Examining noncommunicable diseases using satellite imagery: a systematic literature review.
Asthma
Cancer
Chronic disease
Diabetes
Geospatial Epidemiology
Heart Disease
Noncommunicable disease
Population Health
Satellite Imagery
Systematic review
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
10 Oct 2024
10 Oct 2024
Historique:
received:
03
12
2023
accepted:
07
10
2024
medline:
11
10
2024
pubmed:
11
10
2024
entrez:
10
10
2024
Statut:
epublish
Résumé
Noncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide, accounting for 74% of deaths annually. Satellite imagery provides previously unattainable data about factors related to NCDs that overcome limitations of traditional, non-satellite-derived environmental data, such as subjectivity and requirements of a smaller geographic area of focus. This systematic literature review determined how satellite imagery has been used to address the top NCDs in the world, including cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes. A literature search was performed using PubMed (including MEDLINE), CINAHL, Web of Science, Science Direct, Green FILE, and Engineering Village for articles published through June 6, 2023. Quantitative, qualitative, and mixed-methods peer-reviewed studies about satellite imagery in the context of the top NCDs (cancer, cardiovascular disease, chronic respiratory disease, and diabetes) were included. Articles were assessed for quality using the criteria from the Oxford Centre for Evidence-Based Medicine. A total of 43 studies were included, including 5 prospective comparative cohort trials, 22 retrospective cohort studies, and 16 cross-sectional studies. Country economies of the included studies were 72% high-income, 16% upper-middle-income, 9% lower-middle-income, and 0% low-income. One study was global. 93% of the studies found an association between the satellite data and NCD outcome(s). A variety of methods were used to extract satellite data, with the main methods being using publicly available algorithms (79.1%), preprocessing techniques (34.9%), external resource tools (30.2%) and publicly available models (13.9%). All four NCD types examined appeared in at least 20% of the studies. Researchers have demonstrated they can successfully use satellite imagery data to investigate the world's top NCDs. However, given the rapid increase in satellite technology and artificial intelligence, much of satellite imagery used to address NCDs remains largely untapped. In particular, with most existing studies focusing on high-income countries, future research should use satellite data, to overcome limitations of traditional data, from lower-income countries which have a greater burden of morbidity and mortality from NCDs. Furthermore, creating and refining effective methods to extract and process satellite data may facilitate satellite data's use among scientists studying NCDs worldwide.
Identifiants
pubmed: 39390457
doi: 10.1186/s12889-024-20316-z
pii: 10.1186/s12889-024-20316-z
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2774Informations de copyright
© 2024. The Author(s).
Références
World Health Organization. Noncommunicable Diseases. 2022. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
Waters H, Graf M. The Costs of Chronic Disease in the U.S.. Milken Institute; 2018 Aug [cited 2023 Sep 20]. https://milkeninstitute.org/sites/default/files/reports-pdf/ChronicDiseases-HighRes-FINAL_2.pdf
Centers for Disease Control and Prevention. Centers for Disease Control and Prevention. 2023 [cited 2023 Aug 26]. PLACES: Local Data for Better Health. https://www.cdc.gov/places/index.html
World health Organization. Environmental risk factors and NCDs. [cited 2024 Mar 21]. https://www.who.int/teams/noncommunicable-diseases/integrated-support/environmental-risk-factors-and-ncds
World Health Organization W health statistics. The Global Health Observatory. 2023. WHO Indicator Data: Global Health Observatory. https://www.who.int/data/gho/data/themes/topics/indicator-groups/indicator-group-details/GHO/sdg-target-3.4-noncommunicable-diseases-and-mental-health#:~:text=Indicator Groups-,SDG Target 3.4 %7C Noncommunicable diseases and mental health%3A By 2030,mental health and well%2Dbeing
Watkins DA, Msemburi WT, Pickersgill SJ, Kawakatsu Y, Gheorghe A, Dain K, et al. NCD countdown 2030: efficient pathways and strategic investments to accelerate progress towards the sustainable development goal target 3.4 in low-income and middle-income countries. Lancet. 2022;399(10331):1266–78.
