2019 Joint Urban Remote Sensing Event (JURSE)最新文献_第8页

Deep Learning Models to Count Buildings in High-Resolution Overhead Images 在高分辨率头顶图像中计算建筑物的深度学习模型

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809058

Sylvain Lobry, D. Tuia

引用次数: 6

Towards combining Satellite Imagery and VGI for Urban LULC classification 卫星影像与VGI相结合的城市LULC分类研究

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808966

D. Ienco, K. Ose, C. Weber

引用次数: 1

Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka 评价高空间分辨率影像的背景特征与城市属性之间的关系:以斯里兰卡为例

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809041

R. Engstrom, R. Harrison, M. Mann, Amanda Fletcher

{"title":"Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka","authors":"R. Engstrom, R. Harrison, M. Mann, Amanda Fletcher","doi":"10.1109/JURSE.2019.8809041","DOIUrl":"https://doi.org/10.1109/JURSE.2019.8809041","url":null,"abstract":"Extracting information about variations within urban areas using satellite imagery has generally focused on mapping individual buildings or slum versus non-slum areas. While these data are useful, they can run into issues in very dense urban areas, additionally slums have a subjective definition. In previous research we have found that contextual features are related to population, census variables, poverty, and other values, but have not explored which urban attributes (i.e., buildings and roads) these features represent. In this study we seek to determine the correlation between contextual features calculated on Very High Spatial Resolution (VHSR) satellite data and urban attributes derived from Open Street Map (OSM) for portions of multiple cities in Sri Lanka. Results indicate that individual contextual features are highly correlated with building area, building density, road area, road density, total built up areas and other features. Moreover, when multiple contextual features are combined within a model, they can explain from 70 to 92 percent of the variance of these urban features within the study area. This indicates that contextual features are very strong indicators of urban variability and can be used to map differences within the urban setting. This may allow us to forgo having to map each building and road individually for mapping urban areas in future projects.","PeriodicalId":299183,"journal":{"name":"2019 Joint Urban Remote Sensing Event (JURSE)","volume":"312 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116763442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Automated Mapping Of Accessibility Signs With Deep Learning From Ground-level Imagery and Open Data 基于地面图像和开放数据的深度学习的无障碍标志自动映射

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808961

A. Nassar, S. Lefèvre

引用次数: 2

Urban Expansion Trajectories in China’s 36 Major Cities 中国36个主要城市的城市扩张轨迹

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808981

Yao Shen, Huanfeng Shen, Qing Cheng, Liwen Huang, Liangpei Zhang

引用次数: 0

Learning geometric soft constraints for multi-view instance matching across street-level panoramas 学习几何软约束的多视图实例匹配跨街道级全景

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808935

A. Nassar, Nico Lang, S. Lefèvre, J. D. Wegner

引用次数: 5

Contextual Information Based SAR Tomography of Urban Areas 基于上下文信息的城市区域SAR层析成像

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809076

A. Budillon, A. C. Johnsy, Gilda Schirinzi

引用次数: 7

Improving the SLEUTH urban growth model via temporal consistency in urban input data 基于城市输入数据时间一致性的SLEUTH城市增长模型改进

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809025

Sarochinee Kaewthani, Chaiyapon Keeratikasikorn

引用次数: 0

Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN 大规模建筑提取在非常高分辨率的航空图像使用掩模R-CNN

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8808977

Dorothee Stiller, Thomas Stark, M. Wurm, S. Dech, H. Taubenböck

{"title":"Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN","authors":"Dorothee Stiller, Thomas Stark, M. Wurm, S. Dech, H. Taubenböck","doi":"10.1109/JURSE.2019.8808977","DOIUrl":"https://doi.org/10.1109/JURSE.2019.8808977","url":null,"abstract":"Urban areas are hotspots of complex and dynamic alterations of the Earth’s surface. Using deep learning (DL) techniques in remote sensing applications can significantly contribute to document these tremendous changes. Open source building data at a very high level of detail are still scarce or incomplete for many regions, therefore, hindering research and policy to properly provide knowledge on urban structures. In this study, we use a convolutional neural network to extract buildings for the city of Santiago de Chile. We deploy the recently released Mask R-CNN and use a pretrained model (PM) which already has been trained with remote sensing imagery. We fine-tune PM with very high-resolution (VHR) airborne RGB images from our study region and generate the fine-tuned model (FM). To extend the number of training data, we test several data augmentation methods for training purposes and evaluate their performance in context of urban environments. We achieve highest overall accuracy of 92 % by using augmentations and the generated FM. Our findings encourage to use DL methods in the urban context. The presented method can be adapted and applied to other global urban regions, and, help to overcome lacks in open source building data to assess urban environments.","PeriodicalId":299183,"journal":{"name":"2019 Joint Urban Remote Sensing Event (JURSE)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115339924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

The necessary yet complex evaluation of 3D city models: a semantic approach 三维城市模型的必要而复杂的评估:一种语义方法

2019 Joint Urban Remote Sensing Event (JURSE) Pub Date : 2019-05-01 DOI: 10.1109/JURSE.2019.8809002

O. Ennafii, C. Mallet, A. L. Bris, Florent Lafarge

引用次数: 2