《国际数字地球学报》2020年第13卷第10期论文目次

2020年第13卷第10期论文目次

Current status and future directions of geoportals

Hao Jiang , John van Genderen , Paolo Mazzetti , Hyeongmo Koo & Min Chen

Pages: 1093-1114

Published online: 15 Apr 2019

摘要：

    Geoportals are a consolidated web-based solution to provide open spatial data sharing and online geo-information management. Their roles and possible advancements according to the Digital Earth vision and implementation require investigations. This paper presents a review of the literature concerning geoportals and serves the following primary purposes. First, various geoportal approaches for discovering and accessing Earth observation data and geo-information, mainly with scientific purposes, are summarized according to their characteristics and functionalities. Second, current major challenges in geoportals are identified in terms of functionalities, technologies, and especially big data support, from geoportal cases of China. Finally, based on lessons learned from the international and Chinese geoportals, solutions and recommendations for the challenges in geoportals are proposed in terms of their architectures, services, and technologies. The results show that geoportals usually provide access to distributed data systems, offering maps, data discovery, and data downloads. Some of them are also capable of offering online analysis and processing service, enhanced semantic search engines, and dynamic visualization tools. The strength of geoportals could lead to a full-fledged online Digital Earth system that could provide better data sharing and dissemination solutions to the challenges posed by big data.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1603331

Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds

Mario Soilán , Belén Riveiro , Jesús Balado & Pedro Arias

Pages: 1115-1134

Published online: 09 Sep 2019

摘要：

The automatic definition of the ground from 3D point clouds has been a common process for the last two decades, with many different approaches and applications that can be found in a vast literature. This paper presents a comparison of three different methodological concepts for ground classification, in order to establish the advantages and drawbacks of each method. First, a heuristic method, based on previous knowledge of the geometry and context of the 3D data. Secondly, a Deep Convolutional Network based on SegNet that classifies 2D images generated from the 3D point cloud. Finally, the third method applies a Deep Learning classification based on PointNet, which takes 3D points directly as inputs. To validate each method and compare them, public and labelled point clouds from the Actueel Hoogtebestand Nederland dataset are employed. Furthermore, the three methods are validated against the ISPRS 3D Semantic Labeling Contest benchmark. The results obtained show that the deep learning-based approaches outperform the heuristic method, with F-scores above 96%. The best results were obtained using a shallower version of SegNet, with F-score above 97%.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1663948

Fixing an illusion – an empirical assessment of correction methods for the terrain reversal effect in satellite images

Gianna Hartung & Arzu Çöltekin

Pages: 1135-1150

Published online: 20 Oct 2019

摘要：

Identifying land forms and land cover classes are important tasks in image interpretation. Sometimes, a phenomenon called terrain reversal effect (TRE) causes an inverted perception of 3D forms. When this inversion occurs, valleys appear as ridges and vice versa. While the TRE can severely impair the ability to identify 3D land forms, ‘correcting’ for the TRE in imagery can introduce new problems. Importantly, one of most commonly-proposed methods – shaded relief map (SRM) overlay – appears to impair the ability to identify land cover classes. In this paper, we report a comparative empirical evaluation of an SRM overlay solution, and its ‘enhanced’ versions supported by various other cues (stereopsis, motion, labels). In response to the different solutions, we measure the effectiveness, efficiency, confidence and preferences of our participants in land form and land cover identification tasks. All examined methods significantly improve the ability to detect land forms accurately, but they also impair the ability to identify the land cover classes to different degrees. Additionally, participants’ visualization preferences contradict their performance with them, calling for reflection on the visual effects of the applied correction methods. Based on the study, recommendations concerning the correction of the TRE are drawn, and gaps are identified.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1681526

A spatial multi-scale integer coding method and its application to three-dimensional model organization

Guangling Lai , Xiaochong Tong , Yongsheng Zhang , Lu Ding , Yinling Sui , Yi Lei & Yong Zhang

Pages: 1151-1171

Published online: 12 Nov 2019

摘要：

With the rapid development of digital earth, smart city, and digital twin technology, the demands of three-dimensional model data’s application is getting higher and higher. These data tend to be multi-objectification, multi-type, multi-scale, complex spatial relationship, and large amount, which brings great challenges to the efficient organization of them. This paper mainly studies the organization of three-dimensional model data, and the main contributions are as follows: 1) A integer coding method of three dimensional multi-scale grid is proposed, which can reduce the four-dimensional (spatial dimension and scale dimension) space into one-dimensional, and has better space and scale clustering characteristics by comparing with various types of grid coding. 2) The binary algebra calculation method is proposed to realize the basic spatial relationship calculation of three-dimensional grid, which has higher spatial relationship computing ability than 3D-Geohash method; 3) The multi-scale integer coding method is applied to the data organization of three-dimensional city model, and the experiment results show that: it is more efficient and stable than the three-dimensional R-tree index and Geohash coding method in the establishment of index and the query of three dimensional space.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1690594

Launching an unmanned aerial vehicle remote sensing data carrier: concept, key components and prospects

Xiaohan Liao , Huanyin Yue , Ronggao Liu , Xiangyong Luo , Bin Luo , Ming Lu , Barbara Ryan & Huping Ye

Pages: 1172-1185

Published online: 16 Dec 2019

摘要：

Unmanned aerial vehicles (UAV) based remote sensing is an emerging and important data source. Recently, the use of UAVs for remote sensing applications has been rapidly growing owing to their greater availability and the miniaturization of sensors. UAVs are surpassing satellites and aircraft in remote sensing data supply for many local requirements. In comparison with satellite remote sensing data, most UAV remote sensing data is characterized by high resolution, small coverage area, and heterogeneous multi-sources. However, UAVs lack a unified space–time framework and standardized data process. This paper describes a UAV remote sensing data carrier that can be used as an e-commerce platform for data sharing among registered members and a mission planner for new data acquisition. To the best of our knowledge, the data carriers described herein, are the first of their kind. Through seamless docking with UAVs, the data carrier will form a national UAV network, capable of dynamically obtaining very-high-resolution UAV remote sensing images. In practice, a pilot retrieval system of UAV meta data has been developed to provide a catalogue of data product services.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1698664

Taking the pulse of COVID-19: a spatiotemporal perspective

Chaowei Yang , Dexuan Sha , Qian Liu , Yun Li , Hai Lan , Weihe Wendy Guan , Tao Hu , Zhenlong Li , Zhiran Zhang , John Hoot Thompson , Zifu Wang , David Wong , Shiyang Ruan , Manzhu Yu , Douglas Richardson , Luyao Zhang , Ruizhi Hou , You Zhou , Cheng Zhong , Yifei Tian , Fayez Beaini , Kyla Carte , Colin Flynn , Wei Liu , Dieter Pfoser , Shuming Bao , Mei Li , Haoyuan Zhang , Chunbo Liu , Jie Jiang , Shihong Du , Liang Zhao , Mingyue Lu , Lin Li , Huan Zhou & Andrew Ding

Pages: 1186-1211

Published online: 25 Aug 2020

摘要：

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, Brazil, Russia, and the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., India, Russia, and Brazil. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, human mobility, environmental impact, socioeconomical impact; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

全文链接：

https://www.tandfonline.com/doi/full/10.1080/17538947.2020.1809723