Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Melanie Laverdiere"'
Autor:
Hsiuhan Lexie Yang, Melanie Laverdiere, Taylor Hauser, Benjamin Swan, Erik Schmidt, Jessica Moehl, Andrew Reith, Daniel Adams, Bennett Morris, Jacob McKee, Matthew Whitehead, Mark Tuttle
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure
Externí odkaz:
https://doaj.org/article/3c1f7eaa515e4ffdb7e604ae7320d78a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9122-9138 (2024)
The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained mod
Externí odkaz:
https://doaj.org/article/7b455a5fcfbd4d12b4bfc4041ef0462c
Publikováno v:
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 1-16 (2022)
Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires trainin
Externí odkaz:
https://doaj.org/article/fff061cfa5504431b813fc486b0477ba
Autor:
Andrew Reith, Jacob McKee, Amy Rose, Melanie Laverdiere, Benjamin Swan, David Hughes, Sophie Voisin, Lexie Yang, Laurie Varma, Liz Neunsinger, Dalton Lunga
Publikováno v:
Advances in Scalable and Intelligent Geospatial Analytics ISBN: 9781003270928
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c833cf323dc318634ce5851ea4313482
https://doi.org/10.1201/9781003270928-11
https://doi.org/10.1201/9781003270928-11
Publikováno v:
GIScience & Remote Sensing. 59:1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d1d2d1fa51a6be08f90e18c06f082d9
https://doi.org/10.1080/15481603.2021.2006433
https://doi.org/10.1080/15481603.2021.2006433
Autor:
Jacob J. McKee, Melanie Laverdiere
Publikováno v:
IGARSS
The availability of open source, remote sensing-derived vector data has increased exponentially in recent years. Unfortunately, these vector data are rarely made available with the corresponding source images from which objects were extracted. As suc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cf616ce610ce92a8507333d6aeeaff1
https://doi.org/10.1109/igarss47720.2021.9554510
https://doi.org/10.1109/igarss47720.2021.9554510
Publikováno v:
IGARSS
Disaster response requires timely damage assessment to prioritize rescue and restoration resources. However, providing critical and actionable knowledge after a natural disaster can be challenging due to the scale and the type of damages. This paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28cc8060e9a51e03fada259416896355
https://doi.org/10.1109/igarss39084.2020.9324160
https://doi.org/10.1109/igarss39084.2020.9324160
Publikováno v:
GeoAI@SIGSPATIAL
There is a general consensus in the neural network community that noise in training data has a negative impact on model output; however, efforts to quantify the impact of varying levels have been limited, particularly for semantic segmentation tasks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d08a487766f3053547c14ca56dfa012
https://doi.org/10.1145/3281548.3281557
https://doi.org/10.1145/3281548.3281557
Publikováno v:
ISEE Conference Abstracts. 2018
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c96fd0110c4dd8647fb996f857ccee9
https://doi.org/10.1289/isesisee.2018.s03.03.01
https://doi.org/10.1289/isesisee.2018.s03.03.01
Autor:
Dalton Lunga, Amy Rose, Jiangye Yuan, Melanie Laverdiere, Budhendra L. Bhaduri, Hsiuhan Lexie Yang
Establishing up-to-date large scale building maps is essential to understand urban dynamics, such as estimating population, urban planning and many other applications. Although many computer vision tasks has been successfully carried out with deep co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19e1af62ca8d82012eb6db3a5e90cf1d