Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
Source abstract
Abstract
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been maturing rapidly [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing in Agriculture · Automated Road and Building Extraction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea
Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
References (21)
Show source-provided references
1. Truong, X., Mitamura, M., Kono, Y., Raghavan, V., Yonezawa, G., Truong, X., Do, T., Tien Bui, D., and Lee, S. (2018). Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree. Appl. Sci., 8.10.3390/app8071046 ↗
2. Seo, D., Kim, Y., Eo, Y., and Park, W. (2018). Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression. Appl. Sci., 8.10.3390/app8081269 ↗
3. Arabameri, A., Pradhan, B., Pourghasemi, H., Rezaei, K., and Kerle, N. (2018). Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms. Appl. Sci., 8.10.3390/app8081369 ↗
4. Deng, F., and Pu, S. (2018). Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning. Appl. Sci., 8.10.3390/app8091448 ↗
5. Wiratama, W., Lee, J., Park, S., and Sim, D. (2018). Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery. Appl. Sci., 8.10.3390/app8101785 ↗
6. Zhang, C., Gao, S., Yang, X., Li, F., Yue, M., Han, Y., Zhao, H., Zhang, Y., and Fan, K. (2018). Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution. Appl. Sci., 8.10.3390/app8101981 ↗
7. Liu, T., Wang, J., Yu, H., Cao, X., and Ge, Y. (2018). A New Weighting Approach with Application to Ionospheric Delay Constraint for GPS/GALILEO Real-Time Precise Point Positioning. Appl. Sci., 8.10.3390/app8122537 ↗
8. Chen, W., Sun, Z., and Han, J. (2019). Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models. Appl. Sci., 9.10.3390/app9010171 ↗
9. Azeez, O., Pradhan, B., Shafri, H., Shukla, N., Lee, C., and Rizeei, H. (2019). Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks. Appl. Sci., 9.10.3390/app9020313 ↗
10. Kwak, G., and Park, N. (2019). Impact of Texture Information on Crop Classification with Machine Learning and UAV Images. Appl. Sci., 9.10.3390/app9040643 ↗
11. Park, S., and Kim, J. (2019). Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance. Appl. Sci., 9.10.3390/app9050942 ↗
12. Li, Y., Tong, G., Du, X., Yang, X., Zhang, J., and Yang, L. (2019). A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification. Appl. Sci., 9.10.3390/app9050951 ↗
13. Choung, Y., and Kim, J. (2019). Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea. Appl. Sci., 9.10.3390/app9061098 ↗
14. Wang, E., Li, Y., Nie, Z., Yu, J., Liang, Z., Zhang, X., and Yiu, S. (2019). Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images. Appl. Sci., 9.10.3390/app9061130 ↗
15. Oh, H., Syifa, M., Lee, C., and Lee, S. (2019). Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models. Appl. Sci., 9.10.3390/app9061248 ↗
16. Wiratama, W., and Sim, D. (2019). Fusion Network for Change Detection of High-Resolution Panchromatic Imagery. Appl. Sci., 9.10.3390/app9071441 ↗
17. Mao, H., Meng, J., Ji, F., Zhang, Q., and Fang, H. (2019). Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands. Appl. Sci., 9.10.3390/app9071459 ↗
18. Liu, B., Mavrin, B., Kong, L., and Niu, D. (2019). Spatial Data Reconstruction via ADMM and Spatial Spline Regression. Appl. Sci., 9.10.3390/app9091733 ↗
19. Utomo, D., Chen, S., and Hsiung, P. (2019). Landslide Prediction with Model Switching. Appl. Sci., 9.10.3390/app9091839 ↗
20. Li, J. (2019). A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R. Appl. Sci., 9.10.3390/app9102048 ↗
21. Xu, G., Wang, Z., and Xia, T. (2019). Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region. Appl. Sci., 9.10.3390/app9112294 ↗