Ground penetrating radar applied to subsurface culverts

Autor: Tess Luo, Song Zhu, Yiliminuer Yikeremu, Jiasong Zhu, John van Genderen

Publikováno v: Geo-spatial Information Science, Vol 27, Iss 6, Pp 2092-2108 (2024)

The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a wid

Externí odkaz: https://doaj.org/article/7bfdac5b0d9a43bb83281ebac437d0e7

An efficient built-up land expansion model using a modified U-Net

Autor: Hanieh Shojaei, Saeed Nadi, Hossein Shafizadeh-Moghadam, Amin Tayyebi, John Van Genderen

Publikováno v: International Journal of Digital Earth, Vol 15, Iss 1, Pp 148-163 (2022)

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urb

Externí odkaz: https://doaj.org/article/c5dd9acc56b246bd8cfbeab9b4268694

The Digital Belt and Road program in support of regional sustainability

Autor: Huadong Guo, Jie Liu, Yubao Qiu, Massimo Menenti, Fang Chen, Paul F. Uhlir, Li Zhang, John van Genderen, Dong Liang, Ishwaran Natarajan, Lanwei Zhu, Jiuliang Liu

Publikováno v: International Journal of Digital Earth, Vol 11, Iss 7, Pp 657-669 (2018)

The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, in

Externí odkaz: https://doaj.org/article/64ca667fdb4649d8a67859eb644e8b17

Artificial intelligence for sustainable development of smart cities and urban land-use management

Autor: Zohreh Masoumi, John van Genderen

Publikováno v: Geo-spatial Information Science. :1-25

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b73e8b7915ba41e65c7f3f262f48f88e
https://doi.org/10.1080/10095020.2023.2184729

Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data

Autor: Seyed-Mohammad Tavakkoli-Sabour, Saeid Zare Naghadehi, John van Genderen, Samira-Sadat Saleh, Milad Asadi, Mohammad Maleki

Publikováno v: ISPRS International Journal of Geo-Information
Volume 10
Issue 8
ISPRS International Journal of Geo-Information, Vol 10, Iss 513, p 513 (2021)

A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f3d6ac15c75dc81fb766a48239feafa