A5048737807- Oceanographic and Atmospheric Processes
- Atmospheric and Environmental Gas Dynamics
- Image and Signal Denoising Methods
- Geological Modeling and Analysis
- Meteorological Phenomena and Simulations
- Automated Road and Building Extraction
- Geochemistry and Geologic Mapping
- Flood Risk Assessment and Management
- Soil Geostatistics and Mapping
- Data Management and Algorithms
- Underwater Acoustics Research
- Geographic Information Systems Studies
- Advanced Neural Network Applications
- Advanced Image Processing Techniques
- Ocean Waves and Remote Sensing
Indian Institute of Science Bangalore
2020
Much of the progress in development highly adaptable and reusable artificial intelligence (AI) models is expected to have a profound impact on Earth science remote sensing. Foundation are pre-trained large unlabeled datasets through self-supervision, then fine-tuned for various downstream tasks with small labeled datasets. There an increasing interest within scientific community investigate how effectively build generalist AI that exploit multi-sensor data observation applications. This...
Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting coupled ocean–atmosphere systems. Today, some North Atlantic regional models, skilled human experts visualize extract gulf stream rings (warm cold eddies) through an extensive knowledge-based manual process. To automate this task, we develop a dynamics-inspired deep learning system that extracts Gulf Stream from concurrent satellite images sea surface temperature (SST) height...
Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science remote sensing. Foundation are pre-trained large unlabeled datasets through self-supervision, then fine-tuned for various downstream tasks with small labeled datasets. This paper introduces first-of-a-kind framework efficient pre-training fine-tuning foundational extensive geospatial data. We utilized this create Prithvi,...
Availability of high-resolution maps geophysical fields, devoid data loss due to clouds, is an urgent requirement for operational forecasting. We develop a Bayesian algorithm super-resolution (or downscaling) lower resolution fields observed by satellites. The key novelty in the present development and use Generative Adversarial Network (GAN) learn prior probability distribution from historical and/or model forecasts. trained GAN used sample particle filter along with low-resolution (as...
The rising concentration of Carbon dioxide (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) in the atmosphere is one main driving factors for global warming. This leading to many adverse impacts on planet Earth. Therefore, it critical accurately measure and reduce CO emissions. Remote sensing technologies offers a scalable solution estimate scale. However, estimates obtained from satellite such as Orbiting Observatory 2 (OCO2)...