Abstract
Foundation models are deep learning models trained on massive datasets. Recent advancements have made them capable of performing a wide range of general tasks, including language processing, summarization, code generation, problem-solving, and reasoning. Geospatial foundation models are specifically trained on large-scale geospatial and temporal data. While the capabilities of general-purpose foundation models have been demonstrated through numerous popular applications, such as natural language generation, question answering, and text summarization, the applications of geospatial foundation models are still in the exploratory stage. In this article, we summarize recent advancements in geospatial foundation models and describe various applications.
| Original language | English (US) |
|---|---|
| Title of host publication | BigSpatial 2024 - Proceedings of the 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data |
| Editors | Ashwin Shashidharan, Krishna Karthik Gadiraju, Varun Chandola, Ranga Raju Vatsavai |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 30-33 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798400711435 |
| DOIs | |
| State | Published - Oct 29 2024 |
| Event | 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2024 - Atlanta, United States Duration: Oct 29 2024 → … |
Publication series
| Name | BigSpatial 2024 - Proceedings of the 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data |
|---|
Conference
| Conference | 12th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2024 |
|---|---|
| Country/Territory | United States |
| City | Atlanta |
| Period | 10/29/24 → … |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Keywords
- Foundation Models
- Geospatial AI
- Geospatial Applications