Nyström least-squares collocation for regional geoid modeling with a large dataset - Xun Zhang; Guobin Chang; Hefang Bian; Xiannan Han; Houpu Li (Earth Science Informatics, 2026)

DPA-MSCL: A multi-scale supervised contrastive learning method based on dynamic prototype alignment for lithology identification - Xianshan Li; Saiwei Sun; Yao Wang; Ning Shi; Pengwei Zhang; Chao Han; Yurun Shao; Hongjia Ren; Fangfang Pan; Fengda Zhao (Earth Science Informatics, 2026)

Single-BDS GNSS-R water surface altimetry and performance analysis - Peiyuan Wang; Junqiang Han; Fang Cheng; Pengfei Zhang; Xiaolei Wang; Rui Tu; Weisheng Wang; Majid Gulayozov; Yaoming Li; Xiaochun Lu (Earth Science Informatics, 2026)

A novel reasoning random forest imputation algorithm for recovering incomplete datasets in geological hazard prediction - Jiahao Sun; Diyuan Li; Zhenyu Han; Lianku Xie (Earth Science Informatics, 2026)

A multi-dimensional modified deep learning framework for predicting potential creep-type landslides based on geological data - Yue Liu; Peihua Xu; Chen Cao; Liling Jin; Bo Han; Rongchen Ma; Chensi Li (Earth Science Informatics, 2026)

Analysis of slope instability factors: An application study of a novel interpretable ensemble model - Shunchuan Wu; Xiaolong Wang; Longqiang Han; Pengbin He; Jingqi Cui; Haokai Shen; Zhuoqing Li (Earth Science Informatics, 2025)

Fine-tuned BERT-BiLSTM-CRF approach for named entity recognition in geological disaster texts - Yange Li; Li Luo; XinRui Zeng; Zheng Han (Earth Science Informatics, 2025)

Crystal-modeler: A tool for geometric analysis and three-dimensional modeling of crystal forms based on rectangular coordinates in space - Huixia Chao; Yue Li; Weihua Yao; Xin Han; Ran Wang (Earth Science Informatics, 2023)

Research on fish identification in tropical waters under unconstrained environment based on transfer learning - Shan Zhang; Weifang Liu; Yupeng Zhu; Wei Han; Yi Huang; Jianqiang Li (Earth Science Informatics, 2022)
Seafloor unconstrained environment video is shot in uncontrolled open sea area. There are multiple backgrounds, complex illumination and weather changes, and rapid growth of algae and attached lenses, which affect the stability of video shooting quality, resulting in difficulty in image recognition. At present, there is still no algorithm that is superior to other methods generally, and it is necessary to build a model combined with specific scenes and applications. In this paper, a fish identification method in tropical waters based on transfer learning under unconstrained environment is proposed. Firstly, the image is pre-processed by affine transformation to realize data enhancement. Furthermore, RestNet50 deep convolutional neural network is constructed based on transfer learning to compare the effect of fish recognition before and after transfer learning. The results show that, the accuracy and loss indicators are better than those of non-transfer learning when the trained model of imagenet is introduced as the initial weight of the network. When the model is trained to 150 epochs, the indicators begin to converge, which can better complete the fish identification task in tropical waters under unconstrained environment.

Spatial–temporal evolution pattern and prediction analysis of flood disasters in China in recent 500 years - Cuixia Wei; Bing Guo; Hailing Zhang; Baomin Han; Xiangshen Li; Huihui Zhao; Yuefeng Lu; Chao Meng; Xiangzhi Huang; Wenqian Zang; Hongwei Wu (Earth Science Informatics, 2022)

Retrieval and analysis of land surface temperature in permafrost regions in Northeast China based on AMSR2 data - Han Yin; Miao Li; Haoran Man; Xingfeng Dong; Boquan Lu; Shuying Zang (Earth Science Informatics, 2021)

New variogram modeling method using MGGP and SVR - Changik Han; Jiyang Wang; Mingguo Zheng; Ende Wang; Jianming Xia; GwangSu Li; Sunchol Choe (Earth Science Informatics, 2016)