Assessing the transferability of a multi-source land use classification workflow across two heterogeneous urban and rural areas

Abstract

Mapping Land Use (LU) is crucial for monitoring and managing the dynamic evolution of the human activities of a given area and their consequential environmental impacts. In this study, a multimodal machine learning framework, using the XGBoost classifier applied to attributes constructed from heterogeneous spatial data sources, is defined and used to automatically classify LU in the two French departments of Gers and Rhône. It reaches a mean F1 score of 83 and 86% respectively. This research work also assesses the robustness and transferability of the machine learning model between these two diverse study areas and highlights the challenges encountered, arising mainly from the differences of distribution of the attributes and classes between the study areas. Adding a few samples from the test study area allows the model to learn some specificities of the test study area, and thus improves the results. Moreover, the study evaluates the individual contributions of each data source to the accuracy of predictions of the LU classes, providing insights concerning the relevance of each data source in enhancing the overall precision of the Land Use classification. The findings contribute to a validated LU classification workflow, identify valuable data sources, and enhance understanding of model transferability challenges.

Keywords

• Land use classification
• transferability
• data-fusion
• machine learning
• LULC