doi: 10.1016/S0140-6736(21)02347-3
World Health Organization. Global NCD Compact 2020–2030. [cited 2023 Aug 9]. https://www.who.int/initiatives/global-noncommunicable-diseases-compact-2020-2030/achievements
NASA Earth Science Data Systems. What is Remote Sensing? | Earthdata. 2019 [cited 2024 Mar 21]. https://www.earthdata.nasa.gov/learn/backgrounders/remote-sensing
World Economic Forum. 2020 [cited 2023 Aug 29]. Who owns our orbit: Just how many satellites are there in space? https://www.weforum.org/agenda/2020/10/visualizing-easrth-satellites-sapce-spacex/
Barnard PL, Vitousek S. Earth science looks to outer space. Nat Geosci. 2023;16(2):108–9.
doi: 10.1038/s41561-023-01123-4
Yeh C, Perez A, Driscoll A, Azzari G, Tang Z, Lobell D, et al. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nat Commun. 2020;11(1):2583.
pubmed: 32444658
pmcid: 7244551
doi: 10.1038/s41467-020-16185-w
Amoroso N, Cilli R, Maggipinto T, Monaco A, Tangaro S, Bellotti R. Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality. Environ Res. 2022;204:111970.
pubmed: 34474031
doi: 10.1016/j.envres.2021.111970
Desai AN, Kraemer MU, Bhatia S, Cori A, Nouvellet P, Herringer M, et al. Real-time epidemic forecasting: challenges and opportunities. Health Secur. 2019;17(4):268–75.
pubmed: 31433279
pmcid: 6708259
doi: 10.1089/hs.2019.0022
Rogers DJ, Randolph SE, Snow RW, Hay SI. Satellite imagery in the study and forecast of malaria. Nature. 2002;415(6872):710–5.
pubmed: 11832960
pmcid: 3160466
doi: 10.1038/415710a
Jia P, Stein A, James P, Brownson RC, Wu T, Xiao Q, et al. Earth Observation: investigating Noncommunicable diseases from Space. Annu Rev Public Health. 2019;40(1):85–104.
pubmed: 30633713
doi: 10.1146/annurev-publhealth-040218-043807
Earthdata | Earthdata. [cited 2022 Nov 8]. https://www.earthdata.nasa.gov/
ESA - Eduspace EN - Home. - What is remote sensing?. 2010 [cited 2024 Jan 25]. https://www.esa.int/SPECIALS/Eduspace_EN/SEMF9R3Z2OF_0.html#:~:text=Remote sensing is a way of collecting and,data being in direct contact with the object
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88:105906.
pubmed: 33789826
doi: 10.1016/j.ijsu.2021.105906
Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018;121(Pt 1):1027.
pubmed: 30166065
pmcid: 6908441
doi: 10.1016/j.envint.2018.07.015
Howick J, Chalmers I, Glasziou P, Greenhalgh T, Heneghan C, Liberati A et al. OCEBM Levels of Evidence. Centre for Evidence-Based Medicine (CEBM), University of Oxford. 2021.
Chan CC, Chuang KJ, Chen WJ, Chang WT, Lee CT, Peng CM. Increasing cardiopulmonary emergency visits by long-range transported Asian dust storms in Taiwan. Environ Res. 2008;106(3):393–400.
pubmed: 17959168
doi: 10.1016/j.envres.2007.09.006
De Roos AJ, Kenyon CC, Yen YT, Moore K, Melly S, Hubbard RA, et al. Does living near Trees and other Vegetation affect the contemporaneous odds of Asthma Exacerbation among Pediatric Asthma patients? J Urban Health. 2022;99(3):533–48.
pubmed: 35467328
pmcid: 9187838
doi: 10.1007/s11524-022-00633-7
Evans J, Van Donkelaar A, Martin RV, Burnett R, Rainham DG, Birkett NJ, et al. Estimates of global mortality attributable to particulate air pollution using satellite imagery. Environ Res. 2013;120:33–42.
pubmed: 22959329
doi: 10.1016/j.envres.2012.08.005
Gao J, Liu J, Xu R, Pandey S, Vankayala Siva VSKS, Yu D. Environmental Pollution Analysis and Impact Study—A Case Study for the Salton Sea in California. Atmosphere. 2022;13(6):914.
doi: 10.3390/atmos13060914
Garzon-Chavez DR, Quentin E, Harrison SL, Parisi AV, Butler HJ, Downs NJ. The geospatial relationship of pterygium and senile cataract with ambient solar ultraviolet in tropical Ecuador. Photochem Photobiol Sci. 2018;17(8):1075–83.
pubmed: 29926886
doi: 10.1039/c8pp00023a
Higgs G, Sterling DA, Aryal S, Vemulapalli A, Priftis KN, Sifakis NI. Aerosol Optical Depth as a measure of Particulate exposure using Imputed Censored Data, and relationship with Childhood Asthma Hospital Admissions for 2004 in Athens, Greece. Environ Health Insights. 2015;9s1:EHI.S15665.
