Abhishek Potnis

About

I am a Researcher in the Geospatial Artificial Intelligence (GeoAI) group at Oak Ridge National Laboratory (ORNL).

I have completed my Doctoral Degree under the supervision of Prof. Surya Durbha in the GeoComputational Systems Lab at Indian Institute of Technology Bombay.

My research interests include Geospatial Knowledge Representation and Reasoning, Natural Language Processing, Deep Learning for Computer Vision, Satellite Image Processing, Remote Sensing and GIS and Internet Of Things.

My doctoral research explored the areas of Geospatial Semantics and Deep Learning for Satellite Image Processing, towards leveraging Knowledge Graphs for enhanced Scene Understanding of Remote Sensing Scenes.

As a part of the Google Summer of Earth Engine Research Program that concluded in August 2019, I worked on "Machine Learning based Mapping of Croplands with Google Earth Engine for Identifying Human-Wildlife Conflict Locations" with Centre for Wildlife Studies. I have been a two-time Google Summer of Code student in the past for the organizations - Cesium in 2015, where I worked on processing and visualizing 3-dimensional LiDAR data from the CALIPSO satellite; and Liquid Galaxy in 2016, where I worked on enabling Cesium for a panoramic experience on the Liquid Galaxy hardware.

I am an open source enthusiast and have been a code contributor to Mozilla Firefox. When not at my terminal or engrossed in a sci-fi novel, I enjoy travelling places, capturing and captioning the world through my camera.

Employment

• Oak Ridge National Laboratory Associate R&D Scientist
  2022 - Present
• Crowdworks Inc Artificial Intelligence Researcher
  2021 - 2022
• Indian Institute of Technology Bombay Teaching Assistant
  2014 - 2021

Education

• Indian Institute of Technology Bombay M. Tech + Ph. D. Dual Degree
  2014 - 2021
• University of Mumbai Bachelors in Engineering
  2009 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (37)

• Roadmap and benchmarking: Privacy in federated load forecasting
  Future Generation Computer Systems 2027 0 citations DOI: 10.1016/j.future.2026.108713
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• Emerging Flexible Designs for Geospatial Multimodal Foundation Models
  ACM Transactions on Spatial Algorithms and Systems 2026 0 citations DOI: 10.1145/3828541
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• SpaceNet 9-Cross-Sensor Alignment of Optical and SAR Imagery
  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2026 0 citations DOI: 10.1109/JSTARS.2026.3666103
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• Updates to USA Structures (Phase 3 Completion) 2024-2025 Technical Report
  2025 0 citations DOI: 10.2172/3001745
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• TECHNICAL PROGRESS TOWARDS SPACENET-9-CROSS-MODAL SATELLITE IMAGERY REGISTRATION FOR NATURAL DISASTER RESPONSES
  IGARSS 2025-2025 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM 2025 0 citations DOI: 10.1109/IGARSS55030.2025.11244021
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• TOWARDS DIVERSE AND REPRESENTATIVE GLOBAL PRETRAINING DATASETS FOR REMOTE SENSING FOUNDATION MODELS
  Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS) 2024 2 citations DOI: 10.1109/IGARSS53475.2024.10642466
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• TOWARDS INTERPRETABLE MACHINE LEARNING METRICS FOR EARTH OBSERVATION IMAGE ANALYSIS
  Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS) 2024 0 citations DOI: 10.1109/IGARSS53475.2024.10640512
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• Pretraining Billion-scale Geospatial Foundational Models on Frontier
  ArXiv 2024
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• Pretraining Billion-scale Geospatia Foundational Models on Frontier
  Ieee International Parallel and Distributed Processing Symposium Workshops, Ipdpsw 2024 4 citations DOI: 10.1109/IPDPSW63119.2024.00174
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• OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery
  Acm Sigspatial International Conference on Advances in Geographic Information Systems, Acm Sigspatial 2024 11 citations DOI: 10.1145/3678717.3691292
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