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Object-based Class Modeling for Cadastre-constrained Delineation of Geo-objects

机译:基于地物的地物约束描述的基于对象的类建模

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摘要

Remote sensing technology still faces challenges when it comes to monitoring tasks that must be able to stand up to validation from technical, scientific, and practical points of view, in other words, when entering into established, fully operationalworkflows. In this paper, we present an approach for delineating and monitoring aggregated spatial units relevant to regional planning tasks, which has been fully validated within a 3,654 km2 area in the Stuttgart Region of southwestern Germany. This hasbeen achieved by developing algorithms for semi-automated (geo-) object-based class modeling of biotope complexes, which are aggregated, functionally homogenous (but not necessarily spectrally homogenous) units. High levels of complexity in the target classes and the need for integration of auxiliary geodata as a priori knowledge meant that different methods of information extraction were required to be combined in an operational workflow, and that new validation strategies were needed for quality assessment. A total of 31,698 biotope complexes were delineated for the entire Stuttgart Region, with an average size of 11.5 ha for each complex. Approximately 86 percent of the biotope complex boundaries were shown to have been correctly delineated.
机译:遥感技术在监视任务时仍然面临挑战,这些任务必须能够从技术,科学和实践的角度经受考验,换句话说,当进入已建立的完全可操作的工作流程时。在本文中,我们提出了一种用于划定和监视与区域规划任务相关的集合空间单位的方法,该方法已在德国西南部斯图加特地区3,654 km2的区域内得到了充分验证。这已经通过开发用于生物群落复合物的半自动化(基于地理对象)的类建模的算法来实现,该算法是聚集的,功能上均质的(但不一定是光谱均质的)单元。目标类别的高度复杂性以及作为先验知识需要集成辅助地理数据的需要,意味着在工作流程中需要组合不同的信息提取方法,并且需要新的验证策略来进行质量评估。整个斯图加特地区共划定了31,698个生物群落复合物,每个复合物的平均大小为11.5公顷。正确划分出约86%的生物群落复杂边界。

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