Development of Land Cover Classification Model Using AI Based FusionNet Network

Development of a Parcel-Level Land Boundary Extraction Algorithm for Aerial Imagery of Regularly Arranged Agricultural Areas

The boundary extraction of an object from remote sensing imagery has been an important issue in the field of research. The automation of farmland boundary extraction is particularly in demand for rapid updates of the digital farm maps in Korea. This study aimed to develop a boundary extraction algorithm by systematically reconstructing a series of computational and mathematical methods, including the Suzuki85 algorithm, Canny edge detection, and Hough transform. Since most irregular farmlands in Korea have been consolidated into large rectangular arrangements for agricultural productivity, the boundary between two adjacent land parcels was assumed to be a straight line. The developed algorithm was applied over six different study sites to evaluate its performance at the boundary level and sectional area level. The correctness, completeness, and quality of the extracted boundaries were approximately 80.7%, 79.7%, and 67.0%, at the boundary level, and 89.7%, 90.0%, and 81.6%, at the area-based level, respectively. These performances are comparable with the results of previous studies on similar subjects; thus, this algorithm can be used for land parcel boundary extraction. The developed algorithm tended to subdivide land parcels for distinctive features, such as greenhouse structures or isolated irregular land parcels within the land blocks. The developed algorithm is currently applicable only to regularly arranged land parcels, and further study coupled with a decision tree or artificial intelligence may allow for boundary extraction from irregularly shaped land parcels.