Filtering Lpis Data for Building Trustworthy Training Datasets for Crop Type Mapping: A Case Study in Greece

Olympia, Gounari,; Karakizi, C.; Καράντζαλος, Κωνσταντίνος

doi:10.5194/isprs-archives-xliii-b3-2022-871-2022

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…One approach to actually validate the original data on a bigger scale was introduced by Gounari et al . 19 , but this would exceed the project tasks. On our side, we concentrated on a valid harmonisation of the entire dataset.…”

Section: Technical Validationmentioning

confidence: 98%

EuroCrops: The Largest Harmonized Open Crop Dataset Across the European Union

Schneider,

Schelte,

Schmitz

et al. 2023

Sci Data

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EuroCrops contains geo-referenced polygons of agricultural croplands from 16 countries of the European Union (EU) as well as information on the respective crop species grown there. These semantic annotations are derived from self-declarations by farmers receiving subsidies under the common agricultural policy (CAP) of the European Commission (EC). Over the last 1.5 years, the individual national crop datasets have been manually collected, the crop classes have been translated into the English language and transferred into the newly developed hierarchical crop and agriculture taxonomy (HCAT). EuroCrops is publicly available under continuous improvement through an active user community.

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Section: Technical Validationmentioning

confidence: 98%

EuroCrops: The Largest Harmonized Open Crop Dataset Across the European Union

Schneider,

Schelte,

Schmitz

et al. 2023

Sci Data

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“…Our study advances the field of remote sensing data mapping by employing an extended classification scheme in a complex, heterogeneous area, focusing on multiple crop types. To address known accuracy and reliability errors in reference data coming from farmers' declarations [22,[46][47][48] in our study, we relied only on recently verified ground reference data from the state agency managing such databases. The reference samples were used with optimized deep learning models to generate reliable and concrete findings.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

Papadopoulou,

Mallinis,

Siachalou

et al. 2023

Remote Sensing

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The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

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