{"id":1533,"date":"2024-12-27T10:32:32","date_gmt":"2024-12-27T15:32:32","guid":{"rendered":"https:\/\/www.golive.clarku.edu\/faculty\/profiles\/hamed-alemohammad\/"},"modified":"2026-04-08T03:34:45","modified_gmt":"2026-04-08T07:34:45","slug":"hamed-alemohammad","status":"publish","type":"cu_faculty","link":"https:\/\/www.clarku.edu\/faculty\/profiles\/hamed-alemohammad\/","title":{"rendered":"Hamed Alemohammad"},"content":{"rendered":"

Hamed Alemohammad is an Associate Professor in the Graduate School of Geography and Director of the Center for Geospatial Analytics<\/a> at Clark University. He is a technical leader and interdisciplinary scholar with extensive expertise and knowledge in remote sensing, earth science, and artificial intelligence (AI). His research interest lies at the intersection of geospatial analytics\/AI and geography to use observations to better understand the changing Earth system.<\/p>\n

Hamed has been the PI for several projects focused on developing novel AI models for multispectral, microwave and synthetic aperture radar (SAR) satellite observations. In recent years, his research has been focused on development and application of geospatial foundation models. He also serves as a member of the Technical Advisory Committee<\/a> of Digital Earth Africa.<\/p>\n

Prior to Clark University, Hamed was the Chief Data Scientist and Executive Director at Radiant Earth where he established and led the development of Radiant MLHub – the open-access repository for geospatial training data and AI models. Hamed received his Ph.D. in Civil and Environmental Engineering from MIT.<\/p>\n","protected":false},"author":0,"featured_media":38944,"parent":0,"template":"","meta":{"cu_faculty_f180_userid":"C70305646","cu_faculty_first_name":"Hamed","cu_faculty_last_name":"Alemohammad","cu_faculty_employment_status":"Full Time","cu_faculty_rank":"Professor","cu_faculty_position":"Associate Professor
Director of Center for Geospatial Analytics","cu_faculty_phone":"","cu_faculty_email":"HAlemohammad@clarku.edu","cu_faculty_location":"","cu_faculty_about":"

Hamed Alemohammad is an Associate Professor in the Graduate School of Geography and Director of the Center for Geospatial Analytics<\/a> at Clark University. He is a technical leader and interdisciplinary scholar with extensive expertise and knowledge in remote sensing, earth science, and artificial intelligence (AI). His research interest lies at the intersection of geospatial analytics\/AI and geography to use observations to better understand the changing Earth system.<\/p>\n

Hamed has been the PI for several projects focused on developing novel AI models for multispectral, microwave and synthetic aperture radar (SAR) satellite observations. In recent years, his research has been focused on development and application of geospatial foundation models. He also serves as a member of the Technical Advisory Committee<\/a> of Digital Earth Africa.<\/p>\n

Prior to Clark University, Hamed was the Chief Data Scientist and Executive Director at Radiant Earth where he established and led the development of Radiant MLHub - the open-access repository for geospatial training data and AI models. Hamed received his Ph.D. in Civil and Environmental Engineering from MIT.<\/p>","cu_faculty_degrees":"Ph.D. in Civil and Environmental Engineering,<\/span> Massachusetts Institute of Technology, 2015\nM.S. in Water Resources Engineering,<\/span> Sharif University of Technology, 2009\nB.S. in Civil and Environmental Engineering,<\/span> Sharif University of Technology, 2007","cu_faculty_cv":"https:\/\/faculty180.interfolio.com\/public\/download.php?key=SDRwNCtxSUpsamxBQ213WS9ucHFuNnMwT0hzQU11b2RPQkJ2cWc3amxyUmNRdVVXTkF4MU1qN1RxWmlkSFdxenJsbm10d3B4V3U0dkh0TEkyZ2hmamFiMHhkaUZDY21qTWpUWjk0eHBvQktnaHJaZWxNSXI4Zz09","cu_faculty_links":"{\"Professional Website URL\":\"https:\\\/\\\/hamedalemo.github.io\\\/\",\"ORCID ID\":\"https:\\\/\\\/orcid.org\\\/0000-0001-5662-3643\",\"Google Scholar\":\"https:\\\/\\\/scholar.google.com\\\/citations?user=ysq7m-YAAAAJ\",\"Research Gate\":\"https:\\\/\\\/www.researchgate.net\\\/profile\\\/Hamed-Alemohammad-2\"}","cu_faculty_scholarly_interests":"","cu_faculty_scholarly_works":"[{\"activityid\":11980,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Yield estimation from SAR data using patch-based deep learning and machine learning techniques\",\"Journal Title\":\"Computers and Electronics in Agriculture\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"226\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"109340\",\"ISSN\":\"\",\"DOI\":\"10.1016\\\/j.compag.2024.109340\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/doi.org\\\/10.1016\\\/j.compag.2024.109340\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":11980,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Mahya G.Z. Hashemi\",\"Pang-Ning Tan\",\"Ehsan Jalilvand\",\"Brook Wilke\",\"Hamed Alemohammad\",\"Narendra N. Das\"],\"sort_date\":\"2024-9-01\"},{\"activityid\":11981,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications\",\"Journal Title\":\"\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2412.02732v1\",\"Description\":\"This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter models incorporate temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the 600M version outperforms the previous Prithvi-EO model by 8\\\\% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m). The results demonstrate the versatility of the model in both classical earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) are among the key factors that contributed to the project's success. In particular, SME involvement allowed for constant feedback on model and dataset design, as well as successful customization for diverse SME-led applications in disaster response, land use and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available on Hugging Face and IBM terratorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations. [Journal_ref: ]\",\"Include description in output citation\":1,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":11981,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Daniela Szwarcman\",\"Sujit Roy\",\"Paolo Fraccaro\",\"\\u00deorsteinn E G\\u00edslason\",\"Benedikt Blumenstiel\",\"Rinki Ghosal\",\"Pedro H de Oliveira\",\"Joao L Almeida\",\"Rocco Sedona\",\"Yanghui Kang\",\"Srija Chakraborty\",\"Sizhe Wang\",\"Ankur Kumar\",\"Myscon Truong\",\"Denys Godwin\",\"Hyunho Lee\",\"Chia-Yu Hsu\",\"Ata A Asanjan\",\"Besart Mujeci\",\"Trevor Keenan\",\"Paulo Arevalo\",\"Wenwen Li\",\"Hamed Alemohammad\",\"Pontus Olofsson\",\"Christopher Hain\",\"Robert Kennedy\",\"Bianca Zadrozny\",\"Gabriele