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Remote Sensing
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Big Data Analytics in Geospatial Artificial Intelligence
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Big Data Analytics in Geospatial Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 3758

Special Issue Editors

Dr. Lisi Chen

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China
Interests: spatio-temporal data management; spatial keyword query processing; stream data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Kaiqi Zhao

E-Mail Website
Guest Editor
School of Computer Science, University of Auckland, Auckland 1010, New Zealand
Interests: spatio-temporal data mining; text mining; recommender systems
Dr. Xin Cao

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of New South Wales, UNSW, Sydney, Australia
Interests: spatial databases; graph data analytics; geo-social network data management; artificial intelligence and machine learning applications
Dr. Peng Han

E-Mail Website
Guest Editor
Department of Computer Science, Aalborg University, 9100 Aalborg, Denmark
Interests: object detection; distributed deep learning

Special Issue Information

Dear Colleagues,

With the continued proliferation of GPS-enabled devices and the ongoing development of online map services and location-based social media, the volume of geospatial data is skyrocketing. The omnipresence of geospatial data has given rise to the geospatial artificial intelligence (GeoAI) community, which aims to combine innovative ideas in spatio-temporal data management, data mining, machine learning, and high-performance computing to extract knowledge from big geospatial data. Apart from the innate characteristics of general big data (e.g., velocity, volume, value, variety, and veracity), big geospatial data bears additional unique characteristics, including  heterogeneity, multimodality, and diversity. Thus, in order to offer timely and high-quality data resources to the GeoAI community, it is of paramount importance to develop effective data representation models, hybrid data structures, and efficient search and mining algorithms to analyze big geospatial data.

This Special Issue aims to present fundamental research studies combining innovative ideas in spatio-temporal data management, data mining, machine learning, and high-performance computing to analyze big geospatial data. Research studies which will be considered for this Special Issues may be related to the following three scopes of remote sensing: geometric reconstruction, change detection, and data fusion and data assimilation.

Suggested topics for research include the following:

  1. Topic exploration and event detection from geospatial data.
  2. Big trajectory data management and analytics.
  3. Parallel computing framework and algorithms for fundamental operations regarding geospatial data.
  4. Location-aware prediction (e.g., POI recommendations, next-location predictions).
  5. Effective indexing structures for geospatial data.
  6. Route planning and recommendations on the basis of geospatial data.
  7. Spatio-temporal query processing and optimizations.
  8. Other innovative techniques related to GeoAI.

Dr. Lisi Chen
Dr. Kaiqi Zhao
Dr. Xin Cao
Dr. Peng Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

20 pages, 1187 KiB  
Article
In Search of the Max Coverage Region in Road Networks
by Lanting Fang, Ze Kou, Yuzhang Zhou, Yudong Zhang and George Y. Yuan
Remote Sens. 2023, 15(5), 1289; https://doi.org/10.3390/rs15051289 - 26 Feb 2023
Viewed by 1154
Abstract
The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem [...] Read more.
The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions. Given a set of spatial objects and a coverage radius, the MCR problem aims to identify a location from the road network, so that we can reach as many spatial objects as possible within the given coverage radius from the location. This problem is fundamental to supporting many real-world applications. Given a road network and a set of sensors, this problem can be used to find the best location for a sensor maintenance station. This problem can also be applied in medical research, such as in a protein–protein interaction network, where the nodes represent proteins, the edges represent their interactions, and the weight of an edge represents confidence. We can use the MCR problem to find the set of interacting proteins with a confidence budget. We propose an efficient exact solution to solve the problem, where we reduce the MCR problem to an equivalent problem named the most overlapped interval and design an edge-level upper bound estimation method to reduce the search space. Furthermore, we propose two approximate solutions that sacrifice a little accuracy for much better efficiency. Our experimental study on real-road network datasets demonstrates the effectiveness and superiority of the proposed approaches. Full article
(This article belongs to the Special Issue Big Data Analytics in Geospatial Artificial Intelligence)
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Graphical abstract

23 pages, 2295 KiB  
Article
Representing Spatial Data with Graph Contrastive Learning
by Lanting Fang, Ze Kou, Yulian Yang and Tao Li
Remote Sens. 2023, 15(4), 880; https://doi.org/10.3390/rs15040880 - 5 Feb 2023
Cited by 2 | Viewed by 1824
Abstract
Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we [...] Read more.
Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines. Full article
(This article belongs to the Special Issue Big Data Analytics in Geospatial Artificial Intelligence)
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