Data · dataset · 2026
Investigating the capabilities of handheld LiDAR in mapping and identifying underground spaces in the urban environment
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32635083.v1
Coastal flooding presents an increasing threat to many urban centres.
Description
Current flood models are often based upon low-density topographic data and do not include information regarding underground structures, which are high-risk areas and act as sinks in flood scenarios, affecting the flow of water through the streetscape.<br><br>The background study details flood modelling and topographic surveying approaches. Comparison finds that mobile LiDAR provides the most promising solution for accurate, efficient and repeatable modelling of urban environments.<br><br>The methodology describes a novel and repeatable process for surveying a cityscape, using Belfast city centre as the primary study area.
The GeoSLAM Zeb Horizon handheld LiDAR scanner is the primary surveying instrument as it is cost effective (£30-35k) and highly manoeuvrable.<br><br>Results indicate that the digital city model created has improved point density and accuracy compared to the previous highest resolution model. Relative point accuracy ranges from 1-2cm, and point density for targeted features exceeds 3000pts/m². The merging and georeferencing process introduces a negligible amount of error (Comparison with high-resolution aerial laser scanning data collected in Dublin (2015) highlights the strengths and weaknesses of the handheld approach.
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The GeoSLAM provides improved point density and accuracy, especially on building facades, but provides poor coverage of rooftops and gated areas, due to the angle of data capture. Comparison with tripod mounted terrestrial laser scanning (TLS) collected in Belfast highlights a disparity in accuracy and resolution, which the GeoSLAM makes up for with surveying efficiency.<br><br>The relationship between geographical information systems (GIS) and LiDAR data is explored and showcases the importance of GIS based datasets in organising and producing city scale LiDAR datasets.
GIS based approaches were developed to locate underground spaces within the point cloud, to promote their repetition and widespread use throughout relevant organisations. ArcGIS Pro has proven to be a suitable platform for displaying the location of basements scanned directly, indirectly and for which façade features indicate their presence.<br><br>Handheld mobile LiDAR scanning proves to fill a niche within the remote sensing market.
The hardware provides cost and time efficient data capture, inclusive of street level and underground features with resolution and accuracy great enough to enhance flood simulations. The easy deployment of the technology promotes consistent updates of digital models as the streetscape inevitably changes over time. Handheld scanning is suitable for creating a relatively inexpensive digital model or as a source of supplementary data for pre-existing topographic datasets.<br>
Links
Where it is published
- DOI doi.org/10.17034/32635083.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Photogrammetry and remote sensing · Photogrammetry and remote sensing · Photogrammetry and remote sensing · Satellite remote sensing · Satellite remote sensing · Satellite remote sensing · Social Science · Social Science · Social Science
Provenance · 3 source records, 16 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32635083 | 8 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32635083 | 8 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32635083 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:401304 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| concepts[field].anzsrc:field:401304 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| concepts[field].anzsrc:field:401304 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:social-science | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[modality].local:modality:remote-sensing | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| concepts[modality].local:modality:remote-sensing | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| concepts[modality].local:modality:remote-sensing | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['remote sensing'] |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |