TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering

Pijl, Anton; Bailly, Jean‐Stéphane; Feurer, Denis; Maaoui, Mohamed Amine El; Boussema, Mohamed Rached; Tarolli, Paolo

doi:10.1016/j.jag.2019.101977

Cited by 24 publications

(15 citation statements)

References 37 publications

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“…The VHR DSM served to create an nDSM based on an algorithm by Pijl et al (2020). The mean of the VHR nDSM pixel values was used to retrieve the height per building.…”

Section: Validationmentioning

confidence: 99%

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller

Wurm

Stark

et al. 2021

JTLU

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A significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution.

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“…The VHR DSM served to create an nDSM based on an algorithm by Pijl et al (2020). The mean of the VHR nDSM pixel values was used to retrieve the height per building.…”

Section: Validationmentioning

confidence: 99%

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller

Wurm

Stark

et al. 2021

JTLU

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“…In terraced landscapes, a mass movement is commonly manifested as a wall collapse or shallow landslides (Pijl, Bailly, et al, 2020; Preti et al, 2018; Tarolli et al, 2014). Terracing is a common practice for steep‐slope agriculture (Csorba, 2010; Tarolli et al 2015; Varotto, Bonardi, & Tarolli, 2019), modifying the original terrain profile and aiming to reduce erosion rates by controlling surface runoff (García‐Ruiz, 2010).…”

Section: Review Of Slope Instability Studies In Steep Cultivated Areasmentioning

confidence: 99%

Slope instabilities in steep cultivation systems: Process classification and opportunities from remote sensing

et al. 2020

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The cultivation of steep slopes is a widespread practice in hilly and mountainous areas around the world. Such environments often result in particular agricultural systems linked with unique local values of historical and cultural heritage, economy, and food security. However, steep‐slope agriculture is inherently exposed to slope instability processes, which are now widely aggravated due to natural (e.g., growing rainfall aggressiveness) and anthropogenic factors (e.g., unsuitable maintenance or agronomic practices). In the literature, among the many articles published on soil erosion, a specific focus on the analysis of slope instabilities in steep cultivation systems is lacking. The purpose of this article is, therefore, to create a specific overview of this problem, with some useful insights into the role of remote sensing. We introduce the problem first, highlighting the main issues related to slope instabilities in steep cultivated areas. Then we provided a classification of key published papers, based on the different types of mass movements studied and their location in the world. The spatial comparison of past research and estimated global hazard of the mass movement in agricultural areas highlighted a relevant bias: a strong scientific focus on southern European countries, yet large rural areas are at risk on all continents, many of these unexplored by science. The third section is related to the contribution of remote sensing technologies (e.g., LiDAR and RPAS photogrammetry) in mapping the investigated processes. This study could help to guide future research for better management of such socio‐economically relevant agricultural landscapes.

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“…Nowadays, SfM photogrammetry paired with multi-view stereo (MVS), hereafter together referred to as SfM, represents a powerful and successful tool to produce high-quality three-dimensional (3D) surfaces for geoscience applications. In literature, several researches have used this technology to carry-out different kinds of analysis and studies on: structural geology (e.g., Bemis et al, 2014); debris-flow dynamics (Cucchiaro et al, 2019); surveying submerged surfaces (e.g., Woodget et al, 2015;Dietrich 2017); soil erosion (Glendell et al, 2017); design of drainage network (Pijl et al, 2019) or agricultural terraces 3D reconstruction (Pijl et al, 2020); gullies and badlands (e.g., Stöcker et al, 2015;Smith and Vericat 2015;Koci et al, 2017); fluvial morphology (e.g., Javernick et al, 2014;Marteau et al, 2017) and aquatic applications (Carrivick and Smith 2018); glaciers (e.g., Immerzeel et al, 2014;Piermattei et al, 2015;Mallalieu et al, 2017); monitoring on landslide displacement (e.g., Stumpf et al, 2015;Clapuyt et al, 2017;Eker et al, 2018;Turner et al, 2015); coastal recession (e.g., James and Robson 2012;Westoby et al, 2012); open-pit mining areas (Chen et al, 2015;Xiang et al, 2018); extraction of biophysical forest or plants parameters and monitoring (e.g., Iglhaut et al, 2019;Malambo et al, 2018;Zarco-Tejada et al, 2014). Moreover, studies are shifting from proofof-concepts in topographic survey to genuine applications including quantification of bathymetric surveys, underwater archaeology, grain-size mapping, restoration monitoring, habitat classification, geomorphological change detection and sediment transport path delineation (Carrivick and Smith 2018).…”

Section: Sfm Photogrammetrymentioning

confidence: 99%

SfM photogrammetry for GeoArchaeology

Cucchiaro

Fallu

Zhao

et al. 2020

Developments in Earth Surface Processes

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Geoarchaeological studies have benefits from new technological developments in remote sensing technologies that have become an integral and important part of the archaeological researches. In particular, Structure from Motion (SfM) photogrammetry is one of the most successful emerging techniques in high-resolution topography (HRT) and provides exceptionally fast, low-cost and easy 3D survey for geoscience applications. In this chapter we present an example of SfM application for geoarchaeology. The purpose is to realize HRT DTMs (Digital Terrain Models) of an area of prehistoric agricultural terracing together with a geoarchaeological excavation trench in the Ingram Valley, Northumberland National Park, NE England. The study area is one of the six pilot case studies

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TERRA: Terrain Extraction from elevation Rasters through Repetitive Anisotropic filtering

Cited by 24 publications

References 37 publications

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Slope instabilities in steep cultivation systems: Process classification and opportunities from remote sensing

SfM photogrammetry for GeoArchaeology

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