{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:18:06Z","timestamp":1783095486433,"version":"3.54.6"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000046","name":"National Research Council Canada","doi-asserted-by":"publisher","award":["DHGA-119-1"],"award-info":[{"award-number":["DHGA-119-1"]}],"id":[{"id":"10.13039\/501100000046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ecological Informatics"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ecoinf.2026.103824","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T22:23:03Z","timestamp":1778970183000},"page":"103824","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling"],"prefix":"10.1016","volume":"96","author":[{"given":"Mohamed","family":"Elhefnawy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Ragab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Pelletier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Martin","family":"Lussier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mouloud","family":"Amazouz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ecoinf.2026.103824_bb0005","series-title":"12th Symposium on Operating Systems Design and Implementation","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016"},{"issue":"April","key":"10.1016\/j.ecoinf.2026.103824_bb0010","article-title":"Landslide susceptibility assessment along the Karakoram Highway, Gilgit Baltistan, Pakistan: a comparative study between ensemble and neighbor-based machine learning algorithms","volume":"9","author":"Abbas","year":"2024","journal-title":"Sci. Remote Sens."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0015","doi-asserted-by":"crossref","first-page":"9533","DOI":"10.1109\/ACCESS.2017.2697839","article-title":"Data fusion and IoT for smart ubiquitous environments: a survey","volume":"5","author":"Alam","year":"2017","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0020","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s13595-014-0400-6","article-title":"A Swedish case study on the prediction of detailed product recovery from individual stem profiles based on airborne laser scanning","volume":"72","author":"Barth","year":"2015","journal-title":"Ann. For. Sci."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0025","article-title":"Improving the accuracy of machine-learning models with data from machine test repetitions","author":"Bustillo","year":"2020","journal-title":"J. Intell. Manuf."},{"issue":"9","key":"10.1016\/j.ecoinf.2026.103824_bb0030","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1002\/cem.2806","article-title":"Networkmetrics: multivariate big data analysis in the context of the internet","volume":"30","author":"Camacho","year":"2016","journal-title":"J. Chemom."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0035","series-title":"The State of Canada\u2019s Forests: Annual Report 2025","author":"Canada","year":"2026"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0040","doi-asserted-by":"crossref","DOI":"10.1155\/2013\/704504","article-title":"A review of data fusion techniques","volume":"2013","author":"Castanedo","year":"2013","journal-title":"Sci. World J."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0045","series-title":"Conference record - IEEE instrumentation and measurement technology conference, 2016-July(51475170)","article-title":"Machine fault classification using deep belief network","author":"Chen","year":"2016"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0050","first-page":"3","article-title":"STL: a seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-444-63984-4.00001-6","article-title":"Introduction: ways and means to Deal with data from multiple sources","volume":"31","author":"Cocchi","year":"2019","journal-title":"Data Handling in Science and Technology"},{"issue":"2","key":"10.1016\/j.ecoinf.2026.103824_bib361","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1214\/aoms\/1177698950","article-title":"Upper and lower probabilities induced by a multivalued mapping","volume":"38","author":"Dempster","year":"1967","journal-title":"Ann. Math. Stat."},{"issue":"5","key":"10.1016\/j.ecoinf.2026.103824_bb0060","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s10845-021-01742-x","article-title":"Fault classification in the process industry using polygon generation and deep learning","volume":"33","author":"Elhefnawy","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0065","article-title":"Optimizing multi-sensor data fusion for land cover classification using machine learning","volume":"256","author":"Frampton","year":"2021","journal-title":"Remote Sens. Environ."