Automatic generation of land-use maps for a spatial decision support system for Puerto Rico

This study proposes an automatic image processing procedure in order to facilitate regular updating of the land-use map of Puerto Rico, which is a key dataset for the Xplorah Planning Support Systems. The procedure is based on the contextual reclassification of digital high resolution aerial photographs that were preclassified using a decision tree classifier. For the contextual reclassification the Optimized Spatial Reclassification Kernel (OSPARK) is used, which is able to discriminate functional landuse classes and land cover based on the configuration of objects in a kernel. A unique property of OSPARK is that it automatically adapts the kernel size as a function of spatial variation in the neighborhood of each pixel to be classified. The processing chain has been implemented on a computer cluster, which enables parallel processing. Classification results were evaluated using independent land-use data derived from visual interpretation. It can be concluded that the procedure gives good classification results for the tiles that are used to train the algorithm, but that the extrapolation to other tiles resulted in much lower accuracies. Error sources have been identified and suggestions for improvements are given.