Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it is difficult to extract the information from the papery borehole log quickly. What is more, it is unreliable to rely entirely on automated algorithms for modeling without artificial participation, but there is no effective way to integrate geological knowledge into 3D geological modeling currently. Therefore, it is necessary to research how to use existing papery borehole logs efficiently. To overcome the above obstacles, we designed a method that combines structural analysis and layout understanding to extract information from the borehole log. Then, the knowledge-driven three-dimensional geological modeling is proposed based on dynamic profiles. With these methods, the papery borehole log can be converted into structured data which can be used for data analysis directly, and geological knowledge can be integrated into the process of 3D geological modeling. The 3D geological modeling of Xinyang City based on a papery borehole log has been taken as an example to verify the feasibility of the method.

Many models for change point detection from time series remote sensing images have been developed to date. For forest ecosystems, fire disturbance detection models have always been an important topic. However, due to a lack of benchmark datasets, it is difficult to determine which model is appropriate. Therefore, we collected and generated a benchmark dataset specifically for forest fire disturbance detection, named CUG-FFireMCD1. The CUG-FFireMCD1 contains a total of 132 pieces of MODIS MOD13A2 time series, and each time series contains at least one fire disturbance. The occurrence time for a forest fire disturbance was determined using the National Cryosphere DesertDataCenter(NCDC) website, and the precise latitude and longitude coordinates were determined using the FireCCI51 dataset. In addition, we selected four commonly used time series change detection models and validate the advantages and limitations of the four models through dataset analysis. Finally, we use the detection results of the models and their applicable scenarios to label the additional change points. The four models we used are breaks for additive season and trend (BFAST), Prophet, continuous change detection and classification (CCDC), and Landsat-based detection of trends in disturbance and recovery (LandTrendR). The experiments show that the BFAST outperformed the other three models in forest fire disturbance detection from MOD13A2 time series, with the successful-detection-proportion rate of 96.2% with the benchmark dataset. The detection effect of the Prophet model is not as good as that of BFAST, but it also performs well, with the successful-detection-proportion rate of 87.9%. The detection results of CCDC and LandTrendR are similar, and the detection success rate is lower than that of BFAST and Prophet, but their detection results can be used as data support for labeling work. However, to apply them perfectly to MOD13A2 time series change detection, it is best to do some model adaptation. In summary, the CUG-FFireMCD1 data were verified using different types of time series change detection models, and the change points we marked are credible. The CUG-FFireMCD1 will surely provide a reliable benchmark for model optimization and the accuracy verification of remote sensing time series change detection.

Geotechnical investigation is essentially a process of information acquisition, analysis, and application. Informatization is an attractive way to improve the efficiency of geotechnical investigation. Typical approaches divide the process of geotechnical investigation into isolated stages and only focus on the local informatization of the entire process. This leads to limitations in the improvement of geotechnical investigation efficiency, misuse of geotechnical data, and interpreted results that are incorrect. Therefore, a work flow and operational mode to address the informatization throughout the overall process of geotechnical investigation is particularly important in developing a software designed for efficient and precise geotechnical investigations. In this paper, we design a work flow and operational mode for geotechnical investigation based on a geotechnical BIM model and database. Within this work flow, the construction of a 3-D geotechnical information model and BIM-based database is taken as the main line. The constantly updated model and the database are the platform for working and analysis. As this strategy can link every stage in the whole process of geotechnical investigation, all the work can be done based on the BIM model and database in a 3-D environment, so improvement in the overall efficiency of geotechnical investigation is possible. Furthermore, the integrative center combined by the BIM model and database is targeted to reduce data errors, data conversions, and the abstractness of geotechnical data, which can improve the interpreted accuracy of these data. Finally, we use the process of geotechnical investigation at a hydropower station for experimental studies to verify the proposed work flow and operational mode. The result shows that it is feasible to design the software for an efficient and more accurate geotechnical investigation based on the proposed work flow and operational mode.