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Record W4411967716 · doi:10.1080/10106049.2025.2519915

Recent developments in unmanned aerial vehicle (UAV) surveys for rock slope stability analysis—a review

2025· article· en· W4411967716 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeocarto International · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsGeomechanica (Canada)
FundersPrince Sultan UniversityHigher Education Commision, Pakistan
KeywordsStability (learning theory)Aerial photosAerial imageryGeographyRemote sensingGeologyCartographyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

The existence of most of the cut and natural slopes in morphological complex areas makes it impossible to characterize the discontinuities along the rock slope using conventional scanline mapping and geological compass. Consequently, the unmanned aerial vehicle (UAV) survey has gotten greater attention from numerous researchers in recent decades. This research presents a detailed review of the UAV surveys and their applicability of precise determination of discontinuity orientations and spacings along the rock slope. As the discontinuities characterization is carried out in rock slope only, therefore the scope of review article is limited to rock masses. The review article is composed of three sections. The first section compares the applicability of various UAV sensors followed by detail explanation of fundamental principles of UAV survey, such as data acquisition, data processing, and extraction. The next section provides a brief introduction and comparison of various types of UAVs platforms. It is followed by review of the applications of UAV photogrammetry for slope stability assessment, such as kinematic analysis, rock mass quality characterization, and slope deformation monitoring based on previous work. The primary aim of this review article is that no review article was published previously that assess the applicability of UAV photogrammetry in characterization of the entire slope on limited number of Rock Quality Designation (RQD) data. In this paper, a real case study is presented to estimate the area of various RQD indices along the slope. Based on the RQD maps, the area of poor, fair, good, and excellent rock were computed as 1382, 3039, 393, and 37 m2. This shows that the UAV can obtain precise discontinuity spacing and is a reliable way of characterizing the rock mass quality. The previous study reveals that UAV photogrammetry is currently an extensively applied approach for performing kinematic analysis, and slope deformation study. However, its application in rock mass quality characterization is not yet fully adopted. Furthermore, the capability of UAV to be equipped with various geophysical equipment, such as gravity, ground penetrating radar (GPR), and electromagnetic instrument forecast it increasing applications in future for geotechnical investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.279
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it