Remote Sensing, Vol. 18, Pages 114: Land Cover Type Classification Using High-Resolution Orthophotomaps and Convolutional Neural Networks: Case Study of Tatra National Park

Remote Sensing doi: 10.3390/rs18010114

Authors:
Edwin Raczko
Marlena Kycko
Marcin Kluczek

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.

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Edwin Raczko www.mdpi.com