Keywords
artificial intelligence
neural networks
GIS technologies
cloud services
data verification
How to Cite
Abstract
Abstract. This article examines the issue of modernising the training of future ecologists through the implementation of mobile information and communication technologies (ICT) in the practical work of surveying urban green spaces. It is demonstrated that the use of specialised software enables the key task of automating field data collection to be resolved, contributes to improving the accuracy of taxonomic identification, and facilitates the creation of verified geospatial databases of tree vegetation.
As part of the pedagogical experiment conducted (in Kamyanske), the specific content of the students’ activities was defined, which included photographing the morphological characteristics of trees, their automated recognition using the neural network algorithms of the Pl@ntNet application, precise georeferencing of coordinates via the Trees: Forest Monitoring Tools, and the compilation of digital maps of inventory objects. Particular attention was paid to the implementation of a multi-level verification algorithm (algorithmic, platform-based, expert and taxonomic), which enabled students to critically evaluate the results of artificial intelligence and minimise identification errors.
The research methodology is based on a comprehensive approach combining open science methods and mathematical-statistical processing of results. The statistical significance of the growth in students’ professional competencies has been demonstrated (according to Wilcoxon’s T-test, p < 0,01).
The results of the pilot study confirmed that the use of mobile GIS ensures an identification accuracy of 94,3 % and increases students’ motivation for professional activity to 85 %. The practical significance of this work lies in the development of a methodology that integrates technical solutions with educational objectives, preparing future specialists to work in the context of the digital transformation of environmental monitoring.
References
[1] F. Ullah et al., “A scoping review of green Internet of Things in construction and smart cities: current applications, adoption strategies and future directions”, Smart Sustain. Built Environ., vol. 15, no. 1, pp. 294–318, 2024. doi: 10.1108/SASBE-11-2023-0349.
[2] World Health Organization, “Urban green space interventions and health: a review of impacts and effectiveness”, WHO Regional Office for Europe, 2017. [Електронний ресурс]. Доступно: https://iris.who.int/server/api/core/bitstreams/9c952dde-1249-4a94-ae39-4e594c76e2bf/content.
[3] Стратегія біорізноманіття ЄС до 2030 року: Повернення природи у наше життя, пер. з англ. О. Осипенко, ред. А. Куземко та ін. Чернівці: Друк Арт, 2020. [Електронний ресурс]. Доступно: https://uncg.org.ua/wp-content/uploads/2020/10/Stratehiia.pdf.
[4] Інструкція з інвентаризації зелених насаджень у населених пунктах України, Наказ Держбуду України № 226 від 24 груд. 2001. [Електронний ресурс]. Доступно: http://zakon1.rada.gov.ua/laws/show/z0182-02.
[5] Методичні рекомендації щодо обліку зелених насаджень у населених пунктах України, Наказ Мінбуду України № 386 від 22 листоп. 2006. [Електронний ресурс]. Доступно: https://ips.ligazakon.net/document/fin24601.
[6] Міністерство захисту довкілля та природних ресурсів України, Звіт про стан виконання у 2024 році Національного плану дій з охорони навколишнього природного середовища на період до 2025 року. Київ, 2024. [Електронний ресурс]. Доступно: https://mepr.gov.ua/wp-content/uploads/2025/03/Zvit-po-vykonannyu-NPD-za-2024.pdf.
[7] X. Li, L. Wang, H. Guan et al., “Urban Tree Species Classification Using UAV-Based Multispectral Images and LiDAR Point Clouds”, J. Geovis. Spat. Anal., vol. 8, no. 5, 2024. doi: 10.1007/s41651-023-00167-9.
[8] MIT Senseable City Lab, “Treepedia Project”, 2022. [Електронний ресурс]. Доступно: https://senseable.mit.edu/treepedia/.
[9] Z. Bilyk et al., “Features of Using Mobile Applications to Identify Plants and Google Lens During the Learning Process”, in Proc. 2nd M. I. Zhaldak Symp. Adv. Educ. Technol. (AET 2021), vol. 1, 2021, pp. 688–705. doi: 10.5220/0012067000003431.
[10] Y. Sun, Y. Liu, G. Wang, and H. Zhang, “Deep Learning for Plant Identification in Natural Environment”, Comput. Intell. Neurosci., vol. 2017, art. no. 7361042, 2017. doi: 10.1155/2017/7361042.
[11] M. X. A. Truong and R. Van der Wal, “Exploring the landscape of automated species identification apps: Development, promise, and user appraisal”, BioScience, vol. 74, no. 9, pp. 601–613, Sep. 2024. doi: 10.1093/biosci/biae077.
[12] M. M. Nowak, K. Dziób, Ł. Ludwisiak, and J. Chmiel, “Mobile GIS applications for environmental field surveys: A state of the art”, Glob. Ecol. Conserv., vol. 23, 2020. doi: 10.1016/j.gecco.2020.e01089.
[13] H. L. Weil et al., “PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research”, Plant J., vol. 116, no. 3, pp. 974–988, 2023. doi: 10.1111/tpj.16474.
[14] Зелені зони міст на варті прохолоди: інвентаризація зелених насаджень як важлива передумова стійкості міста до екстремальної спеки в Україні. Аналітичний огляд. Київ: ГО “Зелена Хвиля”, 2021. [Електронний ресурс]. Доступно: https://ecoclubua.com/wp-content/uploads/Zeleni-zony-na-varti-proholody-2021.pdf.
[15] Моніторинг зелених насаджень міста Одеса, 2020. [Електронний ресурс]. Доступно: https://greencity.omr.gov.ua/.
[16] G. Gagliardi et al., “IoT Monitoring of Urban Tree Ecosystem Services: Possibilities and Challenges”, Forests, vol. 11, no. 7, art. no. 775, 2020. doi: 10.3390/f11070775.
[17] P. Holloway et al., “Smartphone GIS: exploring technological competency in active learning across geography”, J. Geogr. High. Educ., vol. 49, no. 3, pp. 376–397, 2025. doi: 10.1080/03098265.2024.2443908.
[18] S. G. Lytvynova, “Concepts and characteristics of cloud oriented learning environment of school”, Inf. Technol. Learn. Tools, vol. 40, no. 2, pp. 26–41, 2014. doi: 10.33407/itlt.v40i2.970.
[19] V. Y. Bykov and M. P. Shyshkina, “The conceptual basis of the university cloud-based learning and research environment formation and development in view of the open science priorities”, Inf. Technol. Learn. Tools, vol. 68, no. 6, pp. 1–19, 2018. doi: 10.33407/itlt.v68i6.2609.
[20] О. М. Топузов, Л. П. Вішнікіна, В. М. Самойленко та Т. С. Япринець, “Модернізація географічної освіти у вищій школі: геоінформаційні навчальні моделі”, Інформаційні технології і засоби навчання, т. 73, № 5, с. 174–184, 2019. doi: 10.33407/itlt.v73i5.3190.
[21] X. Xia, “Enhancing Environmental Education through Digital Tools”, Int. J. New Dev. Educ., vol. 6, no. 9, pp. 212–216, 2024. doi: 10.25236/IJNDE.2024.060932.
[22] Стандарт вищої освіти України: перший (бакалаврський) рівень, спеціальність 101 – Екологія, Наказ МОН України № 1076 від 04 жовт. 2018. [Електронний ресурс]. Доступно: https://ecology.kntu.kr.ua/file/content/19817/standart-vyshchoi-osvity-za-spetsialnistyu-ekolohiia.pdf.
[23] Pl@ntNet: Citizen Science Project, 2024. [Online]. Available: https://identify.plantnet.org/uk.
[24] Trees: Forest Monitoring Tools, 2024. [Електронний ресурс]. Доступно: https://sites.google.com/view/forestmonitoringtools/home.
[25] “Searching for MobileNetV3”, PyTorch Documentation. [Електронний ресурс]. Доступно: https://docs.pytorch.org/vision/main/models/mobilenetv3.html.
