Drew Bollinger - IEEE Xplore Author Profile

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Author details

Drew Bollinger

Publications

4

Citations

58

Publications by Year

20192021

Co-Authors:

Daniel J. CecilMichael FalkowskiBrian FreitagIksha GurungChris Hain

Show All Co-Authors (20)

Drew Bollinger

Affiliation

Development Seed

Publication Topics

Amazon Web Services,
Convolutional Neural Network,
Hurricane,
Machine Learning,
Application Programming Interface,
Bounding Box,
Class Activation Maps,
Convolutional Layers,
Convolutional Neural Network Model,
Deep Learning,
Domain Experts,
Earth Science

Biography

Drew Bollinger leads the Labs Team, Development Seed, Washington, DC, USA. He is a Data Analyst, Software Developer, and Machine Learning Engineer, with experience building geo-interfaces and running advanced statistical and spatial analysis on open datasets.,Mr. Bollinger has delivered several impactful workshops at SATSUMMIT, Geoscience and Remote Sensing Society, and most recently at the Africa Geospatial Data and Internet Conference, in Accra, Ghana.(Based on document published on 27 July 2020).

Publications

4

Citations

58

Publications by Year

20192021

Co-Authors:

Daniel J. Cecil
Michael Falkowski
Brian Freitag
Iksha Gurung
Chris Hain

Show All Co-Authors (20)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (3)

Journals (1)

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Single Year
Drew Bollinger(4)
Manil Maskey(4)
Muthukumaran Ramasubramanian(4)
Iksha Gurung(4)
Rahul Ramachandran(3)
NASA Marshall Space Flight Center(3)
Development Seed(3)
University of Alabama, Huntsville(2)
Remote Sensing, NASA Marshall Space Flight Center, Huntsville, AL, USA(1)
Development Seed, Washington, DC, USA(1)
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS(1)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(1)
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium(1)
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium(1)
IEEE(4)
Brussels, Belgium(1)
Waikoloa, HI, USA(1)
Yokohama, Japan(1)
Amazon Web Services(4)
Convolutional Neural Network(3)
Hurricane(3)
Machine Learning(3)
Application Programming Interface(2)

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Visualizing, Exploring, and Communicating Environmental Effects of COVID-19 Using Earth Observation Dashboard

Manil Maskey;Michael Falkowski;Kevin Murphy;Olaf Veerman;Ricardo Mestre;Iksha Gurung;Muthukumaran Ramasubramanian;Lillianne Thomas;Zhuang-Fang Yi;Drew Bollinger;Abigail Seadler;Yvonne Ivey

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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The COVID-19 pandemic caused authorities to limit or lockdown cities resulting in changes in human behaviors that impacted the Earth system. Studying such impacts on the Earth system requires an integrated study of relevant parameters using remotely sensed data. This paper discusses a unique dashboard that brings Earth observation datasets together to visualize, explore, and communicate the enviro...Show More

Visualizing, Exploring, and Communicating Environmental Effects of COVID-19 Using Earth Observation Dashboard

Manil Maskey;Michael Falkowski;Kevin Murphy;Olaf Veerman;Ricardo Mestre;Iksha Gurung;Muthukumaran Ramasubramanian;Lillianne Thomas;Zhuang-Fang Yi;Drew Bollinger;Abigail Seadler;Yvonne Ivey

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

Year: 2021 | Conference Paper |

EMPLOYING DEEP LEARNING TO ENABLE VISUAL EXPLORATION OF EARTH SCIENCE EVENTS

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Brian Freitag;Aaron Kaulfus;Georgios Priftis;Drew Bollinger;Ricardo Mestre;Daniel da Silva

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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Earth science data archives have significantly increased in size due to the number of advanced sensors and science missions. In the meantime, Earth science data systems have not taken advantage of data driven technologies to provide advanced search capabilities. This paper discusses a machine learning-based approach, an enabling data driven technology, to detect Earth science events from image arc...Show More

EMPLOYING DEEP LEARNING TO ENABLE VISUAL EXPLORATION OF EARTH SCIENCE EVENTS

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Muthukumaran Ramasubramanian;Brian Freitag;Aaron Kaulfus;Georgios Priftis;Drew Bollinger;Ricardo Mestre;Daniel da Silva

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

Year: 2020 | Conference Paper |

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Manil Maskey;Rahul Ramachandran;Muthukumaran Ramasubramanian;Iksha Gurung;Brian Freitag;Aaron Kaulfus;Drew Bollinger;Daniel J. Cecil;Jeffrey Miller

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2020 | Volume: 13 | Journal Article |

Cited by: Papers (49)

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Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep...Show More

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Manil Maskey;Rahul Ramachandran;Muthukumaran Ramasubramanian;Iksha Gurung;Brian Freitag;Aaron Kaulfus;Drew Bollinger;Daniel J. Cecil;Jeffrey Miller

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2020 | Volume: 13 | Journal Article |

Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Cited by: Papers (6)

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Enterprises are making machine learning for production as an integral part of their future roadmaps and Earth science domain is no exception. However, there is common problem in transitioning machine learning from science to production due to a major difference in constructing a model versus deploying it for people to use to make decisions. Phases of machine learning lifecycle that includes model ...Show More

Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

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