James P, Hart JE, Banay RF, Laden F. Exposure to greenness and mortality in a nationwide prospective cohort study of women. Environ Health Perspect. 2016;124(9):1344–52.
pubmed: 27074702
pmcid: 5010419
doi: 10.1289/ehp.1510363
Nguyen HD, Azzi M, White S, Salter D, Trieu T, Morgan G, et al. The summer 2019–2020 wildfires in East Coast Australia and their impacts on Air Quality and Health in New South Wales, Australia. Int J Environ Res Public Health. 2021;18(7):3538.
pubmed: 33805472
pmcid: 8038035
doi: 10.3390/ijerph18073538
Prabhu V, Shridhar V, Choudhary A. Investigation of the source, morphology, and trace elements associated with atmospheric PM10 and human health risks due to inhalation of carcinogenic elements at Dehradun, an Indo-Himalayan city. SN Appl Sci. 2019;1(5):429.
doi: 10.1007/s42452-019-0460-1
Prud’homme G, Dobbin NA, Sun L, Burnett RT, Martin RV, Davidson A, et al. Comparison of remote sensing and fixed-site monitoring approaches for examining air pollution and health in a national study population. Atmos Environ. 2013;80:161–71.
doi: 10.1016/j.atmosenv.2013.07.020
Qu Y, Yang B, Lin S, Bloom MS, Nie Z, Ou Y, et al. Associations of greenness with gestational diabetes mellitus: the Guangdong Registry of congenital heart Disease (GRCHD) study. Environ Pollut. 2020;266:115127.
pubmed: 32650202
doi: 10.1016/j.envpol.2020.115127
Ramesh B, Jagger MA, Zaitchik BF, Kolivras KN, Swarup S, Yang B, et al. Estimating changes in emergency department visits associated with floods caused by Tropical Storm Imelda using satellite observations and syndromic surveillance. Health Place. 2022;74:102757.
pubmed: 35131607
doi: 10.1016/j.healthplace.2022.102757
Stowell JD, Geng G, Saikawa E, Chang HH, Fu J, Yang CE, et al. Associations of wildfire smoke PM2.5 exposure with cardiorespiratory events in Colorado 2011–2014. Environ Int. 2019;133:105151.
pubmed: 31520956
pmcid: 8163094
doi: 10.1016/j.envint.2019.105151
Zhang D, Bai K, Zhou Y, Shi R, Ren H. Estimating ground-level concentrations of Multiple Air Pollutants and their Health impacts in the Huaihe River Basin in China. Int J Environ Res Public Health. 2019;16(4):579.
pubmed: 30781540
pmcid: 6407116
doi: 10.3390/ijerph16040579
Allen RW, Gombojav E, Barkhasragchaa B, Byambaa T, Lkhasuren O, Amram O, et al. An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia. Air Qual Atmos Health. 2013;6(1):137–50.
pubmed: 23450113
doi: 10.1007/s11869-011-0154-3
Dagliati A, Marinoni A, Cerra C, Decata P, Chiovato L, Gamba P, et al. Integration of administrative, clinical, and Environmental Data to support the management of type 2 diabetes Mellitus: from satellites to Clinical Care. J Diabetes Sci Technol. 2016;10(1):19–26.
doi: 10.1177/1932296815619180
Eldeirawi K, Kunzweiler C, Zenk S, Finn P, Nyenhuis S, Rosenberg N, et al. Associations of urban greenness with asthma and respiratory symptoms in Mexican American children. Ann Allergy Asthma Immunol. 2019;122(3):289–95.
pubmed: 30557617
doi: 10.1016/j.anai.2018.12.009
Fan J, Guo Y, Cao Z, Cong S, Wang N, Lin H, et al. Neighborhood greenness associated with chronic obstructive pulmonary disease: a nationwide cross-sectional study in China. Environ Int. 2020;144:106042.