[26] QField for QGIS, 2024. [Online]. Available: https://qfield.org/.
[27] Tracklia: Professional GPS Mapping for Android, 2024. [Електронний ресурс]. Доступно: https://tracklia.app/.
[28] ArcGIS Online, ESRI, 2024. [Електронний ресурс]. Доступно: https://www.arcgis.com/.
[29] L. Velasquez-Camacho et al., “Remotely Sensed Tree Characterization in Urban Areas: A Review”, Remote Sens., vol. 13, no. 23, art. no. 4889, 2021. doi: 10.3390/rs13234889.
REFERENCES (TRANSLATED AND TRANSLITERATED)
[1] F. Ullah et al., “A scoping review of green Internet of Things in construction and smart cities: current applications, adoption strategies and future directions”, Smart Sustain. Built Environ., vol. 15, no. 1, pp. 294–318, 2024. doi: 10.1108/SASBE-11-2023-0349. (in English)
[2] World Health Organization, “Urban green space interventions and health: a review of impacts and effectiveness”, WHO Regional Office for Europe, 2017. [Online]. Available: https://iris.who.int/server/api/core/bitstreams/9c952dde-1249-4a94-ae39-4e594c76e2bf/content. (in English)
[3] EU Biodiversity Strategy for 2030: Bringing nature back into our lives, O. Osypenko, Trans., A. Kuzemko et al., Eds. Chernivtsi, Ukraine: Druk Art, 2020. [Online]. Available: https://uncg.org.ua/wp-content/uploads/2020/10/Stratehiia.pdf. (in Ukrainian)
[4] Instructions for the inventory of green spaces in settlements of Ukraine, Order of the State Construction Committee of Ukraine No. 226, Dec. 24, 2001. [Online]. Available: http://zakon1.rada.gov.ua/laws/show/z0182-02. (in Ukrainian)
[5] Methodological recommendations for the accounting of green spaces in settlements of Ukraine, Order of the Ministry of Construction of Ukraine No. 386, Nov. 22, 2006. [Online]. Available: https://ips.ligazakon.net/document/fin24601. (in Ukrainian)
[6] Ministry of Environmental Protection and Natural Resources of Ukraine, Report on the status of implementation in 2024 of the National Environmental Action Plan for the period up to 2025. Kyiv, Ukraine, 2024. [Online]. Available: https://mepr.gov.ua/wp-content/uploads/2025/03/Zvit-po-vykonannyu-NPD-za-2024.pdf. (in Ukrainian)
[7] X. Li, L. Wang, H. Guan et al., “Urban Tree Species Classification Using UAV-Based Multispectral Images and LiDAR Point Clouds”, J. Geovis. Spat. Anal., vol. 8, no. 5, 2024. doi: 10.1007/s41651-023-00167-9. (in English)
[8] MIT Senseable City Lab, “Treepedia Project”, 2022. [Online]. Available: https://senseable.mit.edu/treepedia/. (in English)
[9] Z. Bilyk et al., “Features of Using Mobile Applications to Identify Plants and Google Lens During the Learning Process”, in Proc. 2nd M. I. Zhaldak Symp. Adv. Educ. Technol. (AET 2021), vol. 1, 2021, pp. 688–705. doi: 10.5220/0012067000003431. (in English)
[10] Y. Sun, Y. Liu, G. Wang, and H. Zhang, “Deep Learning for Plant Identification in Natural Environment”, Comput. Intell. Neurosci., vol. 2017, art. no. 7361042, 2017. doi: 10.1155/2017/7361042. (in English)
[11] M. X. A. Truong and R. Van der Wal, “Exploring the landscape of automated species identification apps: Development, promise, and user appraisal”, BioScience, vol. 74, no. 9, pp. 601–613, Sep. 2024. doi: 10.1093/biosci/biae077. (in English)
[12] M. M. Nowak, K. Dziób, Ł. Ludwisiak, and J. Chmiel, “Mobile GIS applications for environmental field surveys: A state of the art”, Glob. Ecol. Conserv., vol. 23, 2020. doi: 10.1016/j.gecco.2020.e01089. (in English)
