Bibliometric analysis based on the Science Citation Index Expanded published by Thomson Scientific was carried out to identify the research status and future trends of remote sensing (RS) during 2010-2015. The analysis revealed the institutional, national, spatio-temporal, and categorical patterns in remote sensing research both from the WP (whole publications) viewpoint and the HCP (highly-cited publications) viewpoint. Statistical analysis results showed that remote sensing research almost doubled during 2010-2015. Environmental sciences comprised the most attractive subject category among remote sensing research. The International Journal of Remote Sensing was the most productive journal, and Remote Sensing of Environment published the most HCP among the 31 distributed journals. The productive ranking of countries was led by the U.S. both from the WP viewpoint and the HCP viewpoint, and CAS (Chinese Academy of Sciences) was the most productive institute both from the WP viewpoint and the HCP viewpoint with lower CPP (average number of citations per paper). Keyword analysis illustrated that model and algorithm research were the key points in RS during 2010-2015. RS data including Moderate-Resolution Imaging Spectroradiometer (MODIS), Landsat, synthetic aperture radar (SAR), and LiDAR (light detection and ranging) were the most frequently adopted, but the data usage of UAVs (unmanned aerial vehicles) and small satellites will be promoted in the future. With the development of data acquisition abilities, big data issues will become the challenges and hotspots of RS research, and new algorithms will continue to emerge.

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.