{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T18:05:49Z","timestamp":1779386749376,"version":"3.53.1"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu University Senior Talent Fund","award":["19JDG004"],"award-info":[{"award-number":["19JDG004"]}]},{"name":"Talent of the \u201cDouble-Entrepreneurial Plan\u201d in Jiangsu Province","award":["19JDG004"],"award-info":[{"award-number":["19JDG004"]}]},{"name":"National Research Foundation of Korea","award":["19JDG004"],"award-info":[{"award-number":["19JDG004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Invasive species negatively affect ecosystems, economies, and human health by outcompeting native species and altering habitats. Ailanthus altissima, also known as the tree of heaven, an invasive species native to China that has spread to North America and Europe. Commonly found in urban areas and forestland, these invasive plants cause ecological and economic damage to local ecosystems; they are also the preferred host of other invasive species. Ecological stability refers to the balance and harmony in species populations. Invasive species like A. altissima disrupt this stability by outcompeting native species, leading to imbalances, and there was a lack of research and data on the tree of heaven. To address this issue, this study leveraged deep learning and satellite imagery recognition to generate reliable and comprehensive prediction maps in the USA. Four deep learning models were trained to recognize satellite images obtained from Google Earth, with A. altissima data obtained from the Life Alta Murgia project, LIFE12 BIO\/IT\/000213. The best performing fine-tuned model using binary classification achieved an AUC score of 90%. This model was saved locally and used to predict the density and probability of A. altissima in the USA. Additionally, multi-class classification methods corroborated the findings, demonstrating similar observational outcomes. The production of these predictive distribution maps is a novel method which offers an innovative and cost-effective alternative for extensive field surveys, providing reliable data for concurrent and future research on the environmental impact of A. altissima.<\/jats:p>","DOI":"10.3390\/sym17030324","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T09:26:32Z","timestamp":1740129992000},"page":"324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting the Distribution of Ailanthus altissima Using Deep Learning-Based Analysis of Satellite Imagery"],"prefix":"10.3390","volume":"17","author":[{"given":"Ruohan","family":"Gao","sequence":"first","affiliation":[{"name":"Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA"},{"name":"Harvard Medical School, Harvard University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zipeng","family":"Song","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Landscape Architecture, Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Harvard University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6972-6190","authenticated-orcid":false,"given":"Yingnan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Environmental Design, Jiangsu University, Zhenjiang 212013, China"},{"name":"OJeong Resilience Institute, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev-environ-033009-095548","article-title":"Invasive Species, Environmental Change and Management, and Health","volume":"35","author":"Richardson","year":"2010","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_2","unstructured":"(2023, July 27). What Is an Invasive Species and Why Are They a Problem?, Available online: https:\/\/www.usgs.gov\/faqs\/what-invasive-species-and-why-are-they-problem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106020","DOI":"10.1016\/j.ecolind.2019.106020","article-title":"Invasive Alien Plant Species: Their Impact on Environment, Ecosystem Services and Human Health","volume":"111","author":"Singh","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_4","unstructured":"(2023, July 27). Tree of Heaven (Ailanthus altissima): Invasive Species ID. Available online: https:\/\/www.nature.org\/en-us\/about-us\/where-we-work\/united-states\/indiana\/stories-in-indiana\/journey-with-nature--tree-of-heaven\/."},{"key":"ref_5","unstructured":"(2023, July 27). Tree of Heaven, Available online: https:\/\/www.invasivespeciescentre.ca\/invasive-species\/meet-the-species\/invasive-plants\/tree-of-heaven\/."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wolde, B., and Lal, P. (2018). Invasive-Plant-Removal Frequency\u2014Its Impact on Species Spread and Implications for Further Integration of Forest-Management Practices. Forests, 9.","DOI":"10.3390\/f9080502"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10530-015-0999-8","article-title":"Using Forest Management to Control Invasive Alien Species: Helping Implement the New European Regulation on Invasive Alien Species","volume":"18","author":"Sitzia","year":"2016","journal-title":"Biol. Invasions"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13750-020-0186-y","article-title":"Management of UK Priority Invasive Alien Plants: A Systematic Review Protocol","volume":"9","author":"Martin","year":"2020","journal-title":"Environ. Evid."