3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis

Herfort, Benjamin; Höfle, Bernhard; Klonner, Carolin

doi:10.1016/j.isprsjprs.2018.01.009

Cited by 20 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task of determining the reference plane on the ground surface in 3D point cloud subsets is implemented using a web‐based tool for 3D geoinformation extraction from micro tasks developed by Herfort et al . (). The source code of our micro task project is openly available, together with a video of the online tool (Herfort et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…It was found in 3D point cloud analysis that, for some geoinformation extraction problems, manual editing via a volunteered geographic information (VGI) approach (Goodchild, ) can produce better results than automated methods (Herfort et al ., ). The framework of Herfort et al .…”

Section: Introductionmentioning

confidence: 97%

“…We hypothesize that aggregating multiple contributions for individual micro tasks could reduce subjective errors of the single expert analysts and increase overall measurement accuracy, as was found in Herfort et al . (). Hereinafter we will refer to the approach of using multiple expert measurements as ‘expert‐driven’.…”

Section: Introductionmentioning

confidence: 97%

“…The framework of Herfort et al . () provides a new method to capture VGI using 3D point clouds, which was shown to reduce uncertainties in data. Manual editing is performed in a web‐based tool that splits a geoprocessing task into multiple components called ‘micro tasks’, with multiple users solving individual micro tasks.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

Anders

Marx

Boike

et al. 2020

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

show abstract

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

Anders

Marx

Boike

et al. 2020

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

show abstract

“…One potential approach is to develop highly scalable measuring methods that could be integrated into a Volunteered Geographic Information (VGI) crowdsourcing project [28]. In this sense, collaborative mapping based on low-cost technology could set the foundations for a national database of gullies.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

Orti

Negussie

Corral-Pazos-de-Provens

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Namibia is a dry and low populated country highly dependent on agriculture, with many areas experiencing land degradation accelerated by climate change. One of the most obvious and damaging manifestations of these degradation processes are gullies, which lead to great economic losses while accelerating desertification. The development of standardized methods to detect and monitor the evolution of gully-affected areas is crucial to plan prevention and remediation strategies. With the aim of developing solutions applicable at a regional or even national scale, fully automated satellite-based remote sensing methods are explored in this research. For this purpose, three different algorithms are applied to a Digital Elevation Model (DEM) generated from the TanDEM-X satellite mission to extract gullies from their geomorphological characteristics: (i) Inverted Morphological Reconstruction (IMR), (ii) Smoothing Moving Polynomial Fitting (SMPF) and (iii) Multi Profile Curvature Analysis (MPCA). These algorithms are adapted or newly developed to identify gullies at the pixel level (12 m) in our study site in the Krumhuk Farm. The results of the three methods are benchmarked with ground truth; specific scenarios are observed to better understand the performance of each method. Results show that MPCA is the most reliable method to identify gullies, achieving an overall accuracy of approximately 0.80 with values of Cohen Kappa close to 0.35. The performance of these parameters improves when detecting large gullies (>30 m width and >3 m depth) achieving Total Accuracies (TA) near to 0.90, Cohen Kappa above 0.5, and User Accuracy (UA) and Producer Accuracy (PA) over 0.50 for the gully class. Small gullies (<12 m wide and <2 m deep) are usually neglected in the classification results due to spatial resolution constraints within the input DEM. In addition, IMR generates accurate results for UA in the gully class (0.94). The MPCA method developed here is a promising tool for the identification of large gullies considering extensive study areas. Nevertheless, further development is needed to improve the accuracy of the algorithms, as well as to derive geomorphological gully parameters (e.g., perimeter and volume) instead of pixel-level classification.

show abstract

Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia

Vallejo,

Castillo,

Zahs

et al. 2024

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

The development of standardised data acquisition strategies and analytical workflows is crucial to quantify gully changes. In this study, we explore synergies between unoccupied aircraft vehicles (UAV) and satellite remote sensing in order to classify gully morphodynamics. Using Time Series Forest (TSF) and the Sentinel‐1 radar backscatter coefficient (σo), gully scenarios can be classified into four categories: gully topographical change, no change outside gully, no change inside gully, and non‐topographical change. In addition, a Random Forest (RF) classification is performed employing individual features obtained from elevation models and temporally aggregated datasets. Training data are generated from multitemporal UAV‐borne photogrammetric point clouds through a manual segmentation of different gullies in Namibia. This information is transferred from point clouds (sub‐m) to satellite imagery (10 m) generating training data at Sentinel‐1 pixel level. Results indicate that the TSF (on the σo Vertical‐Vertical polarisation) and RF (on temporally aggregated features) perform best when training and testing areas are located within the same geographical extent. Both approaches yield similar Total Accuracy (TA ≈ 79%–80%) and Cohen's Kappa value (Kappa ≈ 0.7), but TSF achieves superior Producer Accuracy (PA = 78.5%) and User Accuracy (UA = 84.6%) for the gully topographical change class. Additionally, the utilisation of TSF in Vertical‐Vertical polarisation is the most effective method if the testing and training areas are in different geographical locations, allowing gully identification with TA > 80% and Kappa = 0.49. However, this method presents limitations to precisely delineate the change types, as dynamics are rain‐driven and therefore are geographically related. In summary, by combining the complementary benefits of UAV‐based and satellite‐based solutions, this study opens a line of research for the study and classification of surface land dynamics and geomorphological feature extraction in regional extents.

show abstract

3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis

Cited by 20 publications

References 27 publications

Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia

Contact Info

Product

Resources

About