主要论文论著
[1] Lin, S., Huang, L., Liu, X. et al. A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images. Sci Data 11, 388 (2024).
[2] Huang L, Lin S, Liu X, Wang S, Chen G, Mei Q, Fu Z. The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing. Remote Sensing. 2024; 16(11):1889.
[3] Lin, S.; Yao, X.; Liu, X.; Wang, S.; Chen, H.-M.; Ding, L.; Zhang, J.; Chen, G.; Mei, Q. MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds. Remote Sensing. 2023, 15, 3367.
[4] Lin, S., Zhang, C., Ding, L., Zhang, J., Liu, X., Chen, G., Wang, S. and Chai, J., 2022. Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data. Remote Sensing, 14(13), p.3144.
[5] Zhang, J., Lin, S., Ding, L. and Bruzzone, L., 2020. Multi-scale context aggregation for semantic segmentation of remote sensing images. Remote Sensing, 12(4), p.701.
[6] Ding, L., Lin, D., Lin, S., Zhang, J., Cui, X., Wang, Y., Tang, H. and Bruzzone, L., 2022.Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 60, 2022. 4410313.
[7] Lin, S., Fang, W., Wu, X., Chen, Y. and Huang, Z., 2018. A spark-based high performance computational approach for simulating typhoon wind fields. IEEE Access, 6, pp.39072-39085.
[8] Lin S, Zhang Y, Fei X, et al. ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2. 5 Based on Multi-Source Spatial and Temporal Data[J]. Toxics, 2024, 12(8): 554.
[9] Lin, S.; Zhang, Y.; Liu, X.; Mei, Q.; Zhi, X.; Fei, X. Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning. Mathematics 2024, 12, 1457.
[10] Lin, S., Zhao, J., Li, J., Liu, X., Zhang, Y., Wang, S., Mei, Q., Chen, Z. and Gao, Y., 2022. A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2. 5 Concentration Prediction. Entropy, 24(8), p.1125
[11] Wu, Y., Lin, S., Peng, F. and Li, Q., 2019. Methods and application of archeological cloud platform for grand sites based on spatio-temporal big data. ISPRS International journal of geo-information, 8(9), p.377.
[12] Lin S, Yan H, Zhou S, et al. HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment[J]. Sensors, 2024, 24(15): 5033.
[13] Lin, S., Xu, Z., Sheng, Y., Chen, L. and Chen, J., 2022. AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction. Frontiers in Neuroscience, 15, p.1863.
[14] Guo, C., Lin, S., Huang, Z. and Yao, Y., 2022. Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+ BiLSTM. Health information science and systems, 10(1), p.15.
[15] Lin, S., Shi, C. and Chen, J., 2022. GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery. BMC bioinformatics, 23(1), pp.1-17.
[16] Lin, S., Wang, M., Shi, C., Xu, Z., Chen, L., Gao, Q. and Chen, J., 2022. MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network. BMC bioinformatics, 23(1), pp.1-19.
[17] Lin, S., Gao, J., Zhang, S., He, X., Sheng, Y. and Chen, J., 2020. A continuous learning method for recognizing named entities by integrating domain contextual relevance measurement and Web farming mode of Web intelligence. World Wide Web, 23, pp.1769-1790.
[18] Zhao, X., Hua-Min, C., Lin, S., Li, H., & Chen, T. (2024). A Novel Design for Joint Collaborative NOMA Transmission with a Two–Hop Multi–Path UE Aggregation Mechanism. Symmetry (Basel), 16(8), 1052.
[19] Lin, S.; Chen, Y.; Li, S. Multi-Objective Optimization in Air-to-Air Communication System Based on Multi-Agent Deep Reinforcement Learning. Sensors 2023, 23, 9541.
[20] Chen, H.; Fang, R.; Chen, T.; Wang, P.; Wang, Z.; Lin, S.; Li, F. A Novel Adaptive UE AggregationBased Transmission Scheme Design for a Hybrid Network with Multi-Connectivity. Symmetry 2023, 15, 1766.
[21] 黄磊, 林绍福, 刘希亮, 王少华, 陈桂红, 梅强. 探索城市更新消耗:一种基于高分遥感影像的城市建筑垃圾年产量快速估算方法[J]. 地球信息科学学报, 2024, 26(9): 2192-2212.
[22] 易先颖,林绍福,贾晓丰,高嵩. 基于区块链的商业性短信监管方法研究. 软件导刊. 2023年08期. 124-129. 2023-06-27.
[23] 林绍福,李松静,刘希亮.轻量级柱面电线杆标识牌字符识别算法.计算机工程与设计. 2023年08期. 2498-2505. 2023-08-16.
[24] 任东亮,林绍福,黄鸿发,付钰.基于知识图谱的抗疫意见领袖热点话题检测与分析[J].软件导刊,2020,19(10):20-24.
[25] 龚竞秋,林绍福, 黄智生. 微博“树洞”的抑郁症患者数据空间特征研究[J]. 中国数字医学, 2020, pp.70-74.