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NEW METHODS IN ACQUISITION, UPDATE AND DISSEMINATION OF NATURE CONSERVATION GEODATA - IMPLEMENTATION OF AN INTEGRATED FRAMEWORK

机译:自然保护地理数据的获取,更新和发布的新方法-集成框架的实现

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Within the framework of this project methods are being tested and implemented a) to introduce remote sensing based approaches into the existing process of biotope mapping and b) to develop a framework serving the multiple requirements arising from different users' backgrounds and thus the need for comprehensive data interoperability. Therefore state-wide high resolution land cover vector-data have been generated in an automated object oriented workflow based on aerial imagery and a normalised digital surface models.These data have been enriched by an extensive characterisation of the individual objects by e.g. site specific, contextual or spectral parameters utilising multitemporal satellite images, DEM-derivatives and multiple relevant geo-data. Parameters are tested on relevance in regard to the classification process using different data mining approaches and have been used to formalise categories of the European nature information system (EUNIS) in a semantic framework. The Classification will be realised by ontology-based reasoning. Dissemination and storage of data is developed fully INSPIRE-compatible and facilitated via a web portal. Main objectives of the project are a) maximum exploitation of existing "standard" data provided by state authorities, b) combination of these data with satellite imagery (Copernicus), c) create land cover objects and achieve data interoperability through low number of classes but comprehensive characterisation and d) implement algorithms and methods suitable for automated processing on large scales.
机译:在该项目的框架内,正在测试和实施方法:a)将基于遥感的方法引入到生物群落标测的现有过程中; b)开发一个框架,以满足因不同用户背景而产生的多种需求,因此需要全面数据互操作性。因此,已经在基于航空影像和规范化数字表面模型的面向对象的自动化工作流程中生成了州范围的高分辨率土地覆盖矢量数据。利用多时相卫星图像,DEM导数和多个相关地理数据的站点特定,上下文或频谱参数。使用不同的数据挖掘方法对与分类过程相关的参数进行测试,并已将参数用于在语义框架中形式化欧洲自然信息系统(EUNIS)的类别。分类将通过基于本体的推理来实现。数据的发布和存储完全与INSPIRE兼容,并通过Web门户得以简化。该项目的主要目标是:a)最大限度地利用州政府提供的现有“标准”数据,b)将这些数据与卫星图像(哥白尼)相结合,c)创建土地覆盖物并通过少量分类实现数据互操作性,但是d)实施适用于大规模自动化处理的算法和方法。

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