pubmed: 32827808
doi: 10.1016/j.envint.2020.106042
Jimenez MP, Oken E, Gold DR, Luttmann-Gibson H, Requia WJ, Rifas-Shiman SL, et al. Early life exposure to green space and insulin resistance: an assessment from infancy to early adolescence. Environ Int. 2020;142:105849.
pubmed: 32593049
pmcid: 7784302
doi: 10.1016/j.envint.2020.105849
Klompmaker JO, Laden F, Browning MHEM, Dominici F, Ogletree SS, Rigolon A, et al. Associations of parks, greenness, and blue space with cardiovascular and respiratory disease hospitalization in the US Medicare cohort. Environ Pollut. 2022;312:120046.
pubmed: 36049575
pmcid: 10236532
doi: 10.1016/j.envpol.2022.120046
Lambert KA, Lodge C, Lowe AJ, Prendergast LA, Thomas PS, Bennett CM, et al. Pollen exposure at birth and adolescent lung function, and modification by residential greenness. Allergy. 2019;74(10):1977–84.
pubmed: 30934123
doi: 10.1111/all.13803
Qazi S, Iqbal J, Khan JA. Assessment of the health impact of paper mulberry (Broussonetia papyrifera L.), an invasive plant species in Islamabad, Pakistan. Geospatial Health. 2019 Nov 12 [cited 2023 Jul 12];14(2). https://geospatialhealth.net/index.php/gh/article/view/727
Silveira IHD, Junger WL. Green spaces and mortality due to cardiovascular diseases in the city of Rio De Janeiro. Rev Saúde Pública. 2018;52:49.
pubmed: 29723390
pmcid: 5947462
doi: 10.11606/S1518-8787.2018052000290
Vargas-Cuentas NI, Roman-Gonzalez A, Mantari AA, Muñoz LA. Chagas disease study using satellite image processing: a Bolivian case. Acta Astronaut. 2018;144:216–24.
doi: 10.1016/j.actaastro.2017.12.039
Walker BB, Brinkmann ST, Große T, Kremer D, Schuurman N, Hystad P, et al. Neighborhood Greenspace and Socioeconomic Risk are Associated with Diabetes Risk at the Sub-neighborhood Scale: results from the prospective Urban and Rural Epidemiology (PURE) Study. J Urban Health. 2022;99(3):506–18.
pubmed: 35556211
pmcid: 9187823
doi: 10.1007/s11524-022-00630-w
Bauer SE, Wagner SE, Burch J, Bayakly R, Vena JE. A case-referent study: light at night and breast cancer risk in Georgia. Int J Health Geogr. 2013;12(1):23.
pubmed: 23594790
pmcid: 3651306
doi: 10.1186/1476-072X-12-23
Kloog I, Haim A, Stevens RG, Barchana M, Portnov BA. Light at night co-distributes with incident breast but not Lung Cancer in the Female Population of Israel. Chronobiol Int. 2008;25(1):65–81.
pubmed: 18293150
doi: 10.1080/07420520801921572
Medgyesi DN, Trabert B, Fisher JA, Xiao Q, James P, White AJ, et al. Outdoor light at night and risk of endometrial cancer in the NIH-AARP diet and health study. Cancer Causes Control. 2023;34(2):181–7.
pubmed: 36222982
doi: 10.1007/s10552-022-01632-4
Park Y, Ramirez Y, Xiao Q, Liao LM, Jones GS, McGlynn KA. Outdoor light at night and risk of liver cancer in the NIH-AARP diet and health study. Cancer Causes Control. 2022;33(9):1215–8.
pubmed: 35840828
doi: 10.1007/s10552-022-01602-w
Portnov BA, Stevens RG, Samociuk H, Wakefield D, Gregorio DI. Light at night and breast cancer incidence in Connecticut: an ecological study of age group effects. Sci Total Environ. 2016;572:1020–4.
pubmed: 27531467
doi: 10.1016/j.scitotenv.2016.08.006
Xiao Q, James P, Breheny P, Jia P, Park Y, Zhang D, et al. Outdoor light at night and postmenopausal breast cancer risk in the NIH-AARP diet and health study. Int J Cancer. 2020;147(9):2363–72.
