Data · dataset · 2025
Houston Meyerland Street-Level Image Dataset (HMSLID)
Listed in Teesside University Research Data Repository
This dataset comprises a novel collection of 92,725 street-level images sourced from Google Street View (GSV), meticulously curated to serve as a high-precision reference database for disaster image geolocalization research.
Description
The dataset covers approximately 15 square miles (39 square km) of the Meyerland district in Houston, Texas, a region historically prone to significant flooding, including major events like the Memorial Day flood (2015), Tax Day flood (2016), and Hurricane Harvey (2017).
Images were systematically collected by first extracting road data from OpenStreetMap within a defined boundary box (Latitude: 29.70344981°N to 29.6527425°S; Longitude: -95.43105637°E to -95.5026166°W). Points were then generated along these roads at 11-meter intervals, yielding 24,201 distinct GPS coordinates. For each coordinate, four 640x640 pixel GSV images were downloaded via the GSV Image API, capturing headings of 0°, 90°, 180°, and 270°.
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A temporal filter was applied to include only images from 2017 and prior, ensuring relevance to the Hurricane Harvey disaster context. Unlike broader, less specific datasets, this Meyerland reference database offers a localized scope with known geopositions for robust quantitative evaluation of image geolocation techniques in challenging, flood-affected urban environments. It aims to address the limitations of existing datasets and accelerate advancements in disaster informatics and computer vision applications for real-time emergency response.
Links
Where it is published
- DOI doi.org/10.17632/wxmyj2sy42.2 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Computer vision · Deep learning · Disaster and emergency management · Earth & Environmental Science · Social Science
- Inferred from text
- Image 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/wxmyj2sy42.2 | 16 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:350703 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Disaster Management'] |
| concepts[field].anzsrc:field:460304 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Computer Vision'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |