Using machine learning to assess the livelihood impact of electricity access

Ratledge, Nathan; Cadamuro, Gabe; Cuesta, Brandon de la; Stigler, Matthieu; Burke, Marshall

doi:10.1038/s41586-022-05322-8

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…Through a series of model validation, explanation, generalization ability tests, and applications (Syrian Civil War and Russo-Ukrainian War), this study provides evidence that the our proposed scheme has excellent urban damage monitoring capability on high-and mediumresolution satellite imagery, and has the ability to generate high performance in out-ofsample tests. Our results also indicate that a combination of remote sensing imagery and knowledge-guided deep learning models is capable of generating data appropriate for program evaluation [35]. The findings from the urban detection results reveal that substantial buildings have been destroyed in the entire Mariupol city by the Russia-Ukraine war, in particular the Livoberezhnyi, Zhovtnevyi and Azovstal areas.…”

Section: Discussionmentioning

confidence: 68%

Satellite monitoring of war urban damage with a temporal knowledge-guided deep learning scheme

Zhang

Hou

et al. 2023

Preprint

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Since 1990, between 40 and 68 countries, home to 46–79% of the population in the world were involved in armed conflict per year [1–3], which have caused huge socio-economic costs and severe casualties. Geotargeting is a central challenge for comprehensive assessments of wars and humanitarian assistance: it remains a difficult task to rapidly identify severe destructive zones with traditional or satellite-based methods. To overcome the challenge, we propose the first tempo- ral knowledge-guided detection scheme (TKDS) for satellite urban destruction monitoring. By integrating the proposed pixel-based Transformer model (PtNet) into the efficient TKDS scheme (TKDS-PtNet), we achieve the best destruction detection performance in the Syrian civil war and Russia-Ukraine war with high or medium resolution satellite imagery. Even using medium-resolution satellite images, the performance of the TKDS-PtNet model outperforms the state-of-the-art methods by 44.4 (more than twice as high as the latter, 72.4 vs 28.0) in terms of F1 score in six Syrian cities, and 36.1 (84.6 vs 48.5) in four Ukrainian cities. Our findings located 1,652 severely damaged buildings in Mariupol, Ukraine, including various urban infrastructures such as 81.5% (1,346) houses/apartments, 4.0% (66) industrial, as well as 11 garages, 2 schools, 2 kindergartens, and 1 sports center. Our scheme provides a low-cost, reliable, highly flexible, and generalizable solution for urban destruction monitoring in data-sparse environments. It can be applied to any urban context to near real-time monitor damage caused by wars, earthquakes, or extreme weather events, and approximate the spatio-temporal distribution of the damages to aid relief efforts, in addition to the estimation of potential rehabilitation costs.

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Section: Discussionmentioning

confidence: 68%

Satellite monitoring of war urban damage with a temporal knowledge-guided deep learning scheme

Zhang

Hou

et al. 2023

Preprint

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“…Machine learning (ML) has made impressive achievements in substance discovery, data analysis, and image processing over the past decades, accelerating advances in elds as numerous as earth & life science, [1][2][3] communications & transportation, [4][5][6][7][8][9][10] and chemistry & medicine. [11][12][13][14][15][16][17][18][19][20] As a spotlight to the eld of chemistry, ML provides experimentalists with advice on selecting target molecules for synthesis by predicting physicochemical properties, [11][12][13][14][15] biological effects, [16][17][18] and reaction routes.…”

Section: Introductionmentioning

confidence: 99%

Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk

Yu,

Zhou,

Wang

et al. 2024

Digital Discovery

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“…Autopilot 15 is an exciting application based on ML, and selects the best driving operation by unsupervised learning methods. Almost all mailboxes are configured with spam‐filtering technology, 16,17 and ML has shown new vitality and opportunities in medical diagnosis, 18 data mining, 19 scientific innovation, 20 and social investigation 21 . In a special capacity, it helps researchers to reveal obscure logical relationships and undiscovered theorems from data, 22,23 based on which the results of experiments that have not yet been carried out can be predicted.…”

Section: Introductionmentioning

confidence: 99%

Methods, progresses, and opportunities of materials informatics

Li,

Zheng

2023

InfoMat

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As an implementation tool of data intensive scientific research methods, machine learning (ML) can effectively shorten the research and development (R&D) cycle of new materials by half or even more. ML shows great potential in the combination with other scientific research technologies, especially in the processing and classification of large amounts of material data from theoretical calculation and experimental characterization. It is very important to systematically understand the research ideas of material informatics to accelerate the exploration of new materials. Here, we provide a comprehensive introduction to the most commonly used ML modeling methods in material informatics with classic cases. Then, we review the latest progresses of prediction models, which focus on new processing–structure–properties–performance (PSPP) relationships in some popular material systems, such as perovskites, catalysts, alloys, two‐dimensional materials, and polymers. In addition, we summarize the recent pioneering researches in innovation of material research technology, such as inverse design, ML interatomic potentials, and microtopography characterization assistance, as new research directions of material informatics. Finally, we comprehensively provide the most significant challenges and outlooks related to the future innovation and development in the field of material informatics. This review provides a critical and concise appraisal for the applications of material informatics, and a systematic and coherent guidance for material scientists to choose modeling methods based on required materials and technologies.

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Using machine learning to assess the livelihood impact of electricity access

Cited by 31 publications

References 28 publications

Satellite monitoring of war urban damage with a temporal knowledge-guided deep learning scheme

Satellite monitoring of war urban damage with a temporal knowledge-guided deep learning scheme

Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk

Methods, progresses, and opportunities of materials informatics

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