Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Remote Sensing Datasets and 3D Visualization of Geospatial Big Data
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Datasets and 3D Visualization of Geospatial Big Data

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

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 731

Special Issue Editors

Dr. Mohammad Kakooei

E-Mail Website
Guest Editor
Department of Computer Science, Chalmers University of Technology, Rännvägen 6, 41258 Gothenburg, Sweden
Interests: image processing; machine learning; remote sensing; parallel processing; GPGPU; and data mining applications
Special Issues, Collections and Topics in MDPI journals
Dr. Meisam Amani

E-Mail Website
Guest Editor
WSP Environment and Infrastructure Canada Limited, Ottawa, ON, Canada
Interests: remote sensing; wetlands; soil moisture; image classification; geospatial science; digital mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Yasser Baleghi

E-Mail Website
Guest Editor
Department of Electronic Engineering, Babol Noshirvani University of Technology, Babol, Iran
Interests: remote sensing; convolutional neural network; infrared and visible images; image color analysis; feature extraction; transfer learning

Special Issue Information

Dear Colleagues,

Remote sensing technologies have rapidly expanded their reach across various domains, including agriculture, forestry, weather forecasting, urban planning, environmental studies, oceanography, and socioeconomic analysis. The diverse applications of remote sensing encompass many of the Sustainable Development Goals (SDGs), offering invaluable insights for analysis and future planning aimed at fostering sustainable human life. Leveraging remote sensing datasets as a powerful tool for decision making and policy formulation, this interdisciplinary field underscores the need for effective visualization techniques to render datasets comprehensible and interpretable across different stakeholders.

In line with the dissemination of social benefits, this Special Issue seeks to explore the nexus between remote sensing datasets, open access initiatives, and 3D visualization techniques tailored for geospatial big data. We invite contributions that advocate for free access to remote sensing datasets within the public domain and showcase innovative approaches to the 3D visualization of geospatial big data. Topics of interest encompass a broad spectrum of research areas within remote sensing and earth observation, including but not limited to the following:

  • Urban planning and development;
  • Estimation of building heights and urban morphology;
  • Socioeconomic analysis, including poverty and wealth distribution studies;
  • Forest monitoring and ecosystem analysis;
  • Digital twin technologies for urban and environmental modeling.

We encourage submissions that address multisource data integration, such as multispectral, hyperspectral, and thermal imaging, as well as multiscale approaches to remote sensing data analysis. Additionally, we welcome contributions that elucidate the methodologies employed, spanning from shallow models to deep learning techniques, in the generation of new remote sensing datasets. Prospective authors are invited to submit original research articles, reviews, or methodological studies that contribute to the advancement of remote sensing datasets and 3D visualization techniques for sustainable development.

Dr. Mohammad Kakooei
Dr. Meisam Amani
Dr. Yasser Baleghi
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.

Keywords

  • remote sensing
  • 3D visualization
  • urban planning
  • dataset
  • mapping
  • satellite imagery

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 5474 KiB  
Article
WHU-RS19 ABZSL: An Attribute-Based Dataset for Remote Sensing Image Understanding
by Mattia Balestra, Marina Paolanti and Roberto Pierdicca
Remote Sens. 2025, 17(14), 2384; https://doi.org/10.3390/rs17142384 - 10 Jul 2025
Viewed by 260
Abstract
The advancement of artificial intelligence (AI) in remote sensing (RS) increasingly depends on datasets that offer rich and structured supervision beyond traditional scene-level labels. Although existing benchmarks for aerial scene classification have facilitated progress in this area, their reliance on single-class annotations restricts [...] Read more.
The advancement of artificial intelligence (AI) in remote sensing (RS) increasingly depends on datasets that offer rich and structured supervision beyond traditional scene-level labels. Although existing benchmarks for aerial scene classification have facilitated progress in this area, their reliance on single-class annotations restricts their application to more flexible, interpretable and generalisable learning frameworks. In this study, we introduce WHU-RS19 ABZSL: an attribute-based extension of the widely adopted WHU-RS19 dataset. This new version comprises 1005 high-resolution aerial images across 19 scene categories, each annotated with a vector of 38 features. These cover objects (e.g., roads and trees), geometric patterns (e.g., lines and curves) and dominant colours (e.g., green and blue), and are defined through expert-guided annotation protocols. To demonstrate the value of the dataset, we conduct baseline experiments using deep learning models that had been adapted for multi-label classification—ResNet18, VGG16, InceptionV3, EfficientNet and ViT-B/16—designed to capture the semantic complexity characteristic of real-world aerial scenes. The results, which are measured in terms of macro F1-score, range from 0.7385 for ResNet18 to 0.7608 for EfficientNet-B0. In particular, EfficientNet-B0 and ViT-B/16 are the top performers in terms of the overall macro F1-score and consistency across attributes, while all models show a consistent decline in performance for infrequent or visually ambiguous categories. This confirms that it is feasible to accurately predict semantic attributes in complex scenes. By enriching a standard benchmark with detailed, image-level semantic supervision, WHU-RS19 ABZSL supports a variety of downstream applications, including multi-label classification, explainable AI, semantic retrieval, and attribute-based ZSL. It thus provides a reusable, compact resource for advancing the semantic understanding of remote sensing and multimodal AI. Full article
(This article belongs to the Special Issue Remote Sensing Datasets and 3D Visualization of Geospatial Big Data)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop