Data · dataset · 2026
The Global Very-High-Resolution Remote Sensing Landslide Mapping (GVLM) dataset
Listed in ScienceDB
GVLM is a large-scale benchmark dataset designed for the intelligent interpretation of landslide disasters in remote sensing.
Description
It covers 24 representative landslide events across 17 countries on five continents, encompassing diverse triggering mechanisms, landslide morphologies, and land-cover types. The dataset includes multi-temporal high-resolution optical imagery, multispectral data, and SAR imagery, along with fine-grained pixel-level annotations, supporting a wide range of tasks such as change detection, semantic segmentation, and landslide extraction.
The total image coverage is approximately 860.8 square kilometers. Addressing challenges such as the scarcity of landslide remote sensing data, difficulties in spatiotemporal localization of landslide events, and the high cost of precise annotation, GVLM systematically tackles the long-standing issue of the limited availability of high-quality landslide samples for disaster monitoring and assessment. It provides a high-precision dataset spanning multiple regions and diverse landslide types, and is currently the largest, highest-resolution, and most event-rich benchmark dataset for landslide remote sensing monitoring.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00240.00148 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geoinformatics 71% · Image 75% · Satellite remote sensing 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00240.00148 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3704 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |