A unified framework for land-cover database update and enrichment using satellite imagery

2D land-cover databases (LC-DB) have been established at various levels (global, national or regional scales), various spatial samplings and for various themes of interest (forest, agriculture, urban areas, etc.). However, they exhibit many flaws (limited geometric accuracy, low coverage) and require to be updated with automatic algorithms. Very High Resolution satellite imagery offers a suitable solution for setting up such on-purpose algorithms, and a large body of literature has tackled this topic. This paper proposes a framework that is able to deal with both LC-DB update of any kind and their enrichment in case of incomplete DB. The supervised classification-based solution integrates an efficient learning strategy that allows to capture the heterogeneity of the appearances of the various themes of interest. The proposed framework is favorably compared with two state-of-the-art methods, on a reconstructed dataset, composed of sub-metric satellite image patches.