Digital Mapping and Comparison of Natural and Synthetic Landscapes

自然景观和合成景观的数字测绘和比较

• 批准号: 1147702
• 负责人: Tomasz Stepinski
• 金额: $ 8万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147702&HistoricalAwards=false
• 关键词: Digital; Mapping; Comparison; Natural; Synthetic

Advances in remote sensing and geographical information science (GIS) resulted in proliferation of medium- to-high resolution digital elevation models (DEMs) of global or continental-scale spatial extents. A DEM requires further algorithmic processing in order to provide information for geomorphologic and/or geologic analysis. The standard DEM-derived product is a map of shaded relief that provides a three-dimensional depiction of the terrain. It enables visual terrain analysis, but it is not meant for an automated, algorithm-based analysis. This research project will develop a methodology for auto-generating a thematic map of landforms and tools for its automated analysis. Unlike shaded relief maps, which depict variations in elevation field, the new "geomorphometric" map will depict semantic interpretation of terrain. It will offer an alternative tool for visual geomorphologic analysis, and it will be susceptible to automated terrain analysis. Machine vision-based methodology will be used to generate a robust geomorphometric map in a manner that is computationally efficient. The investigators will deliver a geomorphometeric map of the entire conterminous Unites States based on 30 meters per pixel DEMs. Automated analysis of terrain will be performed by measuring a quantitative similarity between any two "landscapes" for small sections of the map. Similarity-based analysis will be used to determine landscape archetypes, which are characteristic patterns of landforms found across the conterminous Unites States. The new methods also will used to calculate a degree of correspondence between real and simulated landscapes, thus assessing relative success of different landscape evolution models.This project will use machine-vision and similarity-measure techniques to provide better tools for terrain analysis. The project will yield a method for robust and efficient generation of geomorphometric maps, which will provide significant enhancements to shaded relief, thereby facilitating more complete analysis of terrestrial and planetary terrains. The project will provide objective means of landscape classification, a new direction in the field of geomorphometry. It will enable objective, easy-to-interpret comparison of synthetic landscapes to ground truth and produce much-needed quality metric to landscape evolution models. The project further integrates domains of geography and spatial science with computer science domains of pattern recognition and content-based image retrieval.

遥感和地理信息科学（GIS）的进步导致全球或大陆尺度空间范围的中高分辨率数字高程模型（DEM）的激增。 DEM需要进一步的算法处理，以便为地貌和/或地质分析提供信息。 标准的DEM衍生产品是一张地形阴影图，提供了地形的三维描述。 它支持可视化地形分析，但不适用于基于算法的自动分析。 该研究项目将开发一种自动生成地貌专题图的方法及其自动分析工具。 与描绘高程场变化的阴影地形图不同，新的“地貌测量”地图将描绘地形的语义解释。 它将提供一个视觉地貌分析的替代工具，它将易于自动地形分析。 将使用基于机器视觉的方法，以计算效率高的方式生成可靠的地貌测量图。 调查人员将根据每像素30米的数字高程模型提供整个美国接壤州的地貌地图。 地形的自动分析将通过测量地图上任何两个小部分“景观”之间的定量相似性来进行。 基于相似性的分析将用于确定景观原型，这些原型是在美国接壤地区发现的地形特征模式。 新方法亦将用于计算真实的与模拟景观之间的对应程度，从而评估不同景观演变模型的相对成功性。本项目将使用机器视觉和相似性测量技术，为地形分析提供更好的工具。 该项目将产生一种可靠和有效地生成地貌图的方法，这将大大增强地貌阴影，从而促进对陆地和行星地形进行更全面的分析。 该项目将提供客观的景观分类方法，这是地貌测量学领域的一个新方向。 它将使合成景观与地面实况的客观，易于解释的比较成为可能，并为景观演变模型提供急需的质量指标。该项目进一步将地理和空间科学领域与计算机科学领域的模式识别和基于内容的图像检索相结合。