Landscape units estimation in WorldView-2 images by using segmented urban areas, green areas and water bodies for monitoring variation/evolution of cities

This work proposes a method to estimate landscape units contained in a region (using satellite WorldView-2 imagery as input) for urban planning. Number of landscape units contained in a region and their extension are estimated by a graph-based segmentation algorithm while the composition of each landscape unit is estimated by a modular neural network. The proposed method, despite of the subjectivity of what represents a landscape unit, achieves the following results: vegetation estimation accuracy: 99.58%, water estimation accuracy: 98.08%, urban area estimation accuracy: 90.38% and soil estimation accuracy: 90.25%, over 2400 testing pixels (600 pixels/image – 4 satellite images).