Pith. sign in

REVIEW 1 cited by

A comprehensive GeoAI review: Progress, Challenges and Outlooks

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.11643 v1 pith:RROHDDUO submitted 2024-12-16 cs.AI physics.geo-ph

classification cs.AIphysics.geo-ph
keywords geoaigeospatialartificialcarriedchallengescomprehensivedatafields
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In recent years, Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications, while also becoming involved in various fields of use. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management and urban planning. Next, a statistical and semantic analysis is carried out, followed by a clear and precise presentation of the challenges facing GeoAI. Then, a concrete exploration of the future prospects is provided, based on several informations gathered during the census. To sum up, this paper provides a complete overview of the correlation between AI and the geospatial domain, while mentioning the researches conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems (GIS) and large-scale geospatial data, known as big geodata. This will enable researchers and scientific community to assess the state of progress in this promising field, and will help other interested parties to gain a better understanding of the issues involved.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A new open polygon dataset with 52,225 labeled land cover polygons for oil palm mapping in Riau and West Sulawesi, validated at 83% overall accuracy.

Pith tools