pubmed: 32488897
doi: 10.1002/ijc.33016
Xiao Q, Jones RR, James P, Stolzenberg-Solomon RZ. Light at night and risk of pancreatic Cancer in the NIH-AARP Diet and Health Study. Cancer Res. 2021;81(6):1616–22.
pubmed: 33514513
pmcid: 8693799
doi: 10.1158/0008-5472.CAN-20-2256
Hidalgo-García D, Arco-Díaz J. Spatiotemporal analysis of the surface urban heat island (SUHI), air pollution and disease pattern: an applied study on the city of Granada (Spain). Environ Sci Pollut Res. 2023;30(20):57617–37.
doi: 10.1007/s11356-023-26564-7
Yuan Q, Shen H, Li T, Li Z, Li S, Jiang Y, et al. Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ. 2020;241:111716.
doi: 10.1016/j.rse.2020.111716
Upegui E, Viel JF. GeoEye Imagery and Lidar Technology for small-area Population Estimation: an epidemiological viewpoint. Photogramm Eng Remote Sens. 2012;78(7):693–702.
doi: 10.14358/PERS.78.7.693
Liu Y, Zhao B, Cheng Y, Zhao T, Zhang A, Cheng S, et al. Does the quality of street greenspace matter? Examining the associations between multiple greenspace exposures and chronic health conditions of urban residents in a rapidly urbanising Chinese city. Environ Res. 2023;222:115344.
pubmed: 36693460
doi: 10.1016/j.envres.2023.115344
Bibault JE, Bassenne M, Ren H, Xing L. Deep learning prediction of Cancer Prevalence from Satellite Imagery. Cancers. 2020;12(12):3844.
pubmed: 33352801
pmcid: 7766226
doi: 10.3390/cancers12123844
Maharana A, Nsoesie EO. Use of Deep Learning to Examine the Association of the built Environment with Prevalence of Neighborhood adult obesity. JAMA Netw Open. 2018;1(4):e181535.
pubmed: 30646134
pmcid: 6324519
doi: 10.1001/jamanetworkopen.2018.1535
Chatfield K, Simonyan K, Vedaldi A, Zisserman A. Return of the Devil in the Details: Delving Deep into Convolutional Nets. arXiv; 2014 [cited 2023 Sep 14]. http://arxiv.org/abs/1405.3531
Kim Y, Bak SH, Kwon SO, Kim H, Kim WJ, Lee CY. Association between Long-Term exposure to PM2.5 and lung imaging phenotype in CODA Cohort. Atmosphere. 2021;12(2):282.
doi: 10.3390/atmos12020282
Brown SC, Lombard J, Wang K, Byrne MM, Toro M, Plater-Zyberk E, et al. Neighborhood Greenness and Chronic Health conditions in Medicare Beneficiaries. Am J Prev Med. 2016;51(1):78–89.
pubmed: 27061891
doi: 10.1016/j.amepre.2016.02.008
Gariepy G, Kaufman JS, Blair A, Kestens Y, Schmitz N. Place and health in diabetes: the neighbourhood environment and risk of depression in adults with type 2 diabetes. Diabet Med. 2015;32(7):944–50.
pubmed: 25440062
doi: 10.1111/dme.12650
Wang K, Lombard J, Rundek T, Dong C, Gutierrez CM, Byrne MM, et al. Relationship of Neighborhood Greenness to Heart Disease in 249 405 US Medicare Beneficiaries. J Am Heart Assoc. 2019;8(6):e010258.
pubmed: 30835593
pmcid: 6475064
doi: 10.1161/JAHA.118.010258
Yitshak Sade M, Novack V, Ifergane G, Horev A, Kloog I. Air Pollution and ischemic stroke among young adults. Stroke. 2015;46(12):3348–53.
pubmed: 26534971
doi: 10.1161/STROKEAHA.115.010992
Yuan Y, Wu Y, Zhao H, Ren J, Su W, Kou Y, et al. Tropospheric formaldehyde levels infer ambient formaldehyde-induced brain diseases and global burden in China, 2013–2019. Sci Total Environ. 2023;883:163553.
pubmed: 37100142
doi: 10.1016/j.scitotenv.2023.163553
Bureau UC. Census.gov. [cited 2023 Aug 26]. Census.gov. https://www.census.gov/en.html
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579–86.
