A new ant based distributed framework for urban road map updating from high resolution satellite imagery

Receiving updated information about the network of roads from high resolution satellite imagery is a crucially important issue in continuously changing developing urban regions. Considering experiences in road extraction and also exploiting distributed evolutionary computational approaches, in this paper a new framework for road map updating from remotely sensed data is proposed. Three main computational entities of ant-agent, seed extractor and algorithm library are designed and road map updating is performed through three main stages of verification of the old map, extraction of possible roads and grouping of the results of both stages. Extracting corresponding pixels to each road element in the map, an object level supervised classification or any available road verification algorithm from the library capable of producing a road likeliness value is applied. Since road extraction is a simple and also a complex problem, more comprehensive algorithms are chosen from library iteratively by ant-agents so the decision about verification and rejection of each road element is finally made. Ant-agents facilitate choosing road elements and moving of ant agents via stigmergic communication by pheromone cast and evaporation. The proposed method is developed and tested using GeoEye-1 pan-sharpen imagery and 1:2000 corresponding digital vector map of the region. As observed, the results are satisfactory in terms of detection, verification and extraction of roads and generation of the updated map specifically in case of inspection of main roads. Besides, some missed road items are reported in case of inspection of bystreets and alleys specially when situated at the margin of the image. Completeness, correctness and quality measures are computed for evaluation of the initial and the resulted updated maps. The computed measures verify the improvement of the updated map.