This study explores the prediction of changes in Land Use and Land Cover (LULC) over the period of 2000 to 2040 in a region of Algeria. The research combines remote sensing data and machine learning techniques. Landsat image classification is applied to assess LULC changes for the years 2000 to 2020. In addition, a novel approach is employed to account for temporal factors influencing LULC changes in 2025, 2030, and 2040. Promising results are reported and discussed.
Machine Learning Approach to LULC Forecasting / Riche, A.; Ricci, R.; Melgani, F.; Drias, A.; Souissi, B.. - (2024), pp. 59-63. (Intervento presentato al convegno 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024 tenutosi a dza nel 2024) [10.1109/M2GARSS57310.2024.10537270].
Machine Learning Approach to LULC Forecasting
Ricci R.;Melgani F.;Souissi B.
2024-01-01
Abstract
This study explores the prediction of changes in Land Use and Land Cover (LULC) over the period of 2000 to 2040 in a region of Algeria. The research combines remote sensing data and machine learning techniques. Landsat image classification is applied to assess LULC changes for the years 2000 to 2020. In addition, a novel approach is employed to account for temporal factors influencing LULC changes in 2025, 2030, and 2040. Promising results are reported and discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
