Agriculture land appraisal with use of remote sensing and infrastructure data

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dc.contributor.authorKussul, Nataliia
dc.contributor.authorShelestov, Andrii
dc.contributor.authorYailymova, Hanna
dc.contributor.authorShumilo, Leonid
dc.contributor.authorDrozd, Sophia
dc.date.accessioned2022-10-19T13:57:59Z
dc.date.available2022-10-19T13:57:59Z
dc.date.issued2022
dc.description.abstracten1st July 2021 the law on the creation of land market start effect in Ukraine. As a result, land appraisal became cornerstone task in Ukrainian agriculture sector. The official methodology on land appraisal includes use of soil fertility characteristics combined with coefficients related to the distance to the infrastructure objects or settlements and placing of field in specific functional areas, like recreational, or areas with high level of radiation pollution. In this study we collected open source infostructure geospatial information and characteristics of fields obtained from remote sensing data - crop types and Normalized Difference Vegetation Index to build land price predictive model trained on the official land market information. This work designed to investigate potential of geo-informational technologies and remote sensing in the land appraisal use. We separated all available ground truth land price data into three groups by fields size - very small, small, medium and big. We found different relationships between field characteristics and prices. For very small fields the most important features are area, altitude, slope, bonitet and distances to elevators, villages and roads. For small fields the most important are bonitet, altitude, area and distances to cities and roads. For medium and big field's area, slope, distance to cities, roads and historical NDVI.uk
dc.format.pagerangeP. 2785-2788uk
dc.identifier.citationAgriculture Land Appraisal with Use of Remote Sensing and Infrastructure Data / Nataliia Kussul, Andrii Shelestov, Hanna Yailymova, Leonid Shumilo, Sophia Drozd // IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July, 2022, Kuala Lumpur, Malaysia : Proceedings. - Kuala Lumpur: IEEE, 2022. - P. 2785-2788.uk
dc.identifier.doihttps://doi.org/10.1109/IGARSS46834.2022.9884045
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/50428
dc.language.isoenuk
dc.publisherIEEEuk
dc.publisher.placeKuala Lumpuruk
dc.sourceIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July, 2022, Kuala Lumpur, Malaysia : Proceedingsuk
dc.subjectdeep learninguk
dc.subjectGenerative Adversarial Networksuk
dc.subjectsuper-resolutionuk
dc.subjectSentinel-2uk
dc.titleAgriculture land appraisal with use of remote sensing and infrastructure datauk
dc.typeArticleuk

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