We quantify historical land-use with deep learning image segmentation (GeoAI), applied to tiled historical maps, and identify 3 successive drivers of long-term (1774-2022) landscape transformation in northern Belgium (13,800 km2). Between 1774 and 1873, reclamation halved the area of long-established forests, heathland, marshland, and the intertidal zone, i.e. natural and semi-natural land-use. Agricultural transition by globalization was the main driver in the next time interval (1873-1969), as the area of grassland and orchard doubled at the expense of arable land. Urbanization marked the last time interval (1969-2022) and reduced agricultural land-use. The reclamation of fertile soils first increased the association of land-use with soil, but after 1873 this association progressively weakened and land-use interspersed. Here, we demonstrate that GeoAI can generate high-resolution area-wide historical land-use maps to study the extent and rate of landscape transformation, which in our case resulted in the homogenization of previously distinct landscapes.
Keersmaeker et al. (Wed,) studied this question.