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, Abdelkader; Ricci, Riccardo; Melgani, Farid; Drias, Ammar; Souissi, Boularbah. - (2024), pp. 59-63. ( 2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024 dza 2024) [10.1109/M2GARSS57310.2024.10537270].
Machine Learning Approach to LULC Forecasting
Riccardo Ricci;Farid Melgani;Boularbah Souissi
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.| File | Dimensione | Formato | |
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