Innovative Research Award

Shankho Subhra Pal
Affiliation	Indian Institute of Technology Kharagpur
Country	India
Google Scholar ID	7aZ2ycQAAAAJ
Citations	51
h-index	4
i10-index	2
Subject Area	Computer Science
Event	International Young Scientist Awards

Shankho Subhra Pal

Indian Institute of Technology Kharagpur

The Innovative Research Award profile highlights the scholarly activities and research achievements of Shankho Subhra Pal, a researcher affiliated with the Indian Institute of Technology Kharagpur. His research contributions primarily focus on computer science, artificial intelligence, remote sensing, machine learning, image analysis, and data-driven environmental applications. The body of work demonstrates a consistent interest in developing advanced computational methodologies for time-series prediction, multispectral image processing, clustering analysis, and intelligent sensing applications.[1] [2]

Abstract

Shankho Subhra Pal’s research portfolio encompasses machine learning methodologies applied to geospatial data, multispectral satellite imagery, remote sensing analytics, cloud removal techniques, and advanced pattern recognition. His publications indicate an interdisciplinary approach that combines artificial intelligence with environmental observation systems, enabling improved predictive modeling and land-use analysis. The scholarly output reflects contributions to both foundational algorithm development and practical applications relevant to real-world sensing environments.[1] [3]

Keywords

Computer Science, Artificial Intelligence, Remote Sensing, Machine Learning, Time-Series Prediction, Multispectral Images, Land Use Analysis, Cloud Removal, Pattern Recognition, Clustering Algorithms, Geospatial Analytics, Human Sensing.

Introduction

Research in artificial intelligence increasingly intersects with environmental monitoring, satellite imaging, and data analytics. Within this context, Shankho Subhra Pal has contributed to computational frameworks that support improved interpretation of multispectral image datasets and predictive modeling systems. His work explores both theoretical and applied aspects of machine learning, emphasizing scalable techniques capable of extracting meaningful information from large and complex datasets.[2] [4]

Research Profile

The research profile of Shankho Subhra Pal reflects a specialization in computational intelligence and remote sensing technologies. His investigations have examined temporal forecasting of multispectral satellite imagery, clustering structures within complex datasets, and multimodal time-series generation methods. The diversity of publication venues demonstrates engagement with both engineering and computer science communities while maintaining a focus on methodological rigor and practical relevance.[1] [5]

Research Contributions

Among the notable themes in the research record is the application of self-supervised learning techniques to multispectral image prediction and cloud removal. These studies seek to improve image quality and predictive capabilities in remote sensing workflows, potentially enhancing land-use monitoring and environmental assessment processes.[1]

Additional contributions include investigations into hierarchical clustering structures and fine-grained land cover estimation using Landsat imagery. These efforts support more precise categorization of geographic regions and improved understanding of spatial patterns within environmental datasets.[3] [4]

Research activity has also extended into multimodal time-series generation for human sensing and mobile health applications through multi-agent generative adversarial network frameworks. Such studies illustrate the broader applicability of machine learning methodologies beyond geospatial analytics.[2]

Publications

• Time series prediction of multi-spectral images using self-supervised learning and its applications in cloud removal and land use analysis, Engineering Applications of Artificial Intelligence, 2026.[1]
• Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications, 2025.[2]
• Finding hierarchy of clusters, Pattern Recognition Letters, 2024.[3]
• Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral Images, 2023.[4]
• Time series prediction of multi-spectral satellite images and its application for cloud removal, IEEE InGARSS, 2023.[5]

Research Impact

The available citation indicators, including 51 citations, an h-index of 4, and an i10-index of 2, suggest measurable scholarly engagement with the published work. Research outputs contribute to ongoing discussions in artificial intelligence, remote sensing, image processing, and data analytics. The integration of advanced learning methods with practical environmental applications enhances the significance of these contributions within contemporary computational research.[1] [5]

Award Suitability

Based on the documented publication record, interdisciplinary research scope, and demonstrated engagement with emerging artificial intelligence applications, Shankho Subhra Pal exhibits characteristics commonly associated with candidates considered for research recognition programs. The combination of methodological innovation, practical problem-solving orientation, and contributions spanning remote sensing and machine learning aligns with the objectives typically recognized by the International Young Scientist Awards.[2] [3]

Conclusion

Shankho Subhra Pal has developed a growing research portfolio centered on artificial intelligence, remote sensing, machine learning, and computational data analysis. His publications demonstrate engagement with contemporary scientific challenges involving multispectral imagery, predictive modeling, clustering methodologies, and intelligent sensing systems. Collectively, these contributions support continued scholarly development and recognition within the broader computer science research community.[1] [4]

External Links

• Google Scholar Profile
• ORCID Profile
• DOI Link
• Award Website

References

1. Pal, S. S. (2026). Time series prediction of multi-spectral images using self-supervised learning and its applications in cloud removal and land use analysis. Engineering Applications of Artificial Intelligence.
   https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7aZ2ycQAAAAJ&citation_for_view=7aZ2ycQAAAAJ:_FxGoFyzp5QC
2. Pal, S. S. (2025). Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications. Conference Proceedings.
   https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7aZ2ycQAAAAJ&citation_for_view=7aZ2ycQAAAAJ:ufrVoPGSRksC
3. Pal, S. S. (2024). Finding hierarchy of clusters. Pattern Recognition Letters.
   https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7aZ2ycQAAAAJ&citation_for_view=7aZ2ycQAAAAJ:IjCSPb-OGe4C
4. Pal, S. S. (2023). Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral Images. Conference Proceedings.
   https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7aZ2ycQAAAAJ&citation_for_view=7aZ2ycQAAAAJ:zYLM7Y9cAGgC
5. Pal, S. S. (2023). Time series prediction of multi-spectral satellite images and its application for cloud removal. 2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS).
   https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7aZ2ycQAAAAJ&citation_for_view=7aZ2ycQAAAAJ:YsMSGLbcyi4C