{"id":4,"date":"2025-12-10T11:09:51","date_gmt":"2025-12-10T16:09:51","guid":{"rendered":"https:\/\/www.clarku.edu\/geospatial-analytics\/?page_id=4"},"modified":"2025-12-10T15:31:54","modified_gmt":"2025-12-10T20:31:54","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.clarku.edu\/geospatial-analytics\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
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Delve into our comprehensive collection of scholarly and applied works. From groundbreaking research to practical insights, our publications showcase the impact of geospatial analytics across disciplines. Browse through white papers, peer-reviewed articles, conference presentations, and detailed reports to discover how our tools and innovations are driving meaningful change.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Peer-reviewed papers<\/h2>\n\n\n\n
Journal Papers<\/summary>\n
    \n
  1. Hashemi M.G.Z., Jalilvand E., Alemohammad H., Tan P.N., Das N.N. (2024) A Systematic Review of Synthetic Aperture Radar and Deep Learning in Agricultural Applications, ISPRS Journal of Photogrammetry and Remote Sensing, 218(A), 20-49. (link<\/a>)<\/li>\n\n\n\n
  2. Hashemi M.G.Z., Tan P.N., Jalilvand E., Wilke B., Alemohammad H., Das N.N. (2024) Yield Estimation from SAR Data Using Patch-Based Deep Learning and Machine Learning Techniques, Computers and Electronics in Agriculture, 226, 109340. (link<\/a>)<\/li>\n\n\n\n
  3. Fluhrer A., Jagdhuber T., Montzka C., Schumacher M., Alemohammad H., Tabatabaeenejad A., Kunstmann H., Entekhabi D. (2024), Soil Moisture Profile Estimation by Combining P-band SAR Polarimetry with Hydrological and Multi-Layer Scattering Models, Remote Sensing of Environment, 305, 114067. (link<\/a>)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Pre-Prints on arXiv<\/summary>\n
      \n
    1. Szwarcman D., Roy S., Fraccaro P., G\u00edslason \u00de.E., Blumenstiel B., Ghosal R., de Oliveira P.H., de Sousa Almeida J.L., Sedona R., Kang Y., Chakraborty S., Wang S., Kumar A., Truong M., Godwin D., Lee H., Hsu C-Y, and Akbari Asanjan A., Mujeci B., Keenan T., Arevalo P., Li W., Alemohammad H., Olofsson P., Hain C., Kennedy R., Zadrozny B., Cavallaro G., Watson C., Maskey M., Ramachandran R., Moreno J.B. (2024) Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications (link<\/a>).<\/li>\n\n\n\n
    2. Tadesse G.A., Robinson C., Mwangi C., Maina E., Nyakundi J., Marotti L., Hacheme G.Q., Alemohammad H., Dodhia R., Lavista Ferres J.M., (2024) Local vs. Global: Local Land Use and Land Cover Models Deliver Higher Quality Maps (link<\/a>).<\/li>\n\n\n\n
    3. Khallaghi S., Abedi R., Ali H. A., Alemohammad H., Asipunu M. D., Alatise I., Ha N., Luo B., Mai C., Song L., Wussah A., Xiong S., Yao Y.-T., Zhang Q., Estes L. D. (2024) Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples (link<\/a>).<\/li>\n\n\n\n
    4. Jakubik J., Roy S., Phillips C. E., Fraccaro P., Godwin D., Zadrozny B., Szwarcman D., Gomes C., Nyirjesy G., Edwards B., Kimura D., Simumba N., Chu L., Mukkavilli S. K., Lambhate D., Das K., Bangalore R., Oliveira D., Muszynski M., Ankur M., Ramasubramanian M., Gurung I., Khallaghi S., Li H., Cecil M., Ahmadi M., Kordi F., Alemohammad H., Maskey M., Ganti R., Weldemariam K., Ramachandran, R. (2023) Foundation Models for Generalist Geospatial Artificial Intelligence (link<\/a>).<\/li>\n<\/ol>\n<\/details>\n\n\n\n
      Conference Papers<\/summary>\n
        \n
      1. Godwin D., Li H., Cecil M., Alemohammad H., Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model, 2nd ICLR Workshop on Machine Learning for Remote Sensing, Vienna, Austria, 2024. (link<\/a>)<\/li>\n\n\n\n
      2. Lacoste A., Lehmann N., Rodriguez P., Sherwin E. D., Kerner H., L\u00fctjens B., Irvin J.A., Dao D., Alemohammad H., Drouin A., Gunturkun M., Huang G., Vazquez D., Newman D., Bengio Y., Ermon S., Zhu X.X., GEO-Bench: Toward Foundation Models for Earth Monitoring, NeurIPS 2023 Datasets and Benchmarks, New Orleans, LA, USA, 2023. (link<\/a>)<\/li>\n\n\n\n
      3. Fluhrer A., Jagdhuher T., Montzka C., Schumacher M, Alemohammad H., Tabatabaeenejad A., Kunstmann H., Entekhabi D., Estimating Soil Moisture Profiles by Combining P-Band SAR with Hydrological Modeling, 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023), 2846-2849, Pasadena, CA, USA, 2023. (link<\/a>)<\/li>\n<\/ol>\n<\/details>\n\n\n\n

        Conference presentations<\/h2>\n\n\n\n

        Our conference presentations feature cutting-edge insights shared at major industry and academic events. From keynote addresses to workshop sessions, these resources reflect the evolving landscape of geospatial analytics.<\/p>\n\n\n\n

        View our presentations<\/summary>\n
          \n
        1. Alemohammad H., Godwin D., Balogun R., Khallaghi S., Roy S., Ramachandran R., GFM-Bench: A Benchmark to Evaluate Geospatial Characteristics of Foundation Models, 2024 AGU Annual Meeting, Washington, DC, USA, Dec. 2024. (link<\/a>)<\/li>\n\n\n\n
        2. Alemohammad, H., Transforming Global Challenges with Geospatial AI, Korean Academy of Development Policy Winter Conference (invited), Nov. 2024.<\/li>\n\n\n\n
        3. Alemohammad, H., Advancing Foundation Models for Geospatial Applications, Washington State University (invited), Oct. 2024.<\/li>\n\n\n\n
        4. Alemohammad, H., Advancing Foundation Models for Geospatial Applications, Mathematica (invited), Oct. 2024<\/li>\n\n\n\n
        5. Alemohammad, H., The State of GeoAI: Advancements, Opportunities, and Challenges, New York State Geospatial Summit (invited), Sep. 2024.<\/li>\n\n\n\n
        6. Alemohammad, H., Advancements and Applications of Foundation Models for Earth Observations, Amazon Sustainability Speaker Series (invited), Virtual, May 2024.<\/li>\n\n\n\n
        7. Alemohammad, H., The State of GeoAI: Opportunities, Challenges, and Risks, Evaluating the Science of Geospatial AI, Harvard CGA 2024 Conference (invited), Cambridge, MA, May 2024.<\/li>\n\n\n\n
        8. Alemohammad, H., Reproducibility and Uncertainty in the Era of Geospatial AI, Evaluating the Science of Geospatial AI, Harvard CGA 2024 Conference, Cambridge, MA, May 2024.<\/li>\n\n\n\n
        9. Alemohammad, H., Transforming Global Challenges with Geospatial Analytics, US Summit: The National Geospatial Ecosystem of the United States at the 2024 Geospatial World Forum, Rotterdam, The Netherlands, May 2024. (link<\/a>)<\/li>\n\n\n\n
        10. Alemohammad, H., Advancing Foundation Models for Geospatial Applications with Scarce Reference Data, Measuring Development Conference (invited), Washington, DC, May 2024. (link<\/a>)<\/li>\n\n\n\n
        11. Alemohammad, H., Fine-Tuning Foundation Models for Downstream Applications of Remote Sensing Data, 2024 AAG Annual Meeting, Honolulu, HI, Apr. 2024.<\/li>\n\n\n\n
        12. Alemohammad, H., A New Era for Geospatial Analytics: Advancements and Applications of Foundation Models in Remote Sensing, Harvard ABCD-GIS \/ Geography Colloquium, Harvard Center for Geographic Analysis (invited), Cambridge, MA, May 2024. (link<\/a>)<\/li>\n\n\n\n
        13. Alemohammad, H., Applications of Foundation Models in Earth Sciences Applied to Remote Sensing Imagery, 2nd U.S.-Africa Frontiers of Science, Engineering, and Medicine Symposium, Rabat, Morocco, Jan. 2024.<\/li>\n\n\n\n
        14. Alemohammad, H., Transforming Earth Observation Analytics: Advancements and Applications of Foundation Models in Remote Sensing, CV4EO Workshop at IEEE WACV (invited), Waikoloa Beach, HI, USA, Jan. 2024.<\/li>\n\n\n\n
        15. Alemohammad, H., Li, H., Cecil, M., Khallaghi, S., Godwin, D., Ahmadi, M., Kordi, F., Roy, S., Jakubik, J., Fraccaro, P., Ganti, R.K., Ramachandran, R., Exploring Effectiveness of Foundation Models for Downstream Applications on Satellite Data, 2023 AGU Annual Meeting, San Francisco, CA, USA, Dec. 2023. (link<\/a>)<\/li>\n\n\n\n
        16. Ahmadi, M., Khallaghi, S., Cecil, M., Roy, S.,Jakubik, J., Fraccaro, P., Alemohammad, H., Assessing Performance of a Foundation Model for Multi-Label Image Classification of Satellite Imagery, 2023 AGU Annual Meeting, San Francisco, CA, USA, Dec. 2023. (link<\/a>)<\/li>\n\n\n\n
        17. Khallaghi, S., Li, H., Cecil, M., Kordi, F., Roy, S., Jakubik, J., Fraccaro, P., Alemohammad, H., Evaluating Foundation Models for Crop Type Segmentation Using Multi-temporal Multi-spectral Satellite Imagery, 2023 AGU Annual Meeting, San Francisco, CA, USA, Dec. 2023. (link<\/a>)<\/li>\n\n\n\n
        18. Godwin, D., Cecil, M., Li, H., Roy, S., Jakubik, J., Fraccaro, P., Alemohammad, H., Comparative Analysis of Vision Transformer and CGAN Models for Cloud Gap Filling in Time Series of Satellite Images, 2023 AGU Annual Meeting, San Francisco, CA, USA, Dec. 2023. (link<\/a>)<\/li>\n\n\n\n
        19. Alemohammad, H., AI and open remote sensing data for crop yields prediction, International workshop on Earth Observation for Food Security, World Bank (invited), Washington, DC, USA, May 2023.<\/li>\n\n\n\n
        20. Alemohammad, H., Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery, European Geophysical Union, Vienna, Austria, Apr. 2023. (link<\/a>)<\/li>\n\n\n\n
        21. Alemohammad, H., Amplifying Impact through Collaborative Ecosystems for Geospatial Analytics, GeoBuiz Summit, Monterey, CA, USA, Mar. 2023.<\/li>\n<\/ol>\n<\/details>\n\n\n\n

          White papers<\/h2>\n\n\n\n

          Our white papers provide detailed methodologies, and applications in geospatial analytics. Designed for professionals and researchers, these documents offer actionable insights and practical solutions to complex challenges.\u00a0<\/p>\n\n\n\n

          View our white papers<\/summary>\n
            \n
          1. Classification Tree Analysis (link<\/a>)<\/li>\n\n\n\n
          2. Environmental Modeling Analyzing Motion with Trend Surface Analysis (link<\/a>)<\/li>\n\n\n\n
          3. Environmental Change Analysis for Crop Monitoring (link<\/a>)<\/li>\n\n\n\n
          4. Land Cover Mapping in the Yucatan Peninsula (link<\/a>)<\/li>\n\n\n\n
          5. Exploring Image Time Series with Earth Trends Modeler (link<\/a>)<\/li>\n\n\n\n
          6. Land Cover Mapping and the Use of Spatial Priors for Forest Classification (link<\/a>)<\/li>\n\n\n\n
          7. Land Cover Mapping with Hyperspectral Imagery (link<\/a>)<\/li>\n\n\n\n
          8. Land Use Planning for Forest Management (link<\/a>)<\/li>\n\n\n\n
          9. Landuse Planning and Cell Tower Siting (link<\/a>)<\/li>\n\n\n\n
          10. Risk and Vulnerability Assessment for Nuclear Risk Management (link<\/a>)<\/li>\n\n\n\n
          11. Natural Resource Management in India\u2019s Western Ghats (link<\/a>)<\/li>\n\n\n\n
          12. Modeling REDD Baselines using TerrSet\u2019s Land Change Modeler (link<\/a>)<\/li>\n\n\n\n
          13. Segmentation and Segment-based Classification (link<\/a>)<\/li>\n\n\n\n
          14. Species Distribution Modeling with TerrSet\u2019s Habitat and Biodiversity Modeler (link<\/a>)<\/li>\n<\/ol>\n<\/details>\n\n\n\n

            Reports<\/h2>\n\n\n\n

            Reports document key projects and provide an in-depth look at the applications and impacts of our tools, bridging the gap between theory and practice in geospatial science.<\/p>\n\n\n\n

            View our reports<\/summary>\n
              \n
            1. Aquaculture and Coastal Habitats Report No. 1 (link<\/a>)<\/li>\n\n\n\n
            2. Aquaculture and Coastal Habitats Report No. 2 (link<\/a>)<\/li>\n\n\n\n
            3. Aquaculture and Coastal Habitats Report No. 3 (link<\/a>)<\/li>\n\n\n\n
            4. Aquaculture and Coastal Habitats Report No. 4 (link<\/a>)<\/li>\n\n\n\n
            5. Aquaculture and Coastal Habitats Report No. 5 (link<\/a>)<\/li>\n\n\n\n
            6. Aquaculture and Coastal Habitats Report No. 6 (link<\/a>)<\/li>\n\n\n\n
            7. Supplementary Materials Report 6 (link<\/a>)<\/li>\n<\/ol>\n\n\n\n

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