Kenichi Sasaki

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my resume

Research Interests

Remote sensing, Satellite imagery analysis, Machine learning, Earth science, Oceanography


2012-2016: B.S. Mechano-Aerospace Engineering, Tokyo Institute of Technology
2016-2019: M.S. Mechanical Engineering, Tokyo Institute of Technology
2019-Current: PhD. Aerospace Engineering and Sciences, University of Colorado Boulder


Marine debris monitoring using very high resolution satellite images

the combination of in-situ measurement from local cleanup with nearly coincident high-resolution satellite imagery (WorldView-2/3) enables to isolate debris from other materials using a segmentation-based approach. We applied Shannon’s entropy method, which represents the uncertainties of the segmentation results to obtain spectral signatures for individual semantic features.

Unsupervised Domain Adaptation with CycleGAN using High Resolution
Satellite Images

This research aims to extend a segmentation model for land use and land classification (LULC) purposes, trained in a single source (locations, sensors, etc) to different sources using CycleGAN framework.

Oil spill analysis using multi-temporal Synthetic Aperture Radar (SAR) image

An oil tanker ran aground near the coast of Mauritius island on July 25, 2020, and an estimated 1000 tons of fuel oil leaked into the lagoon. To analyze the resulting pollution, we obtained 6 synthetic aperture radar (SAR) satellite images spread over 30 days from the start of the oil spill. We implemented a classical analysis based on Haralick textures and a machine learning analysis based on a U-net semantic segmentation architecture.


We performed particle simulations around the southern islands of Japan to further understand the debris migration overseas. This simulation is based on the Lagrangian framework to look at debris transportation along the fluid motion. The result shows the major contribution of debris deposit comes from southern China.


Machine Learning, Optimization, Remote Sensing Data Analysis, Atmospheric Radiative Transfer Theory, Statistical Learning Theory, Applied Statistics, Fluid Mechanics


  • K. Sasaki, T. Sekine, and W. Emery, ” Particle Simulation of Floating Marine Debris with Statistical Debris Estimation in Coastal Areas of Japan “, American Geophysical Units AGU Fall Meeting 2022, Chicago, IL
  • K. Sasaki, T. Sekine, and W. Emery, “Large Scale Coastal Marine Debris Monitoring in the Coast of Japan islands using Satellite and Drone observations,” International Astronautical Congress 2022, Paris, France, 2022, IAC-22-B1.IP.x73154.
  • K. Sasaki, T. Sekine, L. -J. Burtz and W. J. Emery, “Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6391-6401, 2022, doi: 10.1109/JSTARS.2022.3193993.
  • K. Sasaki and T. Sekine, “Generalized segmentation model for imbalanced high-resolution satellite images using entropy loss “, 33th International Symposium on Space Technology and Science ISTS 2021, Beppu, Japan
  • K. Sasaki, T. Sekine, M. Yoshioka and W. Emery, “Coastal marine debris characterization using multi-temporal satellite and drone images”, American Geophysical Units AGU Fall Meeting 2021, New Orleans, LA
  • K. Sasaki, W. Emery, T. Sekine, L. -J. Burtz and Y. Kudo, “Coastal Marine Debris Density Mapping using a Segmentation Analysis of High-Resolution Satellite Imagery,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 7505-7508
  • K. Sasaki, T. Sekine, and L. -J. Burtz, “Coastal marine debris mapping using multi-modal feature extraction pipeline,” 35th Annual AIAA/USU Conference on Small Satellites, SSC21-S1-33, Aug. 2021.
  • K. Sasaki, L. -J. Burtz, T. Sekine, and M. Yoshioka, “Time Series Monitoring of the 2020 Mauritius Oil Spill with Synthetic Aperture Radar Satellite Imagery,” International Astronautical Congress 2021, Dubai, United Arab Emirates, 2021, IAC-21-B1.4.1.
  • T. Sekine, Y. Kudo and K. Sasaki, “Monitoring Coastal Marine Debris Using High-Resolution Satellite Image Time Series,” IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 2335-2338, doi: 10.1109/IGARSS46834.2022.9884402.