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Your search keyword '"Structure-from-Motion (SfM)"' showing total 3 results
Start Over Descriptor "Structure-from-Motion (SfM)" Publisher elsevier
3 results on '"Structure-from-Motion (SfM)"'

1. Fuji-SfM dataset: a collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry

Author

Josep Ramon Morros, Eduard Gregorio, Joan R. Rosell-Polo, Verónica Vilaplana, Javier Ruiz-Hidalgo, Ricardo Sanz-Cortiella, Jordi Gené-Mola, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo

Subjects
Teledetecció, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Point cloud, lcsh:Computer applications to medicine. Medical informatics, Yield mapping, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Agricultural and Biological Science, Fotogrametria, Structure from motion, Fruit detection, Computer vision, Segmentation, Terrestrial remote sensing, lcsh:Science (General), 030304 developmental biology, Structure-from-motion, 0303 health sciences, Ground truth, Multidisciplinary, business.industry, Remote sensing, Mask R-CNN, Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo [Àrees temàtiques de la UPC], Structure-from-motion (SfM), Yield prediction, Photogrammetry, Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció [Àrees temàtiques de la UPC], lcsh:R858-859.7, RGB color model, Artificial intelligence, business, 030217 neurology & neurosurgery, lcsh:Q1-390
Abstract

The present dataset contains colour images acquired in a commercial Fuji apple orchard (Malus domestica Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled “Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry” [1]. The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. With that, this is the first dataset for fruit detection containing images acquired in a motion sequence to build the 3D model of the scanned trees with SfM and including the corresponding 2D and 3D apple location annotations. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data. This work was partly funded by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). Part of the work was also developed within the framework of the project TEC2016-75976-R, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) and Vicens Maquinària Agrícola S.A. for their support during data acquisition, and Ernesto Membrillo and Roberto Maturino for their support in dataset labelling.

Published
2020

2. Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Author

Schmidt, J., Fassnacht, F.E., Neff, C., Lausch, Angela, Kleinschmit, B., Förster, M., Schmidtlein, S., Schmidt, J., Fassnacht, F.E., Neff, C., Lausch, Angela, Kleinschmit, B., Förster, M., and Schmidtlein, S.

Abstract

Remote sensing can be a valuable tool for supporting nature conservation monitoring systems. However, for many areas of conservation interest, there is still a considerable gap between field-based operational monitoring guidelines and the current remote sensing-based approaches. This hampers application in practice of the latter. Here, we propose a remote sensing approach for mapping the conservation status of Calluna-dominated Natura 2000 dwarf shrub habitats that is closely related to field mapping schemes. We transferred the evaluation criteria of the field guidelines to three related variables that can be captured by remote sensing: (1) coverage of the key species, (2) stand structural diversity, and (3) co-occurring species. Continuous information on these variables was obtained by regressing ground reference data from field surveys and UAV flights against airborne hyperspectral imagery. Merging the three resulting quality layers in an RGB representation allowed for illustrating the habitat quality in a continuous way. User-defined thresholds can be applied to this stack of quality layers to derive an overall assessment of habitat quality in terms of nature conservation, i.e. the conservation status.In our study, we found good accordance of the remotely sensed data with field-based information for the three variables key species, stand structural diversity and co-occurring vegetation (R2 of 0.79, 0.69, and 0.71, respectively) and it was possible to derive meaningful habitat quality maps. The conservation status could be derived with an accuracy of 65%. In interpreting these results it should be considered that the remote sensing based layers are independent estimates of habitat quality in their own right and not a mere replacement of the criteria used in the field guidelines. The approach is thought to be transferable to similar regions with minor adaptions.Our results refer to Calluna heathland which we consider a comparably easy target for remote sensing. Hen

Published
2017

3. Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field.

Author

Meinen BU and Robinson DT

Abstract

Distributed measurements of agricultural erosion at the farm-scale are needed to evaluate both the on and off-site impacts of sediment redistribution. While best management practices have been shown to reduce surface erosion rates and improve water quality, their farm-scale effects can be difficult to quantify. In this study we use imagery from an unmanned aerial vehicle (UAV) and structure-from-motion multi-view stereo (SfM-MVS) to quantify erosion rates and their effects on crop yield across a 15.9-ha agricultural field. Our results highlight that the installation of catch basins were able to stop 159.52 t of sediment and associated nutrients from entering the waterway adjacent to the study site over the course of one year, corresponding to an erosion rate of 18.83 t ha -1  yr -1 across six study basins. Poor soil structure resulting from downslope tillage reduced crop yields on topographic shoulders of the study site, while accelerated water erosion processes were responsible for large areas of washout that caused highly variable crop growth in footslopes. The highest crop yields were associated with backslopes and topographically flat regions of the field which experienced minimal erosion. Change-detection results showed that UAV imagery was able to reliably quantify depositional plumes and was comparable to that of a terrestrial laser scanner (TLS) using a ± 0.04 m confidence interval., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2020
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