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Automatic extraction of buildings in urban and residential scenes has becomea subject of growing interest in the domain of photogrammetry and remotesensing, particularly since mid-1990s. Active contour model, colloquially knownas snake model, has been studied to extract buildings from aerial and satelliteimagery. However, this task is still very challenging due to the complexity ofbuilding size, shape, and its surrounding environment. This complexity leads toa major obstacle for carrying out a reliable large-scale building extraction,since the involved prior information and assumptions on building such as shape,size, and color cannot be generalized over large areas. This paper presents anefficient snake model to overcome such challenge, called Super-Resolution-basedSnake Model (SRSM). The SRSM operates on high-resolution LiDAR-based elevationimages -- called z-images -- generated by a super-resolution process applied toLiDAR data. The involved balloon force model is also improved to shrink orinflate adaptively, instead of inflating the snake continuously. This method isapplicable for a large scale such as city scale and even larger, while having ahigh level of automation and not requiring any prior knowledge nor trainingdata from the urban scenes (hence unsupervised). It achieves high overallaccuracy when tested on various datasets. For instance, the proposed SRSMyields an average area-based Quality of 86.57% and object-based Quality of81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methodsusing this benchmark dataset, this level of accuracy is highly desirable evenfor a supervised method. Similarly desirable outcomes are obtained whencarrying out the proposed SRSM on the whole City of Quebec (total area of 656km2), yielding an area-based Quality of 62.37% and an object-based Quality of63.21%.

Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical Image