TY - JOUR

T1 - Crowd-sourcing of structure-from-motion data for terrain modelling in a real-world disaster scenario

T2 - a proof of concept

AU - Ratner, J

AU - Sury, J

AU - James, Michael Richard

AU - Mather, T

AU - Pyle, D

N1 - The final, definitive version of this article has been published in the Journal, Progress in Physical Geography, 43 (2), 2019, © SAGE Publications Ltd, 2019 by SAGE Publications Ltd at the Progress in Physical Geography page: https://journals.sagepub.com/home/PPG on SAGE Journals Online: http://journals.sagepub.com/

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for disaster risk reduction purposes. Three groups with variable SfM knowledge utilized 16 different camera models, including four camera phones, to collect 1001 total photos in one hour of data collection. Datasets collected by each group were processed using VisualSFM, and the point densities, accuracies and distributions of points in the resultant point clouds (DTM skeletons) were compared. Our results show that the point clouds are resilient to inconsistency in users’ SfM knowledge: crowd-sourced data collected by a moderately informed general public yields topography results comparable in data density and accuracy to those produced with data collected by highly-informed SfM users or experts using LiDAR. This means that in a real-world scenario involving participants with a diverse range of expertise, topography models could be produced from crowd-sourced data quite rapidly and to a very high standard. This could be beneficial to disaster risk reduction as a relatively quick, simple and low-cost method to attain rapidly updated knowledge of terrain attributes, useful for the prediction and mitigation of many natural hazards.

AB - Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for disaster risk reduction purposes. Three groups with variable SfM knowledge utilized 16 different camera models, including four camera phones, to collect 1001 total photos in one hour of data collection. Datasets collected by each group were processed using VisualSFM, and the point densities, accuracies and distributions of points in the resultant point clouds (DTM skeletons) were compared. Our results show that the point clouds are resilient to inconsistency in users’ SfM knowledge: crowd-sourced data collected by a moderately informed general public yields topography results comparable in data density and accuracy to those produced with data collected by highly-informed SfM users or experts using LiDAR. This means that in a real-world scenario involving participants with a diverse range of expertise, topography models could be produced from crowd-sourced data quite rapidly and to a very high standard. This could be beneficial to disaster risk reduction as a relatively quick, simple and low-cost method to attain rapidly updated knowledge of terrain attributes, useful for the prediction and mitigation of many natural hazards.

KW - Structure-from-motion

KW - digital terrain model

KW - point cloud

KW - crowd-sourcing

KW - camera phone

KW - disaster risk reduction

U2 - 10.1177/0309133318823622

DO - 10.1177/0309133318823622

M3 - Journal article

VL - 43

SP - 236

EP - 259

JO - Progress in Physical Geography

JF - Progress in Physical Geography

SN - 0309-1333

IS - 2

ER -