ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2018
Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.BMJ global health · 2022Pooled it
- From innovation to implementation: a global scoping review of artificial intelligence applications, governance, and challenges in humanitarian contexts.Journal of global health · 2026Article
- Advances in small area population estimation in the absence of national census data.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- Estimation of internal displacement in Ukraine from satellite-based car detections.Scientific reports · 2024Article
- Using spatial video and deep learning for automated mapping of ground-level context in relief camps.International journal of health geographics · 2024Article
- AI and climate resilience governance.iScience · 2024Review
- Toward the remote monitoring of armed conflicts.PNAS nexus · 2023Article
- SEmHuS: a semantically embedded humanitarian space.Journal of international humanitarian action · 2023Article
- Understanding peace through the world news.EPJ data science · 2022Article
- What we do know (and could know) about estimating population sizes of internally displaced people.Journal of migration and health · 2022Article
- Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan.Transactions in GIS : TG · 2021Article
- SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images.PeerJ. Computer science · 2021Article
- Article
- The growing ubiquity of algorithms in society: implications, impacts and innovations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.