Landscape Classification: Prior Work

Recent work in landscape classification. Top: Binary classification using a support vector machine classifier (Jean et. al., 2015). This approach partitions images into patches of vegetation (light patches) and non-vegetation (dark patches), which can be reconstructed into coarse, grid-like segmentation maps (Jean et al. 2015). Middle: Binary classification of coniferous (dark patches) and non-coniferous (light patches) regions using a grid-based convolutional classifier (Bayr and Puschmann, 2019); Bottom: Classification of various land cover types (as indicated) using a Conditional Random Fields CRF hybrid classifier (Buscombe and Ritchie, 2018). This hybrid method for high-vantage images combines convolutional networks with conditional random fields, a statistical modelling used to enhance classifier predictions.