Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Mohammad Aghdami-Nia"'
Deep-Learning-Based Edge Detection for Improving Building Footprint Extraction from Satellite Images
Publikováno v:
Environmental Sciences Proceedings, Vol 29, Iss 1, p 61 (2023)
Buildings are objects of great importance that need to be observed continuously. Satellite and aerial images provide valuable resources nowadays for building footprint extraction. Since these images cover large areas, manually detecting buildings wil
Externí odkaz:
https://doaj.org/article/4c366bec74544ad0a125b8c21db6ea83
Autor:
Mohammad Aghdami-Nia, Reza Shah-Hosseini, Saeid Homayouni, Amirhossein Rostami, Nima Ahmadian
Publikováno v:
Environmental Sciences Proceedings, Vol 29, Iss 1, p 16 (2023)
Radiative Transfer Models (RTMs) are one of the major building blocks of remote-sensing data analysis that are widely used for various tasks such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs such as MODTRAN are
Externí odkaz:
https://doaj.org/article/5fa30ad9aab34531af942d848b87180c
Autor:
Amirhossein Rostami, Reza Shah-Hosseini, Shabnam Asgari, Arastou Zarei, Mohammad Aghdami-Nia, Saeid Homayouni
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 992 (2022)
Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely
Externí odkaz:
https://doaj.org/article/30e3b98388924e3abfcde386d14f3507
Publikováno v:
IECG 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba89f94bf6bbe9eb807695914723f64b
https://doi.org/10.3390/iecg2022-14069
https://doi.org/10.3390/iecg2022-14069
Publikováno v:
International Journal of Applied Earth Observation and Geoinformation. 109:102785
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa823c7ce48620dc9f0d32273d04dc78
https://doi.org/10.1016/j.jag.2022.102785
https://doi.org/10.1016/j.jag.2022.102785