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Application of Machine Learning Methods to improve vertical accuracy of CARTOSAT DEM
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  • Venkatesh Kasi,
  • Pavan Kumar Yeditha,
  • Rathinasamy Maheswaran,
  • Chandramouli Sangamreddi
Venkatesh Kasi
School of Infrastructure, Indian Institute of Technology (IIT) Bhubaneshwar, India

Corresponding Author:20wr06009@iitbbs.ac.in

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Pavan Kumar Yeditha
UNESCO-IHE Institute for Water Education, Netherlands
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Rathinasamy Maheswaran
Department of Civil Engineering, Indian Institute of Technology (IIT) Hyderabad, India
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Chandramouli Sangamreddi
MVGR College of Engineering, AndhraPradesh, India
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In recent decades, the application of Digital Elevation Models (DEMs) has been widely used in various aspects such as land management and flood planning since it reflects the actual topographic characteristic on the Earth’s surface. However, obtaining a high-quality DEM is often quite challenging because it is time-consuming, costly, and often confidential. This study presents an innovative approach to derive an improved vertical accuracy of CARTOSAT 10m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods such as Genetic Programming (GP) and Artificial Neural Networks (ANN). SRTM-1 DEM and CARTOSAT DEM in India are applied to GP and ANN to generate improved vertical accuracy high-quality DEM. The results revealed that the proposed approach improves the vertical accuracy by considering the reference as Ground control Points (GCPs) elevation from Differential Global Positioning System (DGPS) survey data. A significant improvement of 47 and 35% generated DEMs in RMSE compared to the SRTM-1 and CARTOSAT, respectively.