[13] H. L. Weil et al., “PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research”, Plant J., vol. 116, no. 3, pp. 974–988, 2023. doi: 10.1111/tpj.16474. (in English)
[14] Urban green zones guarding the coolness: inventory of green spaces as an important prerequisite for city resilience to extreme heat in Ukraine. Kyiv, Ukraine: NGO "Zelena Khvylia", 2021. [Online]. Available: https://ecoclubua.com/wp-content/uploads/Zeleni-zony-na-varti-proholody-2021.pdf. (in Ukrainian)
[15] Monitoring of green spaces in the city of Odesa, 2020. [Online]. Available: https://greencity.omr.gov.ua/. (in Ukrainian)
[16] G. Gagliardi et al., “IoT Monitoring of Urban Tree Ecosystem Services: Possibilities and Challenges”, Forests, vol. 11, no. 7, art. no. 775, 2020. doi: 10.3390/f11070775. (in English)
[17] P. Holloway et al., “Smartphone GIS: exploring technological competency in active learning across geography”, J. Geogr. High. Educ., vol. 49, no. 3, pp. 376–397, 2025. doi: 10.1080/03098265.2024.2443908. (in English)
[18] S. G. Lytvynova, “Concepts and characteristics of cloud oriented learning environment of school”, Inf. Technol. Learn. Tools, vol. 40, no. 2, pp. 26–41, 2014. doi: 10.33407/itlt.v40i2.970. (in English)
[19] V. Y. Bykov and M. P. Shyshkina, “The conceptual basis of the university cloud-based learning and research environment formation and development in view of the open science priorities”, Inf. Technol. Learn. Tools, vol. 68, no. 6, pp. 1–19, 2018. doi: 10.33407/itlt.v68i6.2609. (in English)
[20] O. M. Topuzov, L. P. Vishnikina, V. M. Samoilenko, and T. S. Yaprynets, Modernization of geography education in higher school: geoinformation learning models, Inf. Technol. Learn. Tools, vol. 73, no. 5, pp. 174–184, 2019. doi: 10.33407/itlt.v73i5.3190. (in Ukrainian)
[21] X. Xia, “Enhancing Environmental Education through Digital Tools”, Int. J. New Dev. Educ., vol. 6, no. 9, pp. 212–216, 2024. doi: 10.25236/IJNDE.2024.060932. (in English)
[22] Higher Education Standard of Ukraine: first (Bachelor's) level, specialty 101 – Ecology, Order of the Ministry of Education and Science of Ukraine No. 1076, Oct. 04, 2018. [Online]. Available: https://ecology.kntu.kr.ua/file/content/19817/standart-vyshchoi-osvity-za-spetsialnistyu-ekolohiia.pdf. (in Ukrainian)
[23] Pl@ntNet: Citizen Science Project, 2024. [Online]. Available: https://identify.plantnet.org/uk. (in English)
[24] Trees: Forest Monitoring Tools, 2024. [Online]. Available: https://sites.google.com/view/forestmonitoringtools/home. (in English)
[25] “Searching for MobileNetV3”, PyTorch Documentation. [Online]. Available: https://docs.pytorch.org/vision/main/models/mobilenetv3.html. (in English)
[26] QField for QGIS, 2024. [Online]. Available: https://qfield.org/. (in English)
[27] Tracklia: Professional GPS Mapping for Android, 2024. [Online]. Available: https://tracklia.app/. (in English)
[28] ArcGIS Online, ESRI, 2024. [Online]. Available: https://www.arcgis.com/. (in English)
[29] L. Velasquez-Camacho et al., “Remotely Sensed Tree Characterization in Urban Areas: A Review”, Remote Sens., vol. 13, no. 23, art. no. 4889, 2021. doi: 10.3390/rs13234889. (in English)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2026 Natalia Neposhyvailenko, Oleksandr Huba, Sofiia Mykhalevych