城镇风貌特色作为承载城镇品质的重要视觉载体，是城乡规划与设计工作中倍受关注的内容，鉴于传统城镇风貌特色规划与设计方法受限于规划师的专业背景、情感因素以及有限精力等问题，在某种程度上限制了该方法的发展。本项目跨界人工智能学科领域，借助电子与信息工程专业的遥感技术方法，再辅以历史学的社会科学方法论，以多学科交叉、互动的方式，达成研究目的。以黑龙江省境内的中东铁路设站城镇为主要研究对象，通过无人机搭载多种类传感器，多途径获取海量城镇影像数据，并借助深度学习算法对所获图像数据进行解译与判读，在城镇空间类型学研究的基础上，完成风貌特色要素的识别与分类，构建城镇风貌特色评价体系，并生成导控技术，从而突破传统规划设计方法的壁垒，实现风貌特色规划的范式创新。

Characteristic townscape, as the vision carrier of towns’ quality, is the crucial part in urban and rural planning and design. The traditional planning and design methods of characteristic townscape are subject to the professional background, emotion and limited energy, which greatly limit the development of its planning and design. This research project is a cross-border subject combining with artificial intelligence(AI) disciplines, supported by the scientific techniques of remote sensing from the discipline of electronic and information engineering, and also integrated with the social science methodology of history science; as a result, the purpose of this research task would be achieved in a way of cross-disciplines and subjects fusion. This research takes station-located towns along Chinese Eastern Railway within the area Heilongjiang Province as the main research objects and UAV is equipped with many types of sensors to obtain a considerable number of images and data. And deep learning algorithm is applied to interpret and analyze the acquired images and data. Based on the typology of towns’ space, the characteristic townscape is identified and classified to constitute the evaluation framework and the townscape guiding management methods, which both breaks through the barriers of traditional planning and design and realizing the paradigm innovation of characteristic townscape planning.