Land Cover Type Classification Using High-Resolution Orthophotomaps and Convolutional Neural Networks: Case Study of Tatra National Park.
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| Title: | Land Cover Type Classification Using High-Resolution Orthophotomaps and Convolutional Neural Networks: Case Study of Tatra National Park. |
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| Authors: | Raczko, Edwin1 (AUTHOR) edwin.raczko@uw.edu.pl, Kycko, Marlena1 (AUTHOR), Kluczek, Marcin1 (AUTHOR) |
| Source: | Remote Sensing. Jan2026, Vol. 18 Issue 1, p114. 25p. |
| Subjects: | Convolutional neural networks, Vegetation classification, Statistical reliability, Geospatial data, National parks & reserves, Environmental management, Machine learning, Plant identification |
| Abstract: | Highlights: What are the main findings? CNNs can be successfully applied to mapping diverse and numerous classes (18 classes) over a spatially large mountainous protected region using high-resolution (0.12-m) orthophotomaps. The mapping accuracies are exceedingly high for typical land cover classes but are not satisfactory for more sophisticated classes, such as plant species. Ordinary orthophotomaps are a suitable substrate for land cover type mapping but might not be sufficient for delimiting complex classes, such as plant species or plant habitats. What are the implications of the main findings? Orthophotomaps combined with CNNs could be a sufficient data source to perform mapping of numerous land cover types, resulting in maps with a superior spatial resolution. The use of high-resolution orthophotomaps and CNNs for mapping plant species, habitats, or otherwise complex classes should be investigated further. Land cover mapping delivers crucial information for land and environmental management stakeholders. This work investigated the use of high-resolution RGB orthophotomaps for land cover mapping in the mountainous protected area of Tatra National Park. While a typical orthophotomap has very high spatial resolution, it also lacks multiple spectral bands (especially in the NIR-SWIR region), which makes them ill-suited as input for more classical image classification approaches. With widespread access to sophisticated machine learning algorithms and paradigms such as convolutional neural networks (CNNs), their use for land cover mapping can be investigated using very-high-resolution orthophotomaps. In this work, we investigated the use of CNNs for mapping land cover types of Tatra National Park using orthophotomaps with a spatial resolution of 0.12 m. The overall accuracies varied from 86% to 92% depending on the classification variant. Most classes had high accuracies (with an F1-score above 0.90), but more complex classes, such as plant species, were identified with F1-scores between 0.32 and 0.55. The application of CNNs in land cover mapping represents a significant advancement, greatly enhancing the effectiveness and precision of the mapping process. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? CNNs can be successfully applied to mapping diverse and numerous classes (18 classes) over a spatially large mountainous protected region using high-resolution (0.12-m) orthophotomaps. The mapping accuracies are exceedingly high for typical land cover classes but are not satisfactory for more sophisticated classes, such as plant species. Ordinary orthophotomaps are a suitable substrate for land cover type mapping but might not be sufficient for delimiting complex classes, such as plant species or plant habitats. What are the implications of the main findings? Orthophotomaps combined with CNNs could be a sufficient data source to perform mapping of numerous land cover types, resulting in maps with a superior spatial resolution. The use of high-resolution orthophotomaps and CNNs for mapping plant species, habitats, or otherwise complex classes should be investigated further. Land cover mapping delivers crucial information for land and environmental management stakeholders. This work investigated the use of high-resolution RGB orthophotomaps for land cover mapping in the mountainous protected area of Tatra National Park. While a typical orthophotomap has very high spatial resolution, it also lacks multiple spectral bands (especially in the NIR-SWIR region), which makes them ill-suited as input for more classical image classification approaches. With widespread access to sophisticated machine learning algorithms and paradigms such as convolutional neural networks (CNNs), their use for land cover mapping can be investigated using very-high-resolution orthophotomaps. In this work, we investigated the use of CNNs for mapping land cover types of Tatra National Park using orthophotomaps with a spatial resolution of 0.12 m. The overall accuracies varied from 86% to 92% depending on the classification variant. Most classes had high accuracies (with an F1-score above 0.90), but more complex classes, such as plant species, were identified with F1-scores between 0.32 and 0.55. The application of CNNs in land cover mapping represents a significant advancement, greatly enhancing the effectiveness and precision of the mapping process. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18010114 |