},{"issue":"March","key":"10.1016\/j.ecoinf.2026.103824_bb0070","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0075","series-title":"Data Integration in the Era of Omics: Current and Future Challenges","author":"Gomez-Cabrero","year":"2014"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0080","series-title":"Deep Learning","first-page":"330","article-title":"Convolutional networks","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0085","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0090","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2021.737149","article-title":"Fischer linear discrimination and quadratic discrimination analysis--based data mining technique for internet of things framework for healthcare","volume":"9","author":"Hasan","year":"2021","journal-title":"Front. Public Health"},{"issue":"8","key":"10.1016\/j.ecoinf.2026.103824_bb0095","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1139\/cjfr-2017-0467","article-title":"Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning","volume":"48","author":"Hauglin","year":"2018","journal-title":"Can. J. For. Res."},{"issue":"June","key":"10.1016\/j.ecoinf.2026.103824_bb0100","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2020.07.003","article-title":"Data fusion strategies for energy efficiency in buildings: overview, challenges and novel orientations","volume":"64","author":"Himeur","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0105","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.1993.1626170","article-title":"The behavior-knowledge space method for combination of multiple classifiers","volume":"347","author":"Huang","year":"1993","journal-title":"IEEE Comp. Soc. Conf. Comp. Vision Pattern Recogn."},{"issue":"12","key":"10.1016\/j.ecoinf.2026.103824_bb0110","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1139\/cjfr-2020-0033","article-title":"Field calibration of merchantable and sawlog volumes in forest inventories based on airborne laser scanning","volume":"50","author":"Karjalainen","year":"2020","journal-title":"Can. J. For. Res."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0115","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","article-title":"Multisensor data fusion: a review of the state-of-the-art","volume":"14","author":"Khaleghi","year":"2013","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0120","doi-asserted-by":"crossref","DOI":"10.1177\/2053951714528481","article-title":"Big data, new epistemologies and paradigm shifts","volume":"1","author":"Kitchin","year":"2014","journal-title":"Big Data Soc."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0125","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","article-title":"Industry 4.0","volume":"6","author":"Lasi","year":"2014","journal-title":"Bus. Inf. Syst. Eng."},{"issue":"January","key":"10.1016\/j.ecoinf.2026.103824_bb0130","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.inffus.2019.05.004","article-title":"A survey of data fusion in smart city applications","volume":"52","author":"Lau","year":"2019","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0135","series-title":"A Survey on Deep Learning for Multimodal Data Fusion","author":"Li","year":"2020"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0140","first-page":"1","article-title":"Forest aboveground biomass estimation using Landsat 8 and sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.ecoinf.2026.103824_bb0145","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1007\/s10845-017-1380-9","article-title":"A data-driven method based on deep belief networks for backlash error prediction in machining centers","volume":"31","author":"Li","year":"2020","journal-title":"J. Intell. Manuf."},{"issue":"3","key":"10.1016\/j.ecoinf.2026.103824_bb0155","doi-asserted-by":"crossref","DOI":"10.14214\/sf.10075","article-title":"Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning","volume":"53","author":"Maltamo","year":"2019","journal-title":"Silva Fennica."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0160","doi-asserted-by":"crossref","DOI":"10.1016\/B978-0-444-63984-4.00009-0","article-title":"Dealing with data heterogeneity in a data fusion perspective: models, methodologies, and algorithms","volume":"31","author":"Mandreoli","year":"2019","journal-title":"In Data Handling Sci. Technol."},{"issue":"11","key":"10.1016\/j.ecoinf.2026.103824_bb0165","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1002\/cem.2740","article-title":"Quantitative big data: where chemometrics can contribute","volume":"29","author":"Martens","year":"2015","journal-title":"J. Chemom."},{"issue":"2","key":"10.1016\/j.ecoinf.2026.103824_bb0170","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.inffus.2019.12.001","article-title":"A survey on machine learning for data fusion","volume":"57","author":"Meng","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolmodel.2019.108815","article-title":"Importance of spatial predictor variable selection in machine learning applications \u2013 moving from data reproduction to spatial prediction","volume":"411","author":"Meyer","year":"2019","journal-title":"Ecol. Model."},{"issue":"November","key":"10.1016\/j.ecoinf.2026.103824_bb0180","article-title":"Modeling tree species diversity by combining ALS data and digital aerial photogrammetry","volume":"2","author":"Mohammadi","year":"2020","journal-title":"Sci. Remote Sens."