Cavallaro\",\"Campbell Watson\",\"Manil Maskey\",\"Rahul Ramachandran\",\"Juan B Moreno\"],\"sort_date\":\"2024-9-01\"},{\"activityid\":11982,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps\",\"Journal Title\":\"\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2412.00777v2\",\"Description\":\"In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps has been facilitated by the increasing availability of earth observation data and advancements in geospatial machine learning. However, these global maps exhibit lower accuracy and inconsistencies in Africa, partly due to the lack of representative training data. To address this issue, we propose a data-centric framework with a teacher-student model setup, which uses diverse data sources of satellite images and label examples to produce local land-cover maps. Our method trains a high-resolution teacher model on images with a resolution of 0.331 m\\\/pixel and a low-resolution student model on publicly available images with a resolution of 10 m\\\/pixel. The student model also utilizes the teacher model's output as its weak label examples through knowledge transfer. We evaluated our framework using Murang'a county in Kenya, renowned for its agricultural productivity, as a use case. Our local models achieved higher quality maps, with improvements of 0.14 in the F1 score and 0.21 in Intersection-over-Union, compared to the best global model. Our evaluation also revealed inconsistencies in existing global maps, with a maximum agreement rate of 0.30 among themselves. Our work provides valuable guidance to decision-makers for driving informed decisions to enhance food security. [Journal_ref: ]\",\"Include description in output citation\":1,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":11982,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Girmaw A Tadesse\",\"Caleb Robinson\",\"Charles Mwangi\",\"Esther Maina\",\"Joshua Nyakundi\",\"Luana Marotti\",\"Gilles Q Hacheme\",\"Hamed Alemohammad\",\"Rahul Dodhia\",\"Juan L Ferres\"],\"sort_date\":\"2024-9-01\"},{\"activityid\":11983,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples\",\"Journal Title\":\"\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2408.06467v2\",\"Description\":\"The accuracy of mapping agricultural fields across large areas is steadily improving with high-resolution satellite imagery and deep learning (DL) models, even in regions where fields are small and geometrically irregular. However, developing effective DL models often requires large, expensive label datasets, typically available only for specific years or locations. This limits the ability to create annual maps essential for agricultural monitoring, as domain shifts occur between years and regions due to changes in farming practices and environmental conditions. The challenge is to design a model flexible enough to account for these shifts without needing yearly labels. While domain adaptation techniques or semi-supervised training are common solutions, we explored enhancing the model's generalization power. Our results indicate that a holistic approach is essential, combining methods to improve generalization. Specifically, using an area-based loss function, such as Tversky-focal loss (TFL), significantly improved predictions across multiple years. The use of different augmentation techniques helped to encode different types of invariance, particularly photometric augmentations encoded invariance to brightness changes, though they increased false positives. The combination of photometric augmentation, TFL loss, and MC-dropout produced the best results, although dropout alone led to more false negatives in subsequent year predictions. Additionally, the choice of input normalization had a significant impact, with the best results obtained when statistics were calculated either locally or across the entire dataset over all bands (lab and gab). We developed a workflow that enabled a U-Net model to generate effective multi-year crop maps over large areas. Our code, available at: https:\\\/\\\/github.com\\\/agroimpacts\\\/cnn-generalization-enhancement, will be regularly updated with improvements. [Journal_ref: ]\",\"Include description in output citation\":1,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":11983,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2024,\"termid\":\"2024\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Sam Khallaghi\",\"Rahebe Abedi\",\"Hanan A Ali\",\"Hamed Alemohammad\",\"Mary D Asipunu\",\"Ismail Alatise\",\"Nguyen Ha\",\"Boka Luo\",\"Cat Mai\",\"Lei Song\",\"Amos Wussah\",\"Sitian Xiong\",\"Yao-Ting Yao\",\"Qi Zhang\",\"Lyndon D Estes\"],\"sort_date\":\"2024-9-01\"},{\"activityid\":10819,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Advancing Foundation Models for Geospatial Applications with Scarce Reference Data\",\"Conference \\\/ Meeting Name\":\"Measuring Development\",\"Location of Conference \\\/ Meeting\":\"Washington, DC\",\"Month \\\/ Season\":\"May\",\"Year\":2024,\"Sponsoring Organization\":\"The World Bank\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10819,\"status\":\"Completed\\\/Published\",\"term\":\"May\",\"year\":2024,\"termid\":\"2023\\\/04\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-5-01\"},{\"activityid\":10820,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Advancements and Applications of Foundation Models for Earth Observations\",\"Conference \\\/ Meeting Name\":\"AWS Sustainability Speaker Series\",\"Location of Conference \\\/ Meeting\":\"Online\",\"Month \\\/ Season\":\"May\",\"Year\":2024,\"Sponsoring Organization\":\"AWS\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10820,\"status\":\"Completed\\\/Published\",\"term\":\"May\",\"year\":2024,\"termid\":\"2023\\\/04\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-5-01\"},{\"activityid\":10817,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Fine-Tuning Foundation Models for Downstream Applications of Remote Sensing Data\",\"Conference \\\/ Meeting Name\":\"AAG Annual Meeting\",\"Location of Conference \\\/ Meeting\":\"Honolulu, HI\",\"Month \\\/ Season\":\"April\",\"Year\":2024,\"Sponsoring Organization\":\"AAG\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10817,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2024,\"termid\":\"2023\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-4-01\"},{\"activityid\":10818,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"A New Era for Geospatial Analytics: Advancements and Applications of Foundation Models in Remote Sensing\",\"Conference \\\/ Meeting Name\":\"Harvard ABCD-GIS \\\/ Geography Colloquium \",\"Location of