Optimising Process Automation of Geospatial Data Pipelines by Artificial Intelligence
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866902299341750272 |
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| author | Lemenkova, Polina |
| author_facet | Lemenkova, Polina |
| contents | <p>This study aims at using advanced GeoAI tools for monitoring landscapes in Italy using machine learning (ML) methods for remote sensing (RS) data processing. Changes in land cover types were identified using AI-processed satellite images. The methodology is based on the four ML algorithms of Python library Scikit-Learn embedded in the GRASS GIS: SupportVectorMachine (SVM), Decision Tree Classifier (DTC), RandomForest (RF) and Multilayer Perceptron Classifier (MLPC) of Artificial Neural Network (ANN). The multispectral satellite Landsat imagery was processed and analysed for changes in categories. The workflow of image processing includes classification for automatic detection of land categories. The presented maps demonstrated spatio-temporal vegetation dynamics and changes in land cover types detected using times series of the RS data. The topology of patches was detected by ML considering differences among spectral reflectance of pixels. ML algorithms recognised. Streamlined workflow through integration of RS and ML algorithms for model training, prediction and classification in GRASS GIS environment. This study has shown the advantages of AI methods for automation of RS data processing.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18058578 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Optimising Process Automation of Geospatial Data Pipelines by Artificial Intelligence Lemenkova, Polina Geography Geography Physical geography Geography/classification Remote sensing Remote sensing Remote Sensing Technology Remote Sensing Technology/methods Satellite image Satellite Imagery Data analysis Data Analysis Machine learning Machine Learning Supervised Machine Learning Machine Learning/classification <p>This study aims at using advanced GeoAI tools for monitoring landscapes in Italy using machine learning (ML) methods for remote sensing (RS) data processing. Changes in land cover types were identified using AI-processed satellite images. The methodology is based on the four ML algorithms of Python library Scikit-Learn embedded in the GRASS GIS: SupportVectorMachine (SVM), Decision Tree Classifier (DTC), RandomForest (RF) and Multilayer Perceptron Classifier (MLPC) of Artificial Neural Network (ANN). The multispectral satellite Landsat imagery was processed and analysed for changes in categories. The workflow of image processing includes classification for automatic detection of land categories. The presented maps demonstrated spatio-temporal vegetation dynamics and changes in land cover types detected using times series of the RS data. The topology of patches was detected by ML considering differences among spectral reflectance of pixels. ML algorithms recognised. Streamlined workflow through integration of RS and ML algorithms for model training, prediction and classification in GRASS GIS environment. This study has shown the advantages of AI methods for automation of RS data processing.</p> |
| title | Optimising Process Automation of Geospatial Data Pipelines by Artificial Intelligence |
| topic | Geography Geography Physical geography Geography/classification Remote sensing Remote sensing Remote Sensing Technology Remote Sensing Technology/methods Satellite image Satellite Imagery Data analysis Data Analysis Machine learning Machine Learning Supervised Machine Learning Machine Learning/classification |
| url | https://doi.org/10.5281/zenodo.18058578 |