MC-H-Geo: A Multi-Scale Contextual Hierarchical Framework for Fine-Grained Lithology Classification.

Lang Liu, Yanlin Shao, Yaxiong Shao, Peijin Li, Qingqing Yang, Rui Zeng

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Method for Extracting Sedimentary Outcrops from UAV Oblique Photogrammetry Point Clouds.Sensors (Basel, Switzerland) · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lang LiuSchool of Geosciences, Yangtze University, Wuhan 430100, China.ORCID 0009-0003-4793-6527

Yanlin ShaoSchool of Geosciences, Yangtze University, Wuhan 430100, China.ORCID 0009-0000-8337-7898

Yaxiong ShaoCenter for Governmental Studies, Northern Illinois University, DeKalb, IL 60115, USA.

Peijin LiSchool of Geosciences, Yangtze University, Wuhan 430100, China.

Qingqing YangSchool of Geosciences, Yangtze University, Wuhan 430100, China.

Rui ZengSchool of Geosciences, Yangtze University, Wuhan 430100, China.

Funding

College Students' Innovative Entrepreneurial Training Plan Program Yz2024019Hubei Provincial Department of Education, China 2023269 (2024)

6 · The paper itself

Abstract

High-resolution lithological mapping of outcrops is fundamental for reservoir characterization and petroleum geology, yet distinguishing lithologies with subtle petrophysical contrasts remains a major challenge. This study proposes MC-H-Geo, a multi-scale contextual hierarchical framework for automated lithology classification from terrestrial laser scanning (TLS) point clouds. The framework integrates three modules: (i) a multi-scale contextual feature engine that extracts spectral, geometric, and textural descriptors across local and stratigraphic contexts, enhanced by cross-scale differentials to capture stratigraphic variability; (ii) a gated expert classifier with task-adaptive feature subsets for hierarchical vegetation-rock and intra-rock discrimination; and (iii) a two-step geological post-processing procedure that enforces stratigraphic continuity through

Indexed as

geological mappinghierarchical modellithology classificationmulti-scale contextual learningTLS point clouds

Identifiers

PMID41305067

DOI10.3390/s25226859

PMCPMC12656162

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.