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Warzinaick, T., Haight, R.G., Yemshanov, D., Apriesnig, J.L., Holmes, T.P., Countryman, A.M., Rothlisberger, J.D., and Haberland, C. (2021). Economics of Invasive Species. Invasive Species in Forests and Rangelands of the United States, Springer.","DOI":"10.1007\/978-3-030-45367-1_14"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"485","DOI":"10.3897\/neobiota.67.58038","article-title":"Economic Costs of Biological Invasions within North America","volume":"67","author":"Hudgins","year":"2021","journal-title":"NeoBiota"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3897\/neobiota.67.69971","article-title":"The Economic Costs of Biological Invasions around the World","volume":"67","author":"Zenni","year":"2021","journal-title":"NeoBiota"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, X., Kuang, Y., Chen, Y.V., and Yang, B. (2018, January 8\u201321). Deep CNN-Based Methods to Evaluate Neighborhood-Scale Urban Valuation Through Street Scenes Perception. Proceedings of the 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, China.","DOI":"10.1109\/DSC.2018.00012"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bibault, J.-E., Bassenne, M., Ren, H., and Xing, L. (2020). Deep Learning Prediction of Cancer Prevalence from Satellite Imagery. Cancers, 12.","DOI":"10.3390\/cancers12123844"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2018.11.013","article-title":"Ailanthus Altissima Mapping from Multi-Temporal Very High Resolution Satellite Images","volume":"147","author":"Tarantino","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Niphadkar, M., Nagendra, H., Tarantino, C., Adamo, M., and Blonda, P. (2017). Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests. Front. Plant Sci., 8.","DOI":"10.3389\/fpls.2017.00892"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1002\/rse2.288","article-title":"Deep Learning Detects Invasive Plant Species across Complex Landscapes Using Worldview-2 and Planetscope Satellite Imagery","volume":"8","author":"Lake","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_17","first-page":"276","article-title":"Automatic Detection of Acacia Longifolia Invasive Species Based on UAV-Acquired Aerial Imagery","volume":"9","author":"Santana","year":"2022","journal-title":"Inf. Process. Agric."},{"key":"ref_18","first-page":"1068","article-title":"Aerial Detection of Seed-Bearing Female Ailanthus altissima: A Cost-Effective Method to Map an Invasive Tree in Forested Landscapes","volume":"61","author":"Rebbeck","year":"2015","journal-title":"For. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/TVCG.2019.2945960","article-title":"Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions","volume":"27","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ppees.2007.03.002","article-title":"Biological Flora of Central Europe: Ailanthus altissima (Mill.) Swingle","volume":"8","author":"Kowarik","year":"2007","journal-title":"Perspect. Plant Ecol. Evol. Syst."},{"key":"ref_21","unstructured":"(2023, August 04). Tree-of-Heaven. Available online: https:\/\/extension.psu.edu\/tree-of-heaven."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/0964-8305(94)90011-6","article-title":"The Weathering Ability of Higher Plants. The Case of Ailanthus altissima (Miller) Swingle","volume":"33","author":"Almeida","year":"1994","journal-title":"Int. Biodeterior. Biodegrad."},{"key":"ref_23","unstructured":"(2023, July 27). MDAR Invasive Pest Dashboard. Available online: https:\/\/experience.arcgis.com\/experience\/a25afa4466a54313b21dd45abc34b62d\/page\/Page-2\/?views=Spotted-Lanternfly."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez Valdivia, A., De Stefano, L.G., Ferraro, G., Gianello, D., Ferral, A., Dogliotti, A.I., Reissig, M., Gerea, M., Queimali\u00f1os, C., and P\u00e9rez, G.L. (2024). Characterizing Chromophoric Dissolved Organic Matter Spatio-Temporal Variability in North Andean Patagonian Lakes Using Remote Sensing Information and Environmental Analysis. Remote Sens., 16.","DOI":"10.3390\/rs16214063"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Genzano, N., Pergola, N., and Marchese, F. (2020). A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12193232"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rashidian, V., Baise, L.G., Koch, M., and Moaveni, B. (2021). Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13112176"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_28","unstructured":"(2023, July 27). Using Spatial Simulations of Habitat Modification for Adaptive Management of Protected Areas: Mediterranean Grassland Modification by Woody Plant Encroachment. Environmental Conservation. Cambridge Core. Available online: https:\/\/www.cambridge.org\/core\/journals\/environmental-conservation\/article\/abs\/using-spatial-simulations-of-habitat-modification-for-adaptive-management-of-protected-areas-mediterranean-grassland-modification-by-woody-plant-encroachment\/0EDCEADF910352D9370FEE463C795B0A."},{"key":"ref_29","unstructured":"(2023, July 27). LIFE 3.0\u2014LIFE Project Public Page. Available online: https:\/\/webgate.ec.europa.eu\/life\/publicWebsite\/index.cfm?fuseaction=search.dspPage&n_proj_id=4566#administrative-data."