J
João P. Leitão
Researcher at Swiss Federal Institute of Aquatic Science and Technology
Publications - 76
Citations - 1304
João P. Leitão is an academic researcher from Swiss Federal Institute of Aquatic Science and Technology. The author has contributed to research in topics: Flood myth & Environmental science. The author has an hindex of 18, co-authored 63 publications receiving 792 citations. Previous affiliations of João P. Leitão include Imperial College London & Technical University of Lisbon.
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Journal ArticleDOI
Statistical failure models for water distribution pipes – A review from a unified perspective
TL;DR: This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective and presents a new conceptual failure rate to which the failure rate of each model can be compared.
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Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas
TL;DR: In this paper, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions.
Journal ArticleDOI
Sewer asset management – state of the art and research needs
Franz Tscheikner-Gratl,Nicolas Caradot,Frédéric Cherqui,João P. Leitão,Mehdi Ahmadi,Jeroen Langeveld,Yves Le Gat,Lisa Scholten,Bardia Roghani,Juan Pablo Rodríguez,Mathieu Lepot,Bram Stegeman,Anna Heinrichsen,Ingo Kropp,Karsten Kerres,Maria do Céu Almeida,Peter M. Bach,Matthew Moy de Vitry,Alfeu Sá Marques,Nuno Simoes,Pascale Rouault,Nathalie Hernández,Andrés Torres,Caty Werey,Bénédicte Rulleau,Francois Clemens +25 more
TL;DR: Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments as discussed by the authors. But it is still a relatively new area.
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Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
João P. Leitão,Salvador Peña-Haro,Beat Lüthi,Andreas Scheidegger,Matthew Moy de Vitry,Matthew Moy de Vitry +5 more
TL;DR: In this article, the authors investigated the potential of using surveillance camera footage to measure surface flow velocity thanks to an LSPIV-based method called Surface Structure Image Velocimetry (SSIV) seven realscale experiments conducted in a specialized flood training facility were used to test the SSIV method under varied and challenging conditions.
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Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network
TL;DR: This work proposes a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale and uses a deep convolutional neural network to detect floodwater in surveillance footage and a novel qualitative flood index as a proxy for water level fluctuations visible from a surveillance camera's viewpoint.