Abstract
In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.
Highlights
Slope movement is one of the most common geological and geomorphic natural hazards in China [1]
The data indicate that the proposed method for rock mass recognition and edge tracking can still maintain effectiveness under the challenging low intensity of illumination after sunrise and before sunset, which was turned out to work well in on-site tests, and the results reveal a relative robustness of the prominent structural plane (Pro-SP) method against the changing of lighting conditions
This paper presented some pioneering studies on augmented reality (AR) mapping of geospatial information beyond cyberspace in 2D screens to the physical world, which directly recognizes the slope rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes with mobile devices for the on-site locating of potentially unstable rock masses
Summary
Slope movement is one of the most common geological and geomorphic natural hazards in China [1]. Rockfall is an essential type of slope movement phenomenon that frequently occurs on steep rock cliffs and represents a relevant hazard in mountainous areas, significantly influencing the safety of residents and infrastructure. Defining, zoning, and locating the most probable future rockfall source areas are of primary importance for rock slope hazard investigation, assessment and management. According to the spatial scale of geomorphological observations and investigations, rockfall analysis mainly covers three different scales: the regional scale (areas of square kilometers), block specific scale (areas of square meters), and cliff scale (hundreds to thousands of square meters) [2], and of which this. NTahl eaprepfrooraec,htehs itsharet smeiagrhcthcopnrsoidbelermallisstepeaprtsilocupelas ralsyewquoirvtahleyntosfoaucracedearmeaisc; athtteepnrtoiobnle.m Rowckitfhalolnm-sietechdaenteicstmings, aztonthineg,calinffd sloccaalteinagreofstthreonmgolsyt lliikneklyedroctkofatlhl esosulrocpe earmeaos rapnhdoulnosgtyabalend rock discontirenoncuskuirtmeieasass[me2s]o,roewn ehthfifecechstliovapereeapbnadrdimlryealinraeiblelydesthhtaoezabjroediandatsdssreiesnsssvmeodelnv[t3e, ]d.guOiinnd-esoiutfeurrrtsohtceukrdfadylletasaorilueeardcse.fideTledhtewecotuiroksneacnoadnf remote sensingsutebcsheqnuieqnutessloopveetrreraetcmeenntt,daencdadheelspheasstadblrisahmaatsimcaalrltyroinckcrselaopseedenthviero3nDmednat.taThaecrqeufoirsei,titohnis quality of bothrtehseeasrclohpperoabnledmdisispcaortnictuinlaurliytywgoretohmy oeftraycadfoermricoactktefanltiloann. Based on the remote sensed high-resolutRiocnkf3aDll mpoecinhatncilsomusdast athnedcmlifef sshcamle oadreelsstr,omngolryelirneklieadbtloe ltahne dslsolpideemmorapphpolionggys,aenidthreorckin 2D or
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