Conference \\\/ Meeting\":\"Cambridge, MA\",\"Month \\\/ Season\":\"March\",\"Year\":2024,\"Sponsoring Organization\":\"Harvard Center for Geographic Analysis\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/vimeo.com\\\/925164883\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10818,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2024,\"termid\":\"2023\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-3-01\"},{\"activityid\":8370,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Soil Moisture Profile Estimation by Combining P-band SAR Polarimetry with Hydrological and Multi-Layer Scattering Models\",\"Journal Title\":\"Remote Sensing of Environment\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"Elsevier\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8370,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2023,\"termid\":\"2022\\\/05\",\"listingorder\":6,\"completionorder\":6},{\"id\":8370,\"status\":\"Submitted\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":2,\"completionorder\":2}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Anke Fluhrer\",\"Thomas Jagdhuber\",\"Carsten Montzka\",\"Maike Schumacher\",\"Hamed Alemohammad\",\"Alireza Tabatabaeenejad\",\"Harald Kunstmann\",\"Dara Entekhabi\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":10746,\"fields\":{\"Type\":\"Papers Published - Conference Proceedings\",\"Title of Paper\":\"Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model\",\"Title of Published Proceedings\":\"\",\"Title of Conference\":\"\",\"Conference Location\":\"\",\"Month \\\/ Season\":\"\",\"Year\":null,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Numbers\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2404.19609v1\",\"Description\":\"Filling cloudy pixels in multispectral satellite imagery is essential for accurate data analysis and downstream applications, especially for tasks which require time series data. To address this issue, we compare the performance of a foundational Vision Transformer (ViT) model with a baseline Conditional Generative Adversarial Network (CGAN) model for missing value imputation in time series of multispectral satellite imagery. We randomly mask time series of satellite images using real-world cloud masks and train each model to reconstruct the missing pixels. The ViT model is fine-tuned from a pretrained model, while the CGAN is trained from scratch. Using quantitative evaluation metrics such as structural similarity index and mean absolute error as well as qualitative visual analysis, we assess imputation accuracy and contextual preservation. [Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10746,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2024,\"termid\":\"2023\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Denys Godwin\",\"Hanxi Li\",\"Michael Cecil\",\"Hamed Alemohammad\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":10781,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"<span style="font-size:10pt;">A Systematic Review of Synthetic Aperture Radar and Deep Learning in Agricultural Applications<\\\/span><span style="font-size:medium;"><\\\/span>\",\"Journal Title\":\"Isprs Journal of Photogrammetry and Remote Sensing\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2024,\"Publisher\":\"Elsevier\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"218\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"20-49\",\"ISSN\":\"\",\"DOI\":\"10.1016\\\/j.isprsjprs.2024.08.018\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/doi.org\\\/10.1016\\\/j.isprsjprs.2024.08.018\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10781,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2024,\"termid\":\"2023\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Mahya Ghazi Zadeh Hashemi\",\"Ehsan Jalilvand\",\"Hamed Alemohammad\",\"Pang-Ning Tan\",\"Narenda Das\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":10813,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Applications of Foundation Models in Earth Sciences Applied to Remote Sensing Imagery\",\"Conference \\\/ Meeting Name\":\"Second U.S.-Africa Frontiers of Science, Engineering, and Medicine Symposium\",\"Location of Conference \\\/ Meeting\":\"Rabat, Morocco\",\"Month \\\/ Season\":\"January\",\"Year\":2024,\"Sponsoring Organization\":\"National Academies of Science, Engineering and Medicine\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10813,\"status\":\"Completed\\\/Published\",\"term\":\"Intersession\",\"year\":2024,\"termid\":\"2023\\\/02\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":10814,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Transforming Earth Observation Analytics: Advancements and Applications of Foundation Models in Remote Sensing\",\"Conference \\\/ Meeting Name\":\"IEEE\\\/CVF Winter Conference on Applications of Computer Vision (WACV) \",\"Location of Conference \\\/ Meeting\":\"Waikoloa, HI\",\"Month \\\/ Season\":\"January\",\"Year\":2024,\"Sponsoring Organization\":\"IEEE\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/geoai.ornl.gov\\\/cv4eo-wacv\\\/\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10814,\"status\":\"Completed\\\/Published\",\"term\":\"Intersession\",\"year\":2024,\"termid\":\"2023\\\/02\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2024-1-01\"},{\"activityid\":11984,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Foundation Models for Generalist Geospatial Artificial Intelligence\",\"Journal Title\":\"\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2023,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2310.18660v2\",\"Description\":\"Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets through self-supervision, and then fine-tuned for various downstream tasks with small labeled datasets. This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data. We have utilized this framework to create Prithvi, a transformer-based geospatial foundational model pre-trained on more than 1TB of multispectral satellite imagery from the Harmonized Landsat-Sentinel 2 (HLS) dataset. Our study demonstrates the efficacy of our framework in successfully fine-tuning Prithvi to a range of Earth observation tasks that have not been tackled by previous work on foundation models involving multi-temporal cloud gap imputation, flood mapping, wildfire scar segmentation, and multi-temporal crop segmentation. Our experiments show that the pre-trained model accelerates the fine-tuning process compared to leveraging randomly initialized weights. In addition, pre-trained Prithvi compares well against the state-of-the-art, e.g., outperforming a conditional GAN model in multi-temporal