},{"key":"ref_30","unstructured":"(2023, July 27). Tree-of-Heaven (Ailanthus altissima)\u2014EDDMapS Distribution\u2014EDDMapS. Available online: https:\/\/www.eddmaps.org\/distribution\/uscounty.cfm?sub=3003&map=distribution."},{"key":"ref_31","unstructured":"(2023, July 27). Spotted Lanternfly (Lycorma delicatula)\u2014EDDMapS Distribution\u2014EDDMapS. Available online: https:\/\/www.eddmaps.org\/distribution\/uscounty.cfm?sub=77293."},{"key":"ref_32","unstructured":"(2023, July 27). Google Earth. Available online: https:\/\/earth.google.com\/web\/."},{"key":"ref_33","unstructured":"NLCD 2021 USFS Tree Canopy Cover (CONUS) (2023, July 27). NLCD 2021 USFS Tree Canopy Cover (CONUS)|Multi-Resolution Land Characteristics (MRLC) Consortium. (n.d.), Available online: https:\/\/www.mrlc.gov\/data\/nlcd-2021-usfs-tree-canopy-cover-conus."},{"key":"ref_34","unstructured":"(2023, October 30). Municipal Boundaries of New Jersey, Web Mercator. Available online: https:\/\/undefined.maps.arcgis.com\/sharing\/rest\/content\/items\/a1b13541f0484415b06cf9c8969bfd6c\/info\/metadata\/metadata.xml?format=default&output=html."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","unstructured":"Tan, M., and Le, Q.V. (2021). EfficientNetV2: Smaller Models and Faster Training. arXiv."},{"key":"ref_37","unstructured":"Sarkar, A. (2023, July 27). EfficientNetV2\u2014Faster, Smaller, and Higher Accuracy than Vision Transformers. Available online: https:\/\/medium.com\/towards-data-science\/efficientnetv2-faster-smaller-and-higher-accuracy-than-vision-transformers-98e23587bf04."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in Vision: A Survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_39","unstructured":"Radhakrishnan, P. (2023, July 27). Why Transformers Are Slowly Replacing CNNs in Computer Vision?. Available online: https:\/\/becominghuman.ai\/transformers-in-vision-e2e87b739feb."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gao, R. (2022, January 22\u201324). Determining Critical Lung Cancer Subtypes from Gigapixel Multi-Scale Whole Slide H&E Stains Images. Proceedings of the 2022 5th International Conference on Data Science and Information Technology (DSIT), Shanghai, China.","DOI":"10.1109\/DSIT55514.2022.9943912"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, X., Tang, H.P., Wang, X.Y., Yang, S., Liu, D.F., Chen, Y.J., and Chen, Y.V. (2024). Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings. Comput. Biol. Med., 182.","DOI":"10.1016\/j.compbiomed.2024.109105"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., and Maglogiannis, I. (2018). Further Advantages of Data Augmentation on Convolutional Neural Networks. Artificial Neural Networks and Machine Learning\u2014ICANN 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01418-6"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data Augmentation for Improving Deep Learning in Image Classification Problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_45","unstructured":"(2023, July 27). ImageNet. Available online: https:\/\/www.image-net.org\/update-mar-11-2021.php."},{"key":"ref_46","unstructured":"(2023, July 27). (PDF) Deep Learning on Private Data. Available online: https:\/\/www.researchgate.net\/publication\/330842645_Deep_Learning_on_Private_Data."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Maur\u00edcio, J., Domingues, I., and Bernardino, J. (2023). Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review. Appl. Sci., 13.","DOI":"10.3390\/app13095521"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"012028","DOI":"10.1088\/1742-6596\/1947\/1\/012028","article-title":"A Pre-Trained Vs Fine-Tuning Methodology in Transfer Learning","volume":"1947","author":"Gupta","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_49","unstructured":"USDA APHIS (2023, July 27). Spotted Lanternfly, Available online: https:\/\/www.aphis.usda.gov\/plant-pests-diseases\/slf."},{"key":"ref_50","unstructured":"(2023, July 27). Homeowner Resources, Available online: https:\/\/www.nj.gov\/agriculture\/divisions\/pi\/prog\/pests-diseases\/spotted-lanternfly\/homeowner-resources\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6537","DOI":"10.1021\/acs.jcim.3c01563","article-title":"AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks","volume":"63","author":"Raza","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Akbar, S., Raza, A., and Zou, Q. (2024). Deepstacked-AVPs: Predicting Antiviral Peptides Using Tri-Segment Evolutionary Profile and Word Embedding Based Multi-Perspective Features with Deep Stacking Model. BMC Bioinform., 25.","DOI":"10.1186\/s12859-024-05726-5"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102860","DOI":"10.1016\/j.artmed.2024.102860","article-title":"iAFPs-Mv-BiTCN: Predicting Antifungal Peptides Using Self-Attention Transformer Embedding and Transform Evolutionary Based Multi-View Features with Bidirectional Temporal Convolutional Networks","volume":"151","author":"Akbar","year":"2024","journal-title":"Artif. Intell. Med."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/324\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:39:51Z","timestamp":1760027991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,21]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030324"],"URL":"https:\/\/doi.org\/10.3390\/sym17030324","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,21]]}}}