top of page
Session : 8 Remote sensing and blue economy handled by Dr V. Ambili, Director - Geological Survey of India.
Highlights of the Session - 8
• The importance of ocean studies in the understanding of Earth systems and effect of natural and human-induced changes on global environment and how it was supported by remote sensing was addressed.
• Applications of remote sensing in ocean pattern identification like, currents, regional circulation patterns, shears ,frontal zones, internal waves, gravity waves, eddies, upwelling zones, shallow and deep bathymetry were briefed.
• The utilization of remote sensing in Oceans and coastal monitoring like, storm forecasting, wind and wave retrieval, fish stock, marine mammal assessment, water temperature monitoring, water quality, ocean productivity, phytoplankton concentration and drift, aqua culture inventory and monitoring, Oil spill mapping and predicting oil spill extent and drift, strategic support for oil spill emergency response decisions, shipping, navigation routing, traffic density studies, operational fisheries surveillance, near shore bathymetry mapping, inter tidal zone, tidal and storm effects, coastal vegetation mapping, etc. were explained in detail.
• The parameters needed for ocean monitoring and forecasting like temperature, currents, salinity, sea ice, sea level, wind, biogeochemistry, water quality parameters and some of critical marine issues such as, maritime safety and security, marine resources, coastal and marine environment, weather, climate and seasonal forecasting, over fishing, sea temperature rise, marine pollution, sea level rise, marine invasive species and their effects were elaborated.
• The different instruments and satellite sensors used for the analysis of ocean monitoring and forecasting parameters like, sea surface temperature, salinity, ocean currents, sea ice and biochemical parameters were narrated.
• The efficiency of remote sensing in the measurement of indicators of water quality such as suspended sediments, chl-a, DOM, temperature was explained.
• The various satellite borne sensors used for water quality like, CZCs, SeaWiFs, AISA (Airborne Imaging Spectrometer for Application), CASI (Compact Airborne Spectrographic Imager), Hymap, MODIS and MERIS, AVHRR, ALOS satellite were highlighted.
• The tools employed to detect and monitor oil spills. The airborne tools like SLAR (Side Looking Airborne Radar), LFS (Laser Flouro Sensor), MWR (Micro Wave Radiometry), IR/UV line scanner, FLIR (Forward Looking Infra-Red), Camera/Video and the satellite sensors such as SAR, optical sensors, Microwave RADAR were listed.
• Application of remote sensing in marine resources like, fish stock management, protection and sustainable management of living marine resources in particular for aqua culture, fishery research or regional fishery, fishing research, accessing and monitoring the level of contaminants in fish, finding favourable areas for fish farm were explained in brief.
• She illustrated marine pollution with the well-known example of the Great Pacific Garbage Patch. She also gave an insight on how to use sustainable energy sources such as solar energy, wind energy, hydro energy, tidal energy, geothermal energy, biomass energy in the place of hydrocarbons, coal and nuclear energy so as to reduce marine pollution.
• She also addressed the importance of blue economy and how it was associated with the economic growth of our country and also suggested the gathering to engage in studies on marine pollution so as to maintain the balance of ecosystem and economy of our country.
Session 9 : Machine learning for airborne surveillance applications handled by Dr R.Rajesh, Scientsit F - CABS-DRDO, Government of India.
Highlights of the Session - 9
• The significance of AI at international and national level were discussed with examples of major watershed moments in AI and gave examples where AI beats human in gaming, image recognition, etc.
• Important AI jargons – AI, ML, DL. Neural Networks and the steps for training Neural Networks were informed to the participants.
• Conventional Machine Learning (ML) techniques such as local regression, SVM, Decision trees, etc and its drawbacks are explained.
• Deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN) , etc along with its advantages.
• Both hardware and software tools are to be mastered and incorporated for the purpose.
• Airborne Surveillance System – eg. Mission Aircraft with sensors onboard are addressed with case studies.
• Automatic Target Recognition Systems (ATR) was elaborated under different modules such as ATR- multiclass object classification and ATR-multi object detection, ATR- Image segmentation, etc.
• The challenges & opportunities, trustworthiness, safety risk and mitigation aspects of AI.
• AI Eco-system organisation framework of various modules such as 1. Theoretical foundation 2. Domain knowledge 3. Data 4. Algorithms 5. Pipeline design 6. Hardware and prototyping 7. Integration 8. Test and evaluation. – all of which are inter-related were discussed in detail.
bottom of page