TY - JOUR

T1 - Geo-Information for Humanitarian Mapping and Monitoring Crisis-Affected Regions: A Scoping Review

AU - Леменкова, Полина Алексеевна

PY - 2025/9/29

Y1 - 2025/9/29

N2 - The migration crisis is generated by mass movements of population within or outside the national borders of a country. Triggers to this phenomenon include either sudden events, such as natural catastrophes (floods, earthquakes) or gradual social pressure (wars and civil unrest). This paper aims to analyse the effective cartographic methods of mapping changing patterns of human movements. Replaced settlements are visible from space and can be mapped effectively using satellite images processed by Geo-Information Systems (GIS). This review study presents a thorough in-depth analysis of the significant role of the ML and GIS and their incorporating into crisis control and monitoring migration situations. Machine Learning (ML) hold a significant role in processing geospatial referenced data which is essential for mapping humanitarian crisis using Earth observation data. This review study presents a thorough in-depth analysis of the significant role of the ML and GIS and their incorporating into crisis control and monitoring migration situations. Understanding the reasons of migratory movements is supported by the interrogation of the trajectories which can be detected from space for mapping the ways of the migration's paths. A systematic literature review was performed, synthesizing findings from existing approaches, geospatial analysis and field observations related to humanitarian mapping. This study reveal that integrated use of ML, GIS and EO data can facilitate mapping the endangered areas for sustainable planning during crisis events across multiple spatiotemporal scales.

AB - The migration crisis is generated by mass movements of population within or outside the national borders of a country. Triggers to this phenomenon include either sudden events, such as natural catastrophes (floods, earthquakes) or gradual social pressure (wars and civil unrest). This paper aims to analyse the effective cartographic methods of mapping changing patterns of human movements. Replaced settlements are visible from space and can be mapped effectively using satellite images processed by Geo-Information Systems (GIS). This review study presents a thorough in-depth analysis of the significant role of the ML and GIS and their incorporating into crisis control and monitoring migration situations. Machine Learning (ML) hold a significant role in processing geospatial referenced data which is essential for mapping humanitarian crisis using Earth observation data. This review study presents a thorough in-depth analysis of the significant role of the ML and GIS and their incorporating into crisis control and monitoring migration situations. Understanding the reasons of migratory movements is supported by the interrogation of the trajectories which can be detected from space for mapping the ways of the migration's paths. A systematic literature review was performed, synthesizing findings from existing approaches, geospatial analysis and field observations related to humanitarian mapping. This study reveal that integrated use of ML, GIS and EO data can facilitate mapping the endangered areas for sustainable planning during crisis events across multiple spatiotemporal scales.

KW - migration

KW - social development

KW - disaster mitigation

KW - data analysis

KW - information

UR - https://dergipark.org.tr/en/pub/mipos/issue/94550/1684741

UR - https://www.mendeley.com/catalogue/fa74ef03-0c5f-3e96-9a86-56458d0c9d94/

U2 - 10.69510/mipos.1684741

DO - 10.69510/mipos.1684741

M3 - Article

VL - 3

SP - 196

EP - 220

JO - Journal of Migration and Political Studies

JF - Journal of Migration and Political Studies

SN - 3023-4328

IS - 2

ER -