cloud imputation by up to 5pp (or 5.7%) in the structural similarity index. Finally, due to the limited availability of labeled data in the field of Earth observation, we gradually reduce the quantity of available labeled data for refining the model to evaluate data efficiency and demonstrate that data can be decreased significantly without affecting the model's accuracy. The pre-trained 100 million parameter model and corresponding fine-tuning workflows have been released publicly as open source contributions to the global Earth sciences community through Hugging Face. [Journal_ref: ]\",\"Include description in output citation\":1,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":11984,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2023,\"termid\":\"2023\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Johannes Jakubik\",\"Sujit Roy\",\"C. E Phillips\",\"Paolo Fraccaro\",\"Denys Godwin\",\"Bianca Zadrozny\",\"Daniela Szwarcman\",\"Carlos Gomes\",\"Gabby Nyirjesy\",\"Blair Edwards\",\"Daiki Kimura\",\"Naomi Simumba\",\"Linsong Chu\",\"Karthik S Mukkavilli\",\"Devyani Lambhate\",\"Kamal Das\",\"Ranjini Bangalore\",\"Dario Oliveira\",\"Michal Muszynski\",\"Kumar Ankur\",\"Muthukumaran Ramasubramanian\",\"Iksha Gurung\",\"Sam Khallaghi\",\"Michael Cecil\",\"Maryam Ahmadi\",\"Fatemeh Kordi\",\"Hamed Alemohammad\",\"Manil Maskey\",\"Raghu Ganti\",\"Kommy Weldemariam\",\"Rahul Ramachandran\"],\"sort_date\":\"2023-9-01\"},{\"activityid\":10810,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Machine Learning Applications with Earth Observations\",\"Conference \\\/ Meeting Name\":\"GIZ FAIR Forward Training on ML4EO\",\"Location of Conference \\\/ Meeting\":\"Kigali, Rwanda\",\"Month \\\/ Season\":\"June\",\"Year\":2023,\"Sponsoring Organization\":\"GIZ\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10810,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2023,\"termid\":\"2022\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-6-01\"},{\"activityid\":10811,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Panel on Geospatial\\u00a0Edge Computing\",\"Conference \\\/ Meeting Name\":\"\",\"Location of Conference \\\/ Meeting\":\"Oak Ridge, TN\",\"Month \\\/ Season\":\"June\",\"Year\":2023,\"Sponsoring Organization\":\"Oak Ridge National Lab\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/geoai.ornl.gov\\\/trillion-pixel\\\/\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10811,\"status\":\"Completed\\\/Published\",\"term\":\"Summer\",\"year\":2023,\"termid\":\"2022\\\/05\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-6-01\"},{\"activityid\":8320,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery\",\"Conference \\\/ Meeting Name\":\"EGU General Assembly\",\"Location of Conference \\\/ Meeting\":\"Vienna, Austria\",\"Month \\\/ Season\":\"April\",\"Year\":2023,\"Sponsoring Organization\":\"\",\"CoAuthor\":null,\"URL\":\"https:\\\/\\\/doi.org\\\/10.5194\\\/egusphere-egu23-10528\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8320,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-4-01\"},{\"activityid\":8322,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"A New Era for Geospatial Analytics\",\"Conference \\\/ Meeting Name\":\"The Graduate School of Geography Centennial\",\"Location of Conference \\\/ Meeting\":\"Worcester, MA\",\"Month \\\/ Season\":\"April \",\"Year\":2023,\"Sponsoring Organization\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8322,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-4-01\"},{\"activityid\":8321,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Amplifying Impact through Collaborative Ecosystems for Geospatial Analytics\",\"Conference \\\/ Meeting Name\":\"GeoBuiz Summit\",\"Location of Conference \\\/ Meeting\":\"Monterey, CA\",\"Month \\\/ Season\":\"March\",\"Year\":2023,\"Sponsoring Organization\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8321,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2023,\"termid\":\"2022\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-3-01\"},{\"activityid\":10747,\"fields\":{\"Type\":\"Papers Published - Conference Proceedings\",\"Title of Paper\":\"GEO-Bench: Toward Foundation Models for Earth Monitoring\",\"Title of Published Proceedings\":\"2023 NeurIPS\",\"Title of Conference\":\"2023 NeurIPS\",\"Conference Location\":\"\",\"Month \\\/ Season\":\"Dec\",\"Year\":2023,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Numbers\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2306.03831v2\",\"Description\":\"Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks. [Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10747,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2023,\"termid\":\"2023\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Alexandre Lacoste\",\"Nils Lehmann\",\"Pau Rodriguez\",\"Evan D Sherwin\",\"Hannah Kerner\",\"Bj\\u00f6rn L\\u00fctjens\",\"Jeremy A Irvin\",\"David Dao\",\"Hamed Alemohammad\",\"Alexandre Drouin\",\"Mehmet Gunturkun\",\"Gabriel Huang\",\"David Vazquez\",\"Dava Newman\",\"Yoshua Bengio\",\"Stefano Ermon\",\"Xiao X Zhu\"],\"sort_date\":\"2023-12-01\"},{\"activityid\":10807,\"fields\":{\"Type\":\"Presentations\",\"Title of Presentation\":\"Exploring Effectiveness of Foundation Models for Downstream Applications on Satellite Data\",\"Conference \\\/ Meeting Name\":\"AGU Fall Meeting\",\"Location of Conference \\\/ Meeting\":\"San Francisco, 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Geography\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"Manual\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10809,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2023,\"termid\":\"2023\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\"],\"sort_date\":\"2023-11-01\"},{\"activityid\":8319,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Tropical cyclone wind speed estimation: A large scale training data set and community benchmarking\",\"Journal Title\":\"Earth and Space Science\",\"Series Title\":\"\",\"Month \\\/ Season\":\"\",\"Year\":2023,\"Publisher\":\"\",\"Publisher City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"10\",\"Issue Number \\\/ Edition\":\"3\",\"Page Number(s) or Number of 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City and State\":\"\",\"Publisher Country\":\"\",\"Volume\":\"14\",\"Issue Number \\\/ Edition\":\"10\",\"Page Number(s) or Number of Pages\":\"2410\",\"ISSN\":\"\",\"DOI\":\"\",\"CoAuthor\":null,\"URL\":\"\",\"Description\":\"\",\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8318,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2022,\"termid\":\"2022\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Christian