 km 2 is 60 and 131, respectively. Conversely, 243 lakes have disappeared in this time period.

The proliferation of wireless localization technologies provides a promising future for serving human beings in indoor scenarios. Their applications include real-time tracking, activity recognition, health care, navigation, emergence detection, and target-of-interest monitoring, among others. Additionally, indoor localization technologies address the inefficiency of GPS (Global Positioning System) inside buildings. Since people spend most of their time in indoor environments, indoor tracking service is in great public demand. Based on this observation, this paper aims to provide a better understanding of state-of-the-art technologies and stimulate new research efforts in this field. For these purposes, existing localization technologies that can be used for tracking individuals in indoor environments are reviewed, along with some further discussions.

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