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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Sternberg, Troy & Christopher McCarthy (2026). Artificial Intelligence Applications in Desert Research. Japanese Association for Arid Land Studies .
Exportar Referência (IEEE) 
T. Sternberg and C. McCarthy,  "Artificial Intelligence Applications in Desert Research", in Japanese Association for Arid Land Studies , 2026
Exportar BibTeX 
@article{sternberg2026_1766802407695,
	author = "Sternberg, Troy and Christopher McCarthy",
	title = "Artificial Intelligence Applications in Desert Research",
	journal = "Japanese Association for Arid Land Studies ",
	year = "2026",
	volume = "",
	number = "",
	url = "https://www.jaals.net/conferences/2025-conferences/"
}
Exportar RIS 
TY  - JOUR
TI  - Artificial Intelligence Applications in Desert Research
T2  - Japanese Association for Arid Land Studies 
AU  - Sternberg, Troy
AU  - Christopher McCarthy
PY  - 2026
UR  - https://www.jaals.net/conferences/2025-conferences/
AB  - Covering 40% of the globe, deserts are critical research landscapes. Across arid regions rapid environmental and ecological change reconfigure environments. Characterised by vast datasets and great spatial extent, desert research faces limitations to information analysis, numerical processing and pattern identification. To address and understand our changing world conventional academic study can now take advantage of new technologies for cutting-edge dryland research. Today Artificial Intelligence (AI) provides an advanced method to investigate the world’s deserts. Drawing on our recent paper on species identification in deserts, we outline initial uses of AI architecture in dryland research. 

Current AI tools can maximise researchers’ capacity and creativity. Examples include algorithms to analyze satellite imagery, quantifying desert expansion and vegetation changes and models predicting drought patterns and water availability. Techniques can detect geological features, including earthquake fault lines, and give insights into groundwater systems and natural hazards. Natural language processing synthesizes research literature and environmental reports. In combination these advances suggest AI applications can be part of a unified system for comprehensive desert landscape analysis. Whilst AI presents challenges and caveats, data-rich arid and semi-arid investigations can benefit from the new AI research paradigm.

ER  -