Digital Mapping and Comparison of Natural and Synthetic Landscapes

遥感和地理信息科学（GIS）的进步导致了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衍生产品是阴影浮雕地图，提供地形的三维描述。它支持可视地形分析，但它不是用于自动的、基于算法的分析。本研究计划将发展一种自动生成地形专题地图的方法，以及自动分析地图的工具。与描绘高程变化的阴影地形地图不同，新的“地貌学”地图将描绘地形的语义解释。它将为可视化地貌分析提供另一种工具，并且易于实现自动地形分析。基于机器视觉的方法将被用于以一种计算效率高的方式生成健壮的地貌图。研究人员将提供基于每像素30米的dem的整个美国的地貌学地图。地形的自动分析将通过测量地图上任何两个“景观”之间的定量相似性来执行。基于相似性的分析将用于确定景观原型，这是在美国各地发现的地形特征模式。新方法还将用于计算真实景观和模拟景观之间的对应程度，从而评估不同景观演化模型的相对成功程度。该项目将使用机器视觉和相似度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学地图的方法，这将大大增强阴影地形，从而促进对陆地和行星地形的更完整分析。该项目将为景观分类提供客观手段，为地貌学研究开辟新的方向。它将实现客观、易于解释的合成景观与地面事实的比较，并为景观演变模型提供急需的质量度量。该项目进一步将地理和空间科学领域与模式识别和基于内容的图像检索的计算机科学领域相结合。

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.