Tottrup\",\"Daniel Druce\",\"Rasmus Probst Meyer\",\"Mads Christensen\",\"Michael Riffler\",\"Bjoern Dulleck\",\"Philipp Rastner\",\"Katerina Jupova\",\"Tomas Sokoup\",\"Arjen Haag\",\"Hamed Alemohammad\",\" others\"],\"sort_date\":\"2022-9-01\"},{\"activityid\":10748,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Toward Foundation Models for Earth Monitoring: Proposal for a Climate\\n Change Benchmark\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2112.00570v1\",\"Description\":\"Recent progress in self-supervision shows that pre-training large neural\\nnetworks on vast amounts of unsupervised data can lead to impressive increases\\nin generalisation for downstream tasks. Such models, recently coined as\\nfoundation models, have been transformational to the field of natural language\\nprocessing. While similar models have also been trained on large corpuses of\\nimages, they are not well suited for remote sensing data. To stimulate the\\ndevelopment of foundation models for Earth monitoring, we propose to develop a\\nnew benchmark comprised of a variety of downstream tasks related to climate\\nchange. We believe that this can lead to substantial improvements in many\\nexisting applications and facilitate the development of new applications. This\\nproposal is also a call for collaboration with the aim of developing a better\\nevaluation process to mitigate potential downsides of foundation models for\\nEarth monitoring.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10748,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2021,\"termid\":\"2021\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Alexandre Lacoste\",\"Evan D Sherwin\",\"Hannah Kerner\",\"Hamed Alemohammad\",\"Bj\\u00f6rn L\\u00fctjens\",\"Jeremy Irvin\",\"David Dao\",\"Alex Chang\",\"Mehmet Gunturkun\",\"Alexandre Drouin\",\"Pau Rodriguez\",\"David Vazquez\"],\"sort_date\":\"2021-9-01\"},{\"activityid\":8317,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Accounting for training data error in machine learning applied to Earth observations\",\"Journal Title\":\"Remote Sensing\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2020,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"12\",\"Issue Number \\\/ Edition\":\"6\",\"Page Number(s) or Number of Pages\":\"1034\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8317,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2020,\"termid\":\"2020\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Arthur Elmes\",\"Hamed Alemohammad\",\"Ryan Avery\",\"Kelly Caylor\",\"J Ronald Eastman\",\"Lewis Fishgold\",\"Mark A Friedl\",\"Meha Jain\",\"Divyani Kohli\",\"Bayas, Laso\",\" others\"],\"sort_date\":\"2020-9-01\"},{\"activityid\":10749,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"LandCoverNet: A global benchmark land cover classification training\\n dataset\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2012.03111v1\",\"Description\":\"Regularly updated and accurate land cover maps are essential for monitoring\\n14 of the 17 Sustainable Development Goals. Multispectral satellite imagery\\nprovide high-quality and valuable information at global scale that can be used\\nto develop land cover classification models. However, such a global application\\nrequires a geographically diverse training dataset. Here, we present\\nLandCoverNet, a global training dataset for land cover classification based on\\nSentinel-2 observations at 10m spatial resolution. Land cover class labels are\\ndefined based on annual time-series of Sentinel-2, and verified by consensus\\namong three human annotators.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10749,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2020,\"termid\":\"2020\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Hamed Alemohammad\",\"Kevin Booth\"],\"sort_date\":\"2020-9-01\"},{\"activityid\":10750,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Generating Synthetic Multispectral Satellite Imagery from Sentinel-2\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2012.03108v1\",\"Description\":\"Multi-spectral satellite imagery provides valuable data at global scale for\\nmany environmental and socio-economic applications. Building supervised machine\\nlearning models based on these imagery, however, may require ground reference\\nlabels which are not available at global scale. Here, we propose a generative\\nmodel to produce multi-resolution multi-spectral imagery based on Sentinel-2\\ndata. The resulting synthetic images are indistinguishable from real ones by\\nhumans. This technique paves the road for future work to generate labeled\\nsynthetic imagery that can be used for data augmentation in data scarce regions\\nand applications.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10750,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2020,\"termid\":\"2020\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Tharun Mohandoss\",\"Aditya Kulkarni\",\"Daniel Northrup\",\"Ernest Mwebaze\",\"Hamed Alemohammad\"],\"sort_date\":\"2020-9-01\"},{\"activityid\":10751,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Semantic Segmentation of Medium-Resolution Satellite Imagery using\\n Conditional Generative Adversarial Networks\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2012.03093v1\",\"Description\":\"Semantic segmentation of satellite imagery is a common approach to identify\\npatterns and detect changes around the planet. Most of the state-of-the-art\\nsemantic segmentation models are trained in a fully supervised way using\\nConvolutional Neural Network (CNN). The generalization property of CNN is poor\\nfor satellite imagery because the data can be very diverse in terms of\\nlandscape types, image resolutions, and scarcity of labels for different\\ngeographies and seasons. Hence, the performance of CNN doesn't translate well\\nto images from unseen regions or seasons. Inspired by Conditional Generative\\nAdversarial Networks (CGAN) based approach of image-to-image translation for\\nhigh-resolution satellite imagery, we propose a CGAN framework for land cover\\nclassification using medium-resolution Sentinel-2 imagery. We find that the\\nCGAN model outperforms the CNN model of similar complexity by a significant\\nmargin on an unseen imbalanced test dataset.