期刊
XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III
卷 5-3, 期 -, 页码 25-31出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/isprs-annals-V-3-2022-25-2022
关键词
Volunteered Geographical Information; OSM; Remote Sensing; Satellite; Convolutional Neural Networks; Deep Learning
资金
- Portuguese Foundation for Science and Technology (FCT) [UIDB/00308/2020]
This study evaluates the geographical transferability of satellite image-based segmentation models trained with OpenStreetMap (OSM) derived data through a series of experiments. The results show that models trained in different locations can improve the mapping of certain classes.
Synoptic remote sensing systems have been broadly used within supervised classification methods to map land use and land cover (LULC). Such methods rely on high quality sets of training data that are able to characterize the target classes. Often, training data is manually generated, either by field campaigns and/or by photointerpretation of ancillary remote sensing imagery. Several authors already proposed methodologies to attenuate such labour-intensive task of generating training data. One of the preferred datasets that are used as input training data is OpenStreetMap (OSM), which aims at creating a publicly available vector map of the world with the input of volunteers. However, OSM data is spatially heterogenous (e.g., capital cities and highly populated areas often have high degrees of completion while unpopulated regions often have a lower degree of completion), where there are still large areas without OSM coverage. In this paper we present a set of experiments that aim at assessing the geographical transferability of satellite imagebased segmentation models trained with OSM derived data. To this end, we chose two locations with different OSM coverage and disparate landscape (metropolitan region vs natural park region, in different landscape units), and assess how these models behave when trained in a region and applied in the other. The results show that the mapping of some classes is improved when considering a model trained in a different location.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据