Open land-use map: a regional land-use mapping strategy for incorporating OpenStreetMap with earth observations
Open land-use map: a regional land-use mapping strategy for incorporating OpenStreetMap with earth observations
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DOI：
10.1080/10095020.2017.1371385
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发表时间：
2017-07
影响因子：
6
通讯作者：
Di Yang;Chiung-Shiuan Fu;A. C. Smith;Q. Yu
中科院分区：
文献类型：
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作者：
Di Yang;Chiung-Shiuan Fu;A. C. Smith;Q. Yu
摘要区域尺度的土地利用图是一项繁重的计算任务，但对大多数土地所有者、研究人员和决策者来说至关重要，使他们能够为不同的目标做出明智的决策。在区域尺度上生成土地分类图有两个主要困难：训练点的大型数据集的必要性和昂贵的计算成本，在金钱和时间方面。通过提供一个开放访问的数据库，由志愿公民收集的宝贵的地理参考信息，嵌入式地理信息开辟了一个新的时代，在映射和可视化的物理世界。作为最著名的VGI倡议之一，开放街道地图（OSM），不仅有助于道路网络分布信息，而且还有助于利用这些数据来证明和描绘土地格局的潜力。鉴于大多数大规模制图方法-包括区域和国家尺度-混淆了“土地覆盖”和“土地利用”，或基于模拟的土地覆盖数据集建立土地利用数据库，在这项研究中，我们清楚地区分和区分土地利用和土地覆盖。通过把重点放在我们的主要目标，映射土地利用和管理的做法，一个强大的区域土地利用制图方法，通过集成OSM数据与地球观测遥感图像。我们的新方法采用了一个重要的时间组成部分，大规模的土地利用制图，同时有效地消除了通常繁重的计算和时间/金钱的要求，这样的工作。此外，我们的新方法在区域尺度土地利用制图产生了强大的结果，在我们的研究领域：分类器的整体内部精度为95.2%，外部精度的分类器测量为74.8%。
Abstract A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches – including regional and national scales – confuse “land cover” and “land-use”, or build up the land-use database based on modeled land cover data-sets, in this study, we clearly distinguished and differentiated land-use from land cover. By focusing on our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery. Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work. Furthermore, our novel approach in regional scale land-use mapping produced robust results in our study area: the overall internal accuracy of the classifier was 95.2% and the external accuracy of the classifier was measured at 74.8%.