Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Kyle Bradbury"'
Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-9 (2024)
Abstract Remotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms
Externí odkaz:
https://doaj.org/article/70e4905c123140c2b37d58c476776197
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1988-1998 (2024)
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes due to geography, sensor, or atmospheric conditions) remain a challenge,
Externí odkaz:
https://doaj.org/article/77a3f2dad85a4416a0400034f659dd23
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4956-4970 (2022)
Energy system information for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers—termed the power grid—is often incomplete, outdated, or altogether unavailable. Furthermore, conv
Externí odkaz:
https://doaj.org/article/8bf2db15eec34b40aca158e78e75ba22
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4386-4396 (2022)
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e.g., satellite) imagery. One ongoing challenge however is the acquisition of training data, due to high costs of obtaining satellite imagery and
Externí odkaz:
https://doaj.org/article/40719d4a8f194e419867dff796f9a4df
Autor:
Caleb Kornfein, Frank Willard, Caroline Tang, Yuxi Long, Saksham Jain, Jordan Malof, Simiao Ren, Kyle Bradbury
Publikováno v:
Environmental Data Science, Vol 2 (2023)
Accurate geospatial information about the causes and consequences of climate change, including energy systems infrastructure, is critical to planning climate change mitigation and adaptation strategies. When up-to-date spatial data on infrastructure
Externí odkaz:
https://doaj.org/article/9e7477948c3e4b15bc2e9486bfcfcf01
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5500 (2022)
Transfer learning has been shown to be an effective method for achieving high-performance models when applying deep learning to remote sensing data. Recent research has demonstrated that representations learned through self-supervision transfer bette
Externí odkaz:
https://doaj.org/article/5916a8fc56fc44c891571b4d7925fc72
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 4, p 222 (2022)
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further i
Externí odkaz:
https://doaj.org/article/52c8055b911742a599d4620e1f2f09f7
Autor:
Simiao Ren, Wayne Hu, Kyle Bradbury, Dylan Harrison-Atlas, Laura Malaguzzi Valeri, Brian Murray, Jordan M. Malof
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67f875a0e63b7706784d97c6cdb50440
Autor:
Wei Hu, Kyle Bradbury, Jordan M. Malof, Boning Li, Bohao Huang, Artem Streltsov, K. Sydny Fujita, Ben Hoen
Publikováno v:
Applied Energy. 327:120143
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7b154684df0f6d31eceb2385dced511
https://doi.org/10.1016/j.apenergy.2022.120143
https://doi.org/10.1016/j.apenergy.2022.120143
Autor:
Jordan M. Malof, Natalie Tarn, Tyler Feldman, Baoyan Ye, Wei Hu, Yang Xu, Kyle Bradbury, Yanchen J. Ou
Publikováno v:
IGARSS
Automatic object detection in overhead imagery is greatly increasing the pace at which we learn about anthropic activity across diverse fields such as economics, environmental management, and engineering. Properly-trained object detection models save
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f600fd58282bdd7d204a2053378b519b
https://doi.org/10.1109/igarss47720.2021.9554306
https://doi.org/10.1109/igarss47720.2021.9554306