pubmed: 32416782
pmcid: 7255280
doi: 10.1016/S0140-6736(20)30226-9
Lehnert P, Niederberger M, Backes-Gellner U, Bettinger E. T Jaworski editor 2023 Proxying economic activity with daytime satellite imagery: filling data gaps across time and space. PNAS Nexus 2 4 pgad099.
pubmed: 37077886
pmcid: 10108942
doi: 10.1093/pnasnexus/pgad099
Rolf E, Proctor J, Carleton T, Bolliger I, Shankar V, Ishihara M, et al. A generalizable and accessible approach to machine learning with global satellite imagery. Nat Commun. 2021;12(1):4392.
pubmed: 34285205
pmcid: 8292408
doi: 10.1038/s41467-021-24638-z
Malekzadeh A, Michels K, Wolfman C, Anand N, Sturke R. Strengthening research capacity in LMICs to address the global NCD burden. Glob Health Action. 2020;13(1):1846904.
pubmed: 33373280
pmcid: 7782223
doi: 10.1080/16549716.2020.1846904
Canadian Space Agency, Canadian Space A. 2018 [cited 2023 Aug 11]. How satellites help you stay healthy. https://www.asc-csa.gc.ca/eng/satellites/everyday-lives/how-satellites-help-you-stay-healthy.asp
mosaiks.org. 2023 [cited 2023 Aug 11]. mosaiks.org. https://www.mosaiks.org
National Oceanic and Atmospheric Administration. GOES-R Algorithm Theoretical Basis Documents. 2023 [cited 2023 Dec 3]. https://www.star.nesdis.noaa.gov/goesr/documentation_ATBDs.php
NASA. Worldview. [cited 2023 Sep 17]. https://worldview.earthdata.nasa.gov/
Earth Online. [cited 2023 Sep 17]. https://earth.esa.int/eogateway/
EUMETSAT. EUMETSAT | Monitoring the weather and climate from space | EUMETSAT. [cited 2023 Sep 17]. https://www.eumetsat.int/
Gettelman A, Geer AJ, Forbes RM, Carmichael GR, Feingold G, Posselt DJ, et al. The future of Earth system prediction: advances in model-data fusion. Sci Adv. 2022;8(14):eabn3488.
pubmed: 35385304
pmcid: 8985915
doi: 10.1126/sciadv.abn3488
Chen CP, Zhang CY. Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci. 2014;275:314–47.
doi: 10.1016/j.ins.2014.01.015
Kotawadekar R. 9 - Satellite data: big data extraction and analysis. In: Binu D, Rajakumar BR, editors. Artificial Intelligence in Data Mining. Academic Press; 2021 [cited 2023 Aug 26]. pp. 177–97. https://www.sciencedirect.com/science/article/pii/B9780128206010000082
Abu Qdais H, Shatnawi N. Assessing and predicting landfill surface temperature using remote sensing and an artificial neural network. Int J Remote Sens. 2019;40(24):9556–71.
doi: 10.1080/01431161.2019.1633703
Baghanam AH, Vakili AT, Nourani V, Dąbrowska D, Soltysiak M. AI-based ensemble modeling of landfill leakage employing a lysimeter, climatic data and transfer learning. J Hydrol. 2022;612:128243.
doi: 10.1016/j.jhydrol.2022.128243
Tahir A, Munawar HS, Akram J, Adil M, Ali S, Kouzani AZ, et al. Automatic target detection from Satellite Imagery using machine learning. Sensors. 2022;22(3):1147.
pubmed: 35161892
pmcid: 8839603
doi: 10.3390/s22031147
Tariq A, Siddiqui S, Sharifi A, Shah SHIA. Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan. Arab J Geosci. 2022;15(11):1045.
doi: 10.1007/s12517-022-10238-8
Jiang H, Peng M, Zhong Y, Xie H, Hao Z, Lin J, et al. A Survey on Deep Learning-based change detection from high-resolution remote sensing images. Remote Sens. 2022;14(7):1552.
doi: 10.3390/rs14071552
Bistron M, Piotrowski Z. Artificial Intelligence Applications in Military systems and their influence on sense of security of citizens. Electronics. 2021;10(7):871.
doi: 10.3390/electronics10070871
United Nations Register of Objects. Launched into Outer Space. [cited 2023 Aug 29]. https://www.unoosa.org/oosa/en/spaceobjectregister/index.html