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0185","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/00049158.2006.10674982","article-title":"Developing methods for pre-harvest inventories which use a harvester as the sampling tool","volume":"69","author":"Murphy","year":"2006","journal-title":"Aust. For."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0190","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0195","article-title":"Multi-sensor data fusion with gradient boosting for improved forest variable prediction","volume":"74","author":"Nguyen","year":"2023","journal-title":"Eco. Inform."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0200","doi-asserted-by":"crossref","DOI":"10.14214\/sf.10608","article-title":"Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests","volume":"56","author":"Noordermeer","year":"2022","journal-title":"Silva Fennica"},{"issue":"3","key":"10.1016\/j.ecoinf.2026.103824_bb0205","doi-asserted-by":"crossref","DOI":"10.14214\/sf.23023","article-title":"Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches","volume":"57","author":"Noordermeer","year":"2023","journal-title":"Silva Fennica"},{"issue":"May","key":"10.1016\/j.ecoinf.2026.103824_bb0210","first-page":"1","article-title":"Early detection of Alzheimer\u2019s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning","volume":"14","author":"Pan","year":"2020","journal-title":"Front. Neurosci."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0215","doi-asserted-by":"crossref","DOI":"10.14214\/sf.237","article-title":"Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach","volume":"42","author":"Peuhkurinen","year":"2008","journal-title":"Silva Fennica"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0220","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.neucom.2014.09.088","article-title":"Diagnosis of misalignment faults by tacholess order tracking analysis and RBF networks","volume":"169","author":"Qi","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2022.103640","article-title":"Decision fusion for reliable fault classification in energy-intensive process industries","volume":"138","author":"Ragab","year":"2022","journal-title":"Comput. Ind."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0230","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ipl.2004.12.002","article-title":"On Hamiltonian cycles and Hamiltonian paths","volume":"94","author":"Rahman","year":"2005","journal-title":"Inf. Process. Lett."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0235","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1139\/cjfr-2022-0053","article-title":"Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data","volume":"53","author":"R\u00e4ty","year":"2022","journal-title":"Can. J. For. Res."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109793","article-title":"Applications of artificial intelligence and LiDAR in forest inventories: a systematic literature review","volume":"120","author":"Rodrigues","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0245","unstructured":"Sakpal, M. 2021. 12 Actions to Improve Your Data Quality. Retrieved December 13, 2021, from https:\/\/www.gartner.com\/smarterwithgartner\/how-to-improve-your-data-quality."},{"issue":"8","key":"10.1016\/j.ecoinf.2026.103824_bb0250","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1139\/cjfr-2017-0172","article-title":"Valuation of growing stock using multisource GIS data, a stem quality database, and bucking simulation","volume":"48","author":"Sanz","year":"2018","journal-title":"Can. J. For. Res."},{"issue":"9","key":"10.1016\/j.ecoinf.2026.103824_bb0255","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.3390\/f12091221","article-title":"Integrating detailed timber assortments into airborne laser scanning ALS-based assessments of logging recoveries","volume":"12","author":"Sanz","year":"2021","journal-title":"Forests"},{"issue":"7","key":"10.1016\/j.ecoinf.2026.103824_bb0260","doi-asserted-by":"crossref","DOI":"10.3390\/rs11070797","article-title":"Predicting forest inventory attributes using airborne laser scanning, aerial imagery, and harvester data","volume":"11","author":"Saukkola","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0265","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ecolmodel.2019.06.002","article-title":"Hyperparameter tuning and performance assessment of statistical and ma chine-learning algorithms using spatial data","volume":"406","author":"Schratz","year":"2019","journal-title":"Ecol. Model."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2020.103380","article-title":"A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines","volume":"125","author":"Schwendemann","year":"2021","journal-title":"Comput. Ind."},{"key":"10.1016\/j.ecoinf.2026.103824_bib362","series-title":"A Mathematical Theory of Evidence","author":"Shafer","year":"1976"},{"issue":"17","key":"10.1016\/j.ecoinf.2026.103824_bb0275","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1093\/bioinformatics\/btaa542","article-title":"TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction","volume":"36","author":"Sharma","year":"2020","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0280","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1080\/02827581.2021.1919751","article-title":"Operational prediction of forest attributes using standardised harvester data and airborne laser scanning data in Sweden","volume":"36","author":"S\u00f6derberg","year":"2021","journal-title":"Scand. J. For. Res."