Uav Photogrammetry for Mapping and 3d Modeling – Current Status and Future Perspectives

Remondino, Fabio; Barazzetti, Luigi; Nex, Francesco; Scaioni, Marco; Sarazzi, D.

doi:10.5194/isprsarchives-xxxviii-1-c22-25-2011

Cited by 461 publications

(338 citation statements)

References 21 publications

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“…Para la conservación de estas áreas se buscan y se establecen estrategias de monitoreo, con la finalidad de evidenciar los cambios ocurridos a lo largo del tiempo -recuperación o degradación-y la toma de decisiones oportunas. Dentro de estas estrategias, los drones cumplen un rol de generación de ortofotos y cartografía con una alta resolución espacial y temporal en corto tiempo (Remondino, Barazzetti, Nex, Scaioni, & Sarazzi, 2011).…”

Section: Monitoreo Del Estado De Parques Y Jardines De La Ciudad De Cunclassified

“…Desde el año 1979 existe legislación específica en torno a la protección del patrimonio denominada "Ley de Patrimonio Cultural". A partir del Decreto de emergencia del Patrimonio Cultural del año 2008, la sociedad empezó a tener conciencia de la importancia de precautelar los bienes heredados de nuestros antepasados (Rodas, 2008).…”

Section: Uso En El Inventario De Edificaciones Patrimoniales E Histórunclassified

“…Existen investigaciones que buscan usar esta tecnología para evaluar el tráfico vehicular en base a los videos que se pueden obtener del dron (Salvo, Caruso, & Scordo, 2014).…”

Section: Introductionunclassified

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Drone in urban spaces: Study case in parks, gardens and built heritage of Cuenca

Prado

2017

est

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Resumen a conservación del patrimonio edificado y el mantenimiento de las áreas verdes son actividades de importancia en la planificación de un territorio urbano. Los métodos tradicionales de recolección de cartografía suelen demandar elevados costos y períodos de tiempo relativamente largos. Con el uso de vehículos aéreos no tripulados (VANT) se aspira obtener productos cartográficos y modelos 3D. El uso de técnicas fotogramétricas puede convertir las fotografías capturadas por el dron en información digital como ortofotos, modelos digitales de superficies o modelos 3D. La información de estos bienes urbanos, en series temporales, permitirá evaluar los daños y cambios de los mismos a lo largo del tiempo y, de esta manera, ejecutar acciones de protección y mitigación de sus impactos.Palabras clave: áreas verdes, dron, modelos 3D, ortofotos, patrimonio Abstract:The conservation of the built heritage and the maintenance of the green areas are important activities in the planning of an urban territory. Traditional methods of mapping often require high costs and relatively long periods of time. The use of unmanned aerial vehicles (UAVs) aims to obtain cartographic products and 3D models. The use of photogrammetric techniques can convert photographs captured by the dron into digital information such as orthophotos, digital surface models or 3D models. The information of these urban goods in time series will allow to evaluate the damages and changes of the same over time and thus to execute actions of protection and mitigation of their impacts in a timely manner.

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Section: Monitoreo Del Estado De Parques Y Jardines De La Ciudad De Cunclassified

Section: Uso En El Inventario De Edificaciones Patrimoniales E Histórunclassified

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Drone in urban spaces: Study case in parks, gardens and built heritage of Cuenca

Prado

2017

est

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“…A technique to extract 3D-information of UAV flights is given by the structure-from-motion (SFM) processing chain, which produces point clouds on the basis of feature matches within overlapping images. Several studies depicted a high potential of the combination of UAV image flights and the SFM processing chain, particularly with regard to urban objects and terrain modeling [Remondino et al, 2011]. By now fewer attempts have been conducted to extract vegetation structure or specifically information on forest stands.…”

Section: Dense 3d Reconstruction For Forest Stand Parametersmentioning

confidence: 99%

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Fritz

Kattenborn

Koch

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

138

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ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

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“…Quality of DEMs and DSMs is measured as height difference between points of generated model and control points measured using more precise method calculating RMSE (root mean square error) to express the amount of quality (Uddin, 2002), (Karel et al, 2006), (Saylam, 2009), (Wang et al, 2010), (Remondinoa et al, 2011). Currently, there is ASPRS standard "ASPRS Positional Accuracy Standards for Digital Geospatial Data" (www.asprs.org/Standards-Activities.html), which specifies horizontal and vertical accuracy for digital data and their verification (ASPRS, 2015a), however, it does not specify the best practices and methodologies needed to meet the accuracy thresholds stated by it and does not cover classification tasks and business uses (ASPRS, 2015b).…”

Section: Quality Meaning Among Different Expertsmentioning

confidence: 99%

Point Distribution as True Quality of LiDAR Point Cloud

Kodors¹

2017

BJMC

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Abstract.A parameter "point density" is often used to evaluate the quality of aerial laser scanning data. It is a parameter simple for understanding and human imagination. However, the true quality of LiDAR point cloud is based on point distribution. There are researches, which mention importance of point distribution and users' false perception, that higher point density is better quality of LiDAR point cloud. The goal of this study is to define the mathematical model how to measure quality of LiDAR point cloud. This article discusses the point distribution and LiDAR data quality defining the image resolution of point cloud. It can be interesting for experts in civil geospatial intelligence, LiDAR data processing and flight planning.

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Uav Photogrammetry for Mapping and 3d Modeling – Current Status and Future Perspectives

Cited by 461 publications

References 21 publications

Drone in urban spaces: Study case in parks, gardens and built heritage of Cuenca

Drone in urban spaces: Study case in parks, gardens and built heritage of Cuenca

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Point Distribution as True Quality of LiDAR Point Cloud

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