Parth Naik

Employment

• Helmholtz-Zentrum Dresden-Rossendorf Scientific Employee
  2024 - 2025
• EURAC Research - Institute for Renewable Energy Geospatial Data Scientist
  2023 - 2024
• Fondazione Edmund Mach Researcher
  2018 - 2022

Education

• University of Trento Doctor of Philosophy
  2018 - 2022
• Indian Institute of Remote Sensing Master of Technology
  2016 - 2018
• Gujarat Technological University Bachelor of Engineering
  2011 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (13)

• Evaluation of Spectral and Spatial Reconstruction in Resolution-Enhanced Hyperspectral Data for Effective Mineral Mapping
  IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium 2025 DOI: 10.1109/igarss55030.2025.11242681
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• Fine-tuning Foundation Models for Earth Observation Using a Multi-objective Optimization Strategy
  Living Planet Symposium 2025 2025 DOI: 10.13140/RG.2.2.24284.30085
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• Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data
  Remote Sensing 2025 DOI: 10.3390/rs17111878
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• Deep learning with lidar for Earth observation
  Deep Learning for Multi-Sensor Earth Observation 2025 DOI: 10.1016/b978-0-44-326484-9.00015-4
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• Integrating Airborne and Satellite Hyperspectral Data for Enhanced Spectral Unmixing
  2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2024 DOI: 10.1109/whispers65427.2024.10876472
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• Multi-Objective Optimization Based Hyperspectral Feature Engineering for Spectral Abundance Mapping
  2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2024 DOI: 10.1109/whispers65427.2024.10876480
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• Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023 DOI: 10.1109/JSTARS.2022.3232583
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• Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping
  Geomatics 2022 DOI: 10.3390/geomatics2010007
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• Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 DOI: 10.1109/JSTARS.2022.3179027
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• A Disentangled Variational Autoencoder for Prediction of Above Ground Biomass from Hyperspectral Data
  2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021 DOI: 10.1109/igarss47720.2021.9554415
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