scholarly journals Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Tomáš Řezník ◽  
Jan Chytrý ◽  
Kateřina Trojanová
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Training Data ◽  
External Information ◽  
Land Use Land Cover ◽  
Proof Of Concept ◽  
Area Of Interest ◽  
Open Land ◽  
Sentinel 2

Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

scholarly journals Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

2020 ◽  
Vol 12 (20) ◽  
pp. 3428
Author(s):  
Cidália C. Fonte ◽  
Joaquim Patriarca ◽  
Ismael Jesus ◽  
Diogo Duarte
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Software Package ◽  
Random Forest Classifier ◽  
Training Data ◽  
Automatic Extraction ◽  
Land Use Land Cover ◽  
Mixed Pixels ◽  
Different Characteristics ◽  
Sentinel 2

This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image classification. Firstly, OSM data was converted into LULC maps using the OSM2LULC_4T software package. The Random Forest classifier was then trained to classify a time-series of Sentinel-2 imagery into 8 LULC classes with samples extracted from: (1) The LULC maps produced by OSM2LULC_4T (TD0); (2) the TD1 dataset, obtained after removing mixed pixels from TD0; (3) the TD2 dataset, obtained by filtering TD1 using radiometric indices. The classification results were generalized using a majority filter and hybrid maps were created by merging the classification results with the OSM2LULC outputs. The accuracy of all generated maps was assessed using the 2018 official “Carta de Ocupação do Solo” (COS). The methodology was applied to two study areas with different characteristics. The results show that in some cases the filtering procedures improve the training data and the classification results. This automated methodology allowed the production of maps with overall accuracy between 55% and 78% greater than that of COS, even though the used nomenclature includes classes that can be easily confused by the classifiers.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

2020 ◽  
Vol 12 (7) ◽  
pp. 1135 ◽  
Author(s):  
Swapan Talukdar ◽  
Pankaj Singha ◽  
Susanta Mahato ◽  
Shahfahad ◽  
Swades Pal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Learning Algorithms ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Land Use Land Cover ◽  
Accuracy Level ◽  
Parametric Classifier

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

scholarly journals Quantitative Analysis of Different Environmental Factor Impacts on Land Cover in Nisos Elafonisos, Crete, Greece

2020 ◽  
Vol 17 (18) ◽  
pp. 6437
Author(s):  
Mohamed Elhag ◽  
Silvena Boteva
Keyword(s):  
Land Use ◽  
Environmental Factors ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Mediterranean Ecosystems ◽  
Land Use Land Cover ◽  
Routine Work ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Land Cover monitoring is an essential task for a better understanding of the ecosystem’s dynamicity and complexity. The availability of Remote Sensing data improved the Land Use Land Cover mapping as it is routine work in ecosystem management. The complexity of the Mediterranean ecosystems involves a complexity of the surrounding environmental factors. An attempt to quantitatively investigate the interdependencies between land covers and affected environmental factors was conducted in Nisos Elafonisos to represent diverse and fragile coastal Mediterranean ecosystems. Sentinel-2 (MSI) sensor and ASTER Digital Elevation Model (DEM) data were used to classify the LULC as well as to draw different vegetation conditions over the designated study area. DEM derivatives were conducted and incorporated. The developed methodology is intended to assess the land use land cover for different practices under the present environmental condition of Nisos Elafonisos. Supervised classification resulted in six different land cover clusters and was tested against three different environmental clusters. The findings of the current research pointed out that the environmental variables are independent and there is a vertical distribution of the vegetation according to altitude.

Global land use / land cover with Sentinel 2 and deep learning

2021 ◽  
Author(s):  
Krishna Karra ◽  
Caitlin Kontgis ◽  
Zoe Statman-Weil ◽  
Joseph C. Mazzariello ◽  
Mark Mathis ◽  
...  
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Global Land ◽  
Sentinel 2
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