Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Abu Bakar Siddik Nayem"'
Autor:
A. K. M. Mahbubur Rahman, Moinul Zaber, Qianwei Cheng, Abu Bakar Siddik Nayem, Anis Sarker, Ovi Paul, Ryosuke Shibasaki
Publikováno v:
Sensors, Vol 21, Iss 22, p 7469 (2021)
This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimens
Externí odkaz:
https://doaj.org/article/f4c4f4ee1818416abeee7c944fb0485f
Autor:
Fahim Faisal Niloy, Ovi Paul, Arif, Moinul Zaber, M. Ashraful Amin, Amin Ahsan Ali, Anis Sarker, Abu Bakar Siddik Nayem, Akm Mahbubur Rahman
The advancement of deep learning technology has enabled us to develop systems that outperform any other classification technique. However, success of any empirical system depends on the quality and diversity of the data available to train the propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f120029a61746f0fda7e1459556038fd
http://arxiv.org/abs/2107.01284
http://arxiv.org/abs/2107.01284
Autor:
Anis Sarker, Qianwei Cheng, Akm Mahbubur Rahman, Moinul Zaber, Ryosuke Shibasaki, Ovi Paul, Amin Ahsan Ali, Abu Bakar Siddik Nayem, M. Ashraful Amin
Rapid globalization and the interdependence of humanity that engender tremendous in-flow of human migration towards the urban spaces. With advent of high definition satellite images, high resolution data, computational methods such as deep neural net
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a485801bd06676aa38cef5bee9e9b9a
Autor:
Ryosuke Shibasaki, Anis Sarker, Abu Bakar Siddik Nayem, Moinul Zaber, A K M Mahbubur Rahman, Ovi Paul, Qianwei Cheng
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 22
Sensors, Vol 21, Iss 7469, p 7469 (2021)
Sensors
Volume 21
Issue 22
Sensors, Vol 21, Iss 7469, p 7469 (2021)
This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::678eaa2d793f0cb9aba994cd8d2b8321
https://doi.org/10.3390/s21227469
https://doi.org/10.3390/s21227469