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10751,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2020,\"termid\":\"2020\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Aditya Kulkarni\",\"Tharun Mohandoss\",\"Daniel Northrup\",\"Ernest Mwebaze\",\"Hamed Alemohammad\"],\"sort_date\":\"2020-9-01\"},{\"activityid\":10752,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Proceedings of the ICLR Workshop on Computer Vision for Agriculture\\n (CV4A) 2020\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/2004.11051v3\",\"Description\":\"This is the proceedings of the Computer Vision for Agriculture (CV4A)\\nWorkshop that was held in conjunction with the International Conference on\\nLearning Representations (ICLR) 2020.\\n The Computer Vision for Agriculture (CV4A) 2020 workshop was scheduled to be\\nheld in Addis Ababa, Ethiopia, on April 26th, 2020. It was held virtually that\\nsame day due to the COVID-19 pandemic. The workshop was held in conjunction\\nwith the International Conference on Learning Representations (ICLR) 2020.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10752,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2020,\"termid\":\"2019\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Yannis Kalantidis\",\"Laura Sevilla-Lara\",\"Ernest Mwebaze\",\"Dina Machuve\",\"Hamed Alemohammad\",\"David Guerena\"],\"sort_date\":\"2020-1-01\"},{\"activityid\":8310,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Physics-Based Modeling of Active and Passive Microwave Covariations Over Vegetated Surfaces\",\"Journal Title\":\"IEEE Transactions on geoscience and remote sensing\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2019,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"57\",\"Issue Number \\\/ Edition\":\"2\",\"Page Number(s) or Number of Pages\":\"788--802\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8310,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2019,\"termid\":\"2019\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Thomas Jagdhuber\",\"Alexandra G Konings\",\"Kaighin A McColl\",\"Seyed Hamed Alemohammad\",\"Narendra Narayan Das\",\"Carsten Montzka\",\"Moritz Link\",\"Ruzbeh Akbar\",\"Dara Entekhabi\"],\"sort_date\":\"2019-9-01\"},{\"activityid\":8314,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy\",\"Journal Title\":\"Journal of Hydrology\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2019,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":null,\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":null,\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8314,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2019,\"termid\":\"2019\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Bahram Khazaei\",\"Sina Khatami\",\"Seyed Hamed Alemohammad\",\"Lida Rashidi\",\"Changshan Wu\",\"Kaveh Madani\",\"Zahra Kalantari\",\"Georgia Destouni\",\"Amir Aghakouchak\"],\"sort_date\":\"2019-9-01\"},{\"activityid\":8315,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Soil and Vegetation Scattering Contributions in L-Band and P-Band Polarimetric SAR Observations\",\"Journal Title\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2019,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":null,\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":null,\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8315,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2019,\"termid\":\"2019\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"S. H. Alemohammad\",\"T. Jagdhuber\",\"M. Moghaddam\",\"D. 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Konings\",\"Daniel Kennedy\",\"Seyed Hamed Alemohammad\",\"Rafael S. Oliveira\",\"Maria Uriarte\",\"Pierre Gentine\"],\"sort_date\":\"2018-9-01\"},{\"activityid\":8313,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks\",\"Journal Title\":\"Biogeosciences\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2018,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"15\",\"Issue Number \\\/ Edition\":\"19\",\"Page Number(s) or Number of Pages\":\"5779--5800\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8313,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2018,\"termid\":\"2018\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Yao Zhang\",\"Joanna Joiner\",\"Seyed Hamed Alemohammad\",\"Sha Zhou\",\"Pierre Gentine\"],\"sort_date\":\"2018-9-01\"},{\"activityid\":10753,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Generating a Training Dataset for Land Cover Classification to Advance\\n Global Development\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/1811.07998v1\",\"Description\":\"Semantic segmentation of land cover classes is fundamental for agricultural\\nand economic development work, from sustainable forestry to urban planning, yet\\nexisting training datasets have significant limitations. To generate an open\\nand comprehensive training library of high resolution Earth imagery and high\\nquality land cover classifications, public Sentinel-2 data at 10 m spatial\\nresolution was matched with accurate GlobeLand30 labels from 2010, which were\\nfiltered by agreement with an intermediary Sentinel-2 classification at 20 m\\nproduced during atmospheric correction. Scene-level classifications were\\npredicted by Random Forests trained on valid reflectance data and the filtered\\nlabels, and achieved over 80% model accuracy for a variety of locations.\\nFurther work is required to aggregate individual scene classifications for\\nannual labels and to test the approach in more locations, before crowdsourcing\\nhuman validation. The goal is to create a sustained community-wide effort to\\ngenerate image labels not only for land cover, but also very specific images\\nfor major agriculture crops across the world and other thematic categories of\\ninterest to the global development community.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10753,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2018,\"termid\":\"2018\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Yoni Nachmany\",\"Hamed Alemohammad\"],\"sort_date\":\"2018-9-01\"},{\"activityid\":8304,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence\",\"Journal Title\":\"Biogeosciences\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2017,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":null,\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":null,\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8304,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2017,\"termid\":\"2017\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Seyed Hamed Alemohammad\",\"Bin Fang\",\"Alexandra G Konings\",\"Julia K Green\",\"Jana Kolassa\",\"Catherine Prigent\",\"Filipe Aires\",\"Diego Miralles\",\"Pierre Gentine\"],\"sort_date\":\"2017-9-01\"},{\"activityid\":8305,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"The global distribution and dynamics of surface soil moisture\",\"Journal Title\":\"Nature Geoscience\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2017,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"10\",\"Issue Number \\\/ Edition\":\"2\",\"Page Number(s) or Number of Pages\":\"100--104\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8305,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2017,\"termid\":\"2017\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Kaighin A McColl\",\"Seyed Hamed Alemohammad\",\"Ruzbeh Akbar\",\"Alexandra G Konings\",\"Simon Yueh\",\"Dara Entekhabi\"],\"sort_date\":\"2017-9-01\"},{\"activityid\":8306,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation\",\"Journal Title\":\"Remote Sensing of Environment\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2017,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"195\",\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":\"202--217\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8306,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2017,\"termid\":\"2017\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"J Kolassa\",\"P Gentine\",\"C Prigent\",\"F Aires\",\"S H Alemohammad\"],\"sort_date\":\"2017-9-01\"},{\"activityid\":8307,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Regionally strong feedbacks between the atmosphere and terrestrial biosphere\",\"Journal Title\":\"Nature geoscience\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2017,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"10\",\"Issue Number \\\/ Edition\":\"6\",\"Page Number(s) or Number of Pages\":\"410--414\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8307,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2017,\"termid\":\"2017\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Julia K Green\",\"Alexandra G Konings\",\"Seyed Hamed Alemohammad\",\"Joseph Berry\",\"Dara Entekhabi\",\"Jana Kolassa\",\"Jung-Eun Lee\",\"Pierre Gentine\"],\"sort_date\":\"2017-9-01\"},{\"activityid\":8302,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Triple collocation for binary and categorical variables: Application to validating landscape freeze\\\/thaw retrievals\",\"Journal Title\":\"Remote Sensing of Environment\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2016,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"176\",\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":\"31--42\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8302,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2016,\"termid\":\"2016\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"K. A. McColl\",\"A. Roy\",\"C. Derksen\",\"A. G. Konings\",\"S. H. Alemohammad\",\"D. Entekhabi\"],\"sort_date\":\"2016-9-01\"},{\"activityid\":10754,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Characterization of Vegetation and Soil Scattering Mechanisms across\\n Different Biomes using P-band SAR Polarimetry\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/1611.02729v2\",\"Description\":\"Understanding the scattering mechanisms from the ground surface in the\\npresence of different vegetation densities is necessary for the interpretation\\nof P-band Synthetic Aperture Radar (SAR) observations and for the design of\\ngeophysical retrieval algorithms. In this study, a quantitative analysis of\\nvegetation and soil scattering mechanisms estimated from the observations of an\\nairborne P-band SAR instrument across nine different biomes in North America is\\npresented. The goal is to apply a hybrid (model- and eigen- based) three\\ncomponent decomposition approach to separate the contributions of surface,\\ndouble-bounce and vegetation volume scattering across a wide range of biome\\nconditions. The decomposition makes no prior assumptions about vegetation\\nstructure. We characterize the dynamics of the decomposition across different\\nNorth American biomes and assess their characteristic range. Impacts of\\nvegetation cover seasonality and soil surface roughness on the contributions of\\neach scattering mechanism are also investigated. Observations used here are\\npart of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface\\n(AirMOSS) mission and data have been collected between 2013 and 2015.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10754,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2016,\"termid\":\"2016\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Seyed H Alemohammad\",\"Alexandra G Konings\",\"Thomas Jagdhuber\",\"Mahta Moghaddam\",\"Dara Entekhabi\"],\"sort_date\":\"2016-9-01\"},{\"activityid\":8300,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Quantifying Precipitation Uncertainty for Land Data Assimilation Applications\",\"Journal Title\":\"Monthly Weather Review\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2015,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"143\",\"Issue Number \\\/ Edition\":\"8\",\"Page Number(s) or Number of Pages\":\"3276--3299\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8300,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2015,\"termid\":\"2015\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Seyed Hamed Alemohammad\",\"Dennis B McLaughlin\",\"Dara Entekhabi\"],\"sort_date\":\"2015-9-01\"},{\"activityid\":8301,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Characterization of precipitation product errors across the United States using multiplicative triple collocation\",\"Journal Title\":\"Hydrology and Earth System Sciences\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2015,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"19\",\"Issue Number \\\/ Edition\":\"8\",\"Page Number(s) or Number of Pages\":\"3489--3503\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8301,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2015,\"termid\":\"2015\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"S H Alemohammad\",\"K A McColl\",\"A G Konings\",\"D Entekhabi\",\"A Stoffelen\"],\"sort_date\":\"2015-9-01\"},{\"activityid\":8297,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Ensemble-based characterization of uncertain environmental features\",\"Journal Title\":\"Advances in water resources\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2014,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"70\",\"Issue Number \\\/ Edition\":null,\"Page Number(s) or Number of Pages\":\"36--50\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8297,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2014,\"termid\":\"2014\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Rafa\\\\l W\\\\'ojcik\",\"Dennis McLaughlin\",\"Seyed Hamed Alemohammad\",\"Dara Entekhabi\"],\"sort_date\":\"2014-9-01\"},{\"activityid\":8298,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Evaluation of