Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Sameen, MI"'
© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the select
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::7e5138d7ba69fc5fae56161631ab541c
https://hdl.handle.net/10453/139853
https://hdl.handle.net/10453/139853
© 2019 Elsevier B.V. This study developed a deep learning based technique for the assessment of landslide susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian optimisation in Southern Yangyang Province, South Korea. A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::b4ab096c5ea1fb6f62245621176ea101
https://hdl.handle.net/10453/139855
https://hdl.handle.net/10453/139855
© 2013 IEEE. This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::393769d69909ddddbabf2b15dc39eed1
https://hdl.handle.net/10453/146465
https://hdl.handle.net/10453/146465
© 2019 Elsevier Ltd This study evaluates the contribution of an unsupervised factor optimisation based on sparse autoencoders (SAEs) to spatial landslide modelling with regularised greedy forests (RGFs). A total of 952 landslides were identified by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::d008329b4beb61f5c87589a5683e5344
https://hdl.handle.net/10453/139935
https://hdl.handle.net/10453/139935
© 2018, International Association for Mathematical Geosciences. Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::04c7c5654c07865e2194404e004925ff
https://hdl.handle.net/10453/130837
https://hdl.handle.net/10453/130837
Autor:
Pradhan, B, Sameen, MI
Analysis of Highway Geometry and Safety Using LiDAR Biswajeet Pradhan, Maher Ibrahim Sameen. fusion and optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 118, 22–36. Idrees, M. O., & Pradhan, B. ( 2016). A decade...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::0f62d58d8f3b9537085564959049677d
https://hdl.handle.net/10453/146090
https://hdl.handle.net/10453/146090
© Springer Nature Singapore Pte Ltd. 2019. This study investigates the power of deep learning in predicting the severity of injuries when accidents occur due to traffic on Malaysian highways. Three network architectures based on a simple feedforward
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::47e44dbf0388f0bce365a6a3ffa91d45
https://hdl.handle.net/10453/130800
https://hdl.handle.net/10453/130800
© 2018, Saudi Society for Geosciences. This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::c77fe7080556696dd55854a04c4dde0c
https://hdl.handle.net/10453/130010
https://hdl.handle.net/10453/130010
© 2017, Springer-Verlag GmbH Germany. Landslide is a natural disaster that threatens human lives and properties worldwide. Numerous have been conducted on landslide susceptibility mapping (LSM), in which each has attempted to improve the accuracy of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::304447a27c82678d1537f425e673cc64
https://hdl.handle.net/10453/120866
https://hdl.handle.net/10453/120866