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Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-18 , DOI: 10.1016/j.jag.2022.103018
Shengyuan Zou, Le Wang

Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges.



中文翻译:

通过集成 VHR 遥感和街景图像绘制跨城市的废弃房屋地图

废弃房屋 (AH) 对当代美国收缩城市的城市环境提出了最大挑战。数据可访问性是阻止在单个财产级别获取大规模 AH 信息的主要障碍。为此,最新的开放获取遥感平台革命见证了城市环境的多源、多视角精细空间分辨率数据,其中超高分辨率(VHR)自上而下视图遥感图像和水平透视谷歌街景(GSV)图像是突出的例子。在这项研究中,我们的目标是通过开发一种可以有效利用 VHR 遥感和 GSV 图像的方法来绘制各个城市的废弃房屋地图。所提出的方法由四个步骤组成。第一的,我们探讨了三个最相关和互补的遥感数据用于个体水平 AH 检测的可行性,即白天 VHR 图像、夜间灯光 VHR 图像和 GSV 图像。其次,我们从三个不同的数据源中提取了表明房屋废弃情况的判别特征。第三,我们将决策级融合与 Dempster-Shafer 理论 (DST) 结合使用,以更好地利用有关数据有效性的先验知识。在最后一步中,首先实施了地理随机森林 (GRF) 模型,以改进对 GSV 图像上房屋被遮挡位置的预测。我们在美国纽约州布法罗和俄亥俄州克利夫兰这两个典型的收缩城市中绘制了个体 AH,这使我们能够进一步探索 AH 的个体财产级空间特征。结果表明,所提出的 DST 融合和 GRF 预测在两个城市中始终取得了可喜的表现。鉴于结合开放获取和多视角数据的优点,我们提出的方法有可能被推广到理解解决住房遗弃挑战的区域和国家规模的城市环境。

更新日期:2022-09-18
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