Self-Supervised Representation Learning for Geographical Data—A Systematic Literature Review
Bibliographic Data
| ID | 22033697 |
|---|---|
| Authors | Padraig Corcoran (0000-0001-9731-3385, Cardiff University, corresponding author), Irena Spasić (0000-0002-8132-3885, Cardiff University) |
| Year | 2023 |
| Volume | 12 |
| Issue | 2 |
| Pages | 64 |
| Publication date | 2023-02-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12020064 |
| OpenAlex | W4320491011 |
| Language | EN |
| Citations received | 2 |
| References cited | 92 |
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). In this article, we systematically review the existing research literature in this space to answer the following five research questions. What types of representations were learnt? What SSRL models were used? What downstream problems were the representations used to solve? What machine learning models were used to solve these problems? Finally, does using a learnt representation improve the overall performance
Data science · External Data Representation · Feature learning · Machine learning · Computer Science · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Mathematics · Text and Document Classification Technologies · Artificial Intelligence
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Delineating urban functional use from points of interest data with neural network embedding
A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data
Gsam
A Coefficient of Agreement for Nominal Scales
Word embeddings and deep learning for location prediction
Unsupervised machine learning in urban studies
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |