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Clark University
Center for
Geospatial Analytics

Peer-reviewed Papers

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Peer-reviewed articles represent the forefront of academic and applied research. These publications, vetted by experts, highlight the scientific foundations and advanced applications of our geospatial tools and technologies:

Journal Papers: 

  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)

  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)

  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)

Pre-Prints on arXiv: 

  1. Szwarcman D., Roy S., Fraccaro P., Gíslason Þ.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).
  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).
  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).
  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).

Conference Papers: 

  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)

  2. Lacoste A., Lehmann N., Rodriguez P., Sherwin E. D., Kerner H., Lütjens 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)

  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)