long-term SSM\\\/I-based precipitation records over land\",\"Journal Title\":\"Journal of Hydrometeorology\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":2014,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"15\",\"Issue Number \\\/ Edition\":\"5\",\"Page Number(s) or Number of Pages\":\"2012--2029\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":null,\"Description\":null,\"Include description in output citation\":0,\"Origin\":\"BibTeX\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":8298,\"status\":\"Completed\\\/Published\",\"term\":\"Fall\",\"year\":2014,\"termid\":\"2014\\\/01\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Seyed Hamed Alemohammad\",\"Dara Entekhabi\",\"Dennis B McLaughlin\"],\"sort_date\":\"2014-9-01\"},{\"activityid\":10755,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"A Framework for Modelling Probabilistic Uncertainty in Rainfall Scenario\\n Analysis\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/1304.4302v1\",\"Description\":\"Predicting future probable values of model parameters, is an essential\\npre-requisite for assessing model decision reliability in an uncertain\\nenvironment. Scenario Analysis is a methodology for modelling uncertainty in\\nwater resources management modelling. Uncertainty if not considered\\nappropriately in decision making will decrease reliability of decisions,\\nespecially in long-term planning. One of the challenges in Scenario Analysis is\\nhow scenarios are made. One of the most approved methods is statistical\\nmodelling based on Auto-Regressive models. Stream flow future scenarios in\\ndeveloped basins that human has made changes to the natural flow process could\\nnot be generated directly by ARMA modelling. In this case, making scenarios for\\nmonthly rainfall and using it in a water resources system model makes more\\nsense. Rainfall is an ephemeral process which has zero values in some months\\nwhich introduces some limitations in making use of monthly ARMA model.\\nTherefore, a two stage modelling approach is adopted here which in the first\\nstage yearly modelling is done. Within this yearly model three ranges are\\nidentified: Dry, Normal and Wet. In the normal range yearly ARMA modelling is\\nused. Dry and Wet range are considered as random processes and are modeled by\\nfrequency analysis. Monthly distribution of rainfall, which is extracted from\\navailable data from a moving average are considered to be deterministic and\\nfixed in time. Each rainfall scenario is composed of a yearly ARMA process\\nsuper-imposed by dry and wet events according to the frequency analysis. This\\nmodelling framework is applied to available data from three rain-gauge stations\\nin Iran. Results show this modelling approach has better consistency with\\nobserved data in comparison with making use of ARMA modelling alone.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10755,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2013,\"termid\":\"2012\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Seyed H Alemohammad\",\"Reza Ardakanian\",\"Akbar Karimi\"],\"sort_date\":\"2013-1-01\"},{\"activityid\":10756,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Global Warming and Caspian Sea Level Fluctuations\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/1304.3411v2\",\"Description\":\"Coastal regions have a high social, economical and environmental importance.\\nDue to this importance the sea level fluctuations can have many bad\\nconsequences. In this research the correlation between the increasing trend of\\ntemperature in coastal stations due to Global Warming and the Caspian Sea level\\nhas been established. The Caspian Sea level data has been received from the\\nJason-1 satellite. It was resulted that the monthly correlation between the\\ntemperature and sea level is high and also positive and almost the same for all\\nthe stations. But the yearly correlation was negative. It means that the sea\\nlevel has decreased by the increase in temperature.\\n[Journal_ref: ]\",\"Include description in output citation\":0,\"Origin\":\"arXiv\"},\"facultyid\":\"C70305646\",\"status\":[{\"id\":10756,\"status\":\"Completed\\\/Published\",\"term\":\"Spring\",\"year\":2013,\"termid\":\"2012\\\/03\",\"listingorder\":6,\"completionorder\":6}],\"userid\":\"C70305646\",\"attachments\":[],\"coauthors_list\":[\"Reza Ardakanian\",\"Seyed H Alemohammad\"],\"sort_date\":\"2013-1-01\"},{\"activityid\":10757,\"fields\":{\"Type\":\"Articles in Refereed Journals\",\"Title\":\"Merging Satellite Measurements of Rainfall Using Multi-scale Imagery\\n Technique\",\"Journal Title\":\"\",\"Series Title\":null,\"Month \\\/ Season\":null,\"Year\":null,\"Publisher\":null,\"Publisher City and State\":null,\"Publisher Country\":null,\"Volume\":\"\",\"Issue Number \\\/ Edition\":\"\",\"Page Number(s) or Number of Pages\":\"\",\"ISSN\":null,\"DOI\":null,\"CoAuthor\":null,\"URL\":\"http:\\\/\\\/arxiv.org\\\/abs\\\/1304.3406v1\",\"Description\":\"Several passive microwave satellites orbit the Earth and measure rainfall.\\nThese measurements have the advantage of almost full global coverage when\\ncompared to surface rain gauges. However, these satellites have low temporal\\nrevisit and missing data over some regions. Image fusion is a useful technique\\nto fill in the gaps of one image (one satellite measurement) using another one.\\nThe proposed algorithm uses an iterative fusion scheme to integrate information\\nfrom two satellite measurements. The algorithm is implemented on two datasets\\nfor 7 years of half-hourly data. 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FM are trained on large-scale input data in a self-supervised framework and are not necessarily designed for specific tasks such as classification, segmentation, or object detection. The trained foundational model can then be adapted for downstream tasks using a limited sample of training data.<\\\/p>\\n<p>To assess the value of these models in Earth Observation (EO) applications, a team of researchers from NASA and IBM have designed and developed a FM model using Harmonized Landsat Sentinel (HLS) multi-spectral data. The model is currently trained over an area of ~4,000 sq km covering states of Texas, Louisiana, Mississippi, Arkansas and Oklahoma (this will be referred to as the model area of interest, AOI). The input imagery to the model is a temporal sequence of 3-4 cloud-free HLS images at 30m spatial resolution with RGB and NIR bands.<\\\/p>\\n<p>In this project, we propose to design and implement an evaluation framework for downstream tasks using this FM model. 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