Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Meiliu Wu"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104034- (2024)
The fusion of remote sensing and artificial intelligence, particularly deep learning, offers substantial opportunities for developing innovative methods in rapid disaster mapping and damage assessment. However, current models for wildfire burnt area
Externí odkaz:
https://doaj.org/article/f1273f6160ef470797f86c29fa014b21
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 125, Iss , Pp 103591- (2023)
Traditional overhead imagery techniques for urban land use detection and mapping often lack the precision needed for accurate, fine-grained analysis, particularly in complex environments with multi-functional, multi-story buildings. To bridge the gap
Externí odkaz:
https://doaj.org/article/bf292d0a23d647298c223fecbcb111d7
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering w
Externí odkaz:
https://doaj.org/article/f60446f49ee54dd7958c0e75df0ec086
Autor:
Meiliu Wu, Qunying Huang
Publikováno v:
Annals of GIS, Vol 0, Iss 0, Pp 1-23 (2022)
Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social medi
Externí odkaz:
https://doaj.org/article/ddc5c7ae8ada4833a226477868aa29a3
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3263 (2023)
Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have pro
Externí odkaz:
https://doaj.org/article/2e6f83f429924345b5b106f5cef83f09
Publikováno v:
Remote Sensing; Volume 15; Issue 13; Pages: 3263
Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::e371d49dd79afd41038457a561eb5a62
Autor:
Meiliu Wu, Qunying Huang
Publikováno v:
Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::021bbbcaa9d26e2ecc4eed0440ea522a
https://doi.org/10.1145/3557918.3565868
https://doi.org/10.1145/3557918.3565868
Publikováno v:
PredictGIS@SIGSPATIAL
Social media data which capture long-term personal travel activities as a set of space-time points (time series) become widely used for human mobility study. The space-time points representing individual activities are massive and need aggregation up
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55ee68912e5e85eaf96edb83a0492ee6
https://doi.org/10.1145/3152341.3152348
https://doi.org/10.1145/3152341.3152348