Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping
Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) and Earth observation to a new stage. GeoAI has enhanced traditional geospatial analysis and mapping, altering the methods for understanding and managing complex human...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
2023
|
| Online Access: | http://hdl.handle.net/20.500.11937/97966 |
| _version_ | 1848766345329508352 |
|---|---|
| author | Song, Yongze Kalacska, M. Gašparović, M. Yao, J. Najibi, N. |
| author_facet | Song, Yongze Kalacska, M. Gašparović, M. Yao, J. Najibi, N. |
| author_sort | Song, Yongze |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) and Earth observation to a new stage. GeoAI has enhanced traditional geospatial analysis and mapping, altering the methods for understanding and managing complex human–natural systems. However, there are still challenges in various aspects of geospatial applications related to natural, built, and social environments, and in integrating unique geospatial features into GeoAI models. Meanwhile, geospatial and Earth data are critical components in geocomputation and GeoAI studies, as they can effectively reveal geospatial patterns, factors, relationships, and decision-making processes. This editorial provides a comprehensive overview of geocomputation and GeoAI applications in mapping, classifying them into four categories: (i) buildings and infrastructure, (ii) land use analysis, (iii) natural environment and hazards, and (iv) social issues and human activities. In addition, the editorial summarizes geospatial and Earth data in case studies into seven categories, including in-situ data, geospatial datasets, crowdsourced geospatial data (i.e., geospatial big data), remote sensing data, photogrammetry data, LiDAR, and statistical data. Finally, the editorial presents challenges and opportunities for future research. |
| first_indexed | 2025-11-14T11:49:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-97966 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:40Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-979662025-07-22T06:00:54Z Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping Song, Yongze Kalacska, M. Gašparović, M. Yao, J. Najibi, N. Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) and Earth observation to a new stage. GeoAI has enhanced traditional geospatial analysis and mapping, altering the methods for understanding and managing complex human–natural systems. However, there are still challenges in various aspects of geospatial applications related to natural, built, and social environments, and in integrating unique geospatial features into GeoAI models. Meanwhile, geospatial and Earth data are critical components in geocomputation and GeoAI studies, as they can effectively reveal geospatial patterns, factors, relationships, and decision-making processes. This editorial provides a comprehensive overview of geocomputation and GeoAI applications in mapping, classifying them into four categories: (i) buildings and infrastructure, (ii) land use analysis, (iii) natural environment and hazards, and (iv) social issues and human activities. In addition, the editorial summarizes geospatial and Earth data in case studies into seven categories, including in-situ data, geospatial datasets, crowdsourced geospatial data (i.e., geospatial big data), remote sensing data, photogrammetry data, LiDAR, and statistical data. Finally, the editorial presents challenges and opportunities for future research. 2023 Journal Article http://hdl.handle.net/20.500.11937/97966 10.1016/j.jag.2023.103300 http://creativecommons.org/licenses/by-nd/4.0/ fulltext |
| spellingShingle | Song, Yongze Kalacska, M. Gašparović, M. Yao, J. Najibi, N. Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title | Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title_full | Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title_fullStr | Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title_full_unstemmed | Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title_short | Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping |
| title_sort | advances in geocomputation and geospatial artificial intelligence (geoai) for mapping |
| url | http://hdl.handle.net/20.500.11937/97966 |