Monidipa Das
Impact in
- Media Technology top 5%
- Remote-Sensing Image Classification
- Advanced Image Fusion Techniques
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
- Remote Sensing and LiDAR Applications
Papers in
-
- Remote-Sensing Image Classification 12
- Advanced Image Fusion Techniques 4
-
- Hydrological Forecasting Using AI 10
- Co-authors
- Soumya K. GhoshSuparna DuttaMahardhika PratamaV. M. ChowdaryAlexander C. LouiJie ZhangR. NagarajaV. K. Dadhwal
- Journals
- IEEE Geoscience and Remote Sensing Letters (2 papers)Pattern Recognition Letters (2 papers)IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2 papers)Measurement (1 paper)IEEE Transactions on Geoscience and Remote Sensing (1 paper)
- Partner nations
- IndiaSingaporeUnited States
In The Last Decade
Monidipa Das
46 papers receiving 528 citations
Peers
Comparison fields: 5 of 86
- Media Technology 114
- Environmental Engineering 147
- Signal Processing 66
- Transportation 36
- Global and Planetary Change 110
Countries citing papers authored by Monidipa Das
This map shows the geographic impact of Monidipa Das's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Monidipa Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monidipa Das more than expected).
Fields of papers citing papers by Monidipa Das
This network shows the impact of papers produced by Monidipa Das. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Monidipa Das. The network helps show where Monidipa Das may publish in the future.
Co-authors
The 25 scholars most cited alongside Monidipa Das, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 30 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 1 | |
| 11 | 2021 | 0 | |
| 12 | 2020 | 1 | |
| 13 | 2020 | 9 | |
| 14 | 2019 | 7 | |
| 15 | 2018 | 32 | |
| 16 | 2017 | 53 | |
| 17 | 2017 | 2 | |
| 18 | 2016 | 76 | |
| 19 | 2016 | 2 | |
| 20 | 2002 | 9 |
About Monidipa Das
Monidipa Das is a scholar working on Media Technology, Environmental Engineering, Global and Planetary Change, Artificial Intelligence and Signal Processing, having authored 52 papers that have together received 551 indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (12 papers), Remote-Sensing Image Classification (12 papers), Hydrological Forecasting Using AI (10 papers), Advanced Image Fusion Techniques (4 papers), Climate variability and models (4 papers), Land Use and Ecosystem Services (4 papers), Advanced Graph Neural Networks (4 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Media Technology (114 citations), Environmental Engineering (147 citations), Signal Processing (66 citations), Transportation (36 citations) and Global and Planetary Change (110 citations). Monidipa Das has collaborated with scholars based in India, Singapore and United States. Frequent co-authors include Soumya K. Ghosh, Suparna Dutta, Mahardhika Pratama, V. M. Chowdary, Alexander C. Loui, Jie Zhang, R. Nagaraja, V. K. Dadhwal, Arpan Pal and Jie Zhang. Their work appears in journals such as IEEE Geoscience and Remote Sensing Letters, Pattern Recognition Letters, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Measurement and IEEE Transactions on Geoscience and Remote Sensing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.