TY - JOUR

T1 - Optimising Process Automation of Geospatial Data Pipelines by Artificial Intelligence

AU - Леменкова, Полина Алексеевна

PY - 2025/12/25

Y1 - 2025/12/25

N2 - 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.

AB - 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.

KW - GIS

KW - remote sensing

KW - image processing

KW - data analysis

KW - machine learning

U2 - 10.5281/zenodo.18058578

DO - 10.5281/zenodo.18058578

M3 - Article

VL - 24

SP - 147

EP - 166

JO - Facta Universitatis. Series: Automatic Control and Robotics

JF - Facta Universitatis. Series: Automatic Control and Robotics

SN - 1820-6417

IS - 2

ER -