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0285","series-title":"Machine Learning with Python and H2O","author":"Stetsenko","year":"2016"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0290","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TII.2020.2969709","article-title":"Deep learning for industrial KPI prediction: when ensemble learning meets semi-supervised data","volume":"17","author":"Sun","year":"2021","journal-title":"IEEE Trans. Industr. Inform."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0295","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1109\/TPWRS.2019.2963109","article-title":"Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning","volume":"35","author":"Tan","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103824_bb0300","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1038\/s41597-021-00973-0","article-title":"Gridded daily weather data for North America with comprehensive uncertainty quantification","volume":"8","author":"Thornton","year":"2021","journal-title":"Sci. Data"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.jocs.2021.101517","article-title":"Above-ground biomass estimation from LiDAR data using random forest algorithms","volume":"58","author":"Torre-Tojal","year":"2022","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.tfp.2025.100811","article-title":"Estimating timber assortment reduction and sawlog proportions with the application of harvester measurements and open big geodata","volume":"20","author":"V\u00e4h\u00e4-Konka","year":"2025","journal-title":"Trees, Forests and People"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0315","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhou, X., Zhu, X., Dong, Z., & Guo, W. 2016. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data Crop J., 4(3), 212\u2013219. Doi: https:\/\/doi.org\/10.1016\/j.cj.2016.01.008Doi: https:\/\/doi.org\/10.1016\/j.cj.2016.01.008.","DOI":"10.1016\/j.cj.2016.01.008"},{"key":"10.1016\/j.ecoinf.2026.103824_bb0320","article-title":"Adaptive stratified frameworks for heterogeneous environmental data fusion","volume":"144","author":"Wang","year":"2021","journal-title":"Environ. Model Softw."},{"issue":"06","key":"10.1016\/j.ecoinf.2026.103824_bb0325","doi-asserted-by":"crossref","first-page":"722","DOI":"10.5558\/tfc2013-132","article-title":"A best practice guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach","volume":"89","author":"White","year":"2013","journal-title":"For. Chron."},{"issue":"5","key":"10.1016\/j.ecoinf.2026.103824_bb0330","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote sensing Technologies for Enhancing Forest Inventories: a review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote. Sens."},{"issue":"3","key":"10.1016\/j.ecoinf.2026.103824_bb0335","doi-asserted-by":"crossref","DOI":"10.1117\/1.JRS.10.035010","article-title":"Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery","volume":"10","author":"Wu","year":"2016","journal-title":"J. Appl. Remote. Sens."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0340","article-title":"Multi-source data fusion for ecosystem monitoring: methods and applications","volume":"153","author":"Xu","year":"2023","journal-title":"Ecol. Indic."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0345","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2018.05.014","article-title":"WPD and DE\/BBO-RBFNN for solution of rolling bearing fault diagnosis","volume":"312","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"issue":"24","key":"10.1016\/j.ecoinf.2026.103824_bb0350","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products","volume":"12","author":"Zhang","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.ecoinf.2026.103824_bb0355","article-title":"Fusing LiDAR and optical remote sensing data using deep neural ensembles for aboveground biomass estimation","volume":"70","author":"Zhang","year":"2022","journal-title":"Eco. Inform."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103824_bb0360","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: a comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Magazine"}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S157495412600230X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S157495412600230X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:22:47Z","timestamp":1783092167000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S157495412600230X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":73,"alternative-id":["S157495412600230X"],"URL":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103824","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103824","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103824"}}