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Algorithms
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Algorithms and Application for Spatiotemporal Data Processing
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Algorithms and Application for Spatiotemporal Data Processing

A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 1 December 2026 | Viewed by 5788

Editor

Prof. Dr. Di Zhang

E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079，China
Interests: GNSS; precise engineering surveying; surveying adjustment; multi-source fusion positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous advancement of instrument science and sensor technology, spatiotemporal data are demonstrating more diverse types, more complex acquisition environments, and a dramatic increase in data volume. For the field of surveying and mapping, which emphasizes precision and reliability, the selection and improvement of algorithms have become particularly critical. The current challenges include, but are not limited to, the following: (1) data from different sources exhibit significant differences in format, accuracy, spatiotemporal resolution, physical meaning, and mathematical relationships, which poses the challenge of effectively fusing multi-source heterogeneous spatiotemporal data; (2) faced with massive data, traditional algorithms struggle to meet real-time demands, thereby highlighting the need for improvements in processing speed; (3) under extreme or harsh conditions, the data obtained is limited in quantity and uneven in quality, making the challenge of reliably extracting and interpreting necessary information particularly daunting; (4) traditional algorithms often require a significant manual intervention to process complex data, and how to enhance the intelligence level of data processing should be explored.

We invite high-quality, original research and review articles that delve into the latest advancements in spatiotemporal algorithms, their practical applications, and enhancements to current methodologies. We are particularly eager to receive contributions that showcase the implementation of these techniques in addressing complex systems across diverse fields.

Prof. Dr. Di Zhang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly 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 1800 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

  • geodesy and surveying engineering
  • industrial measurement and modeling
  • surveying adjustment and optimal estimation
  • multi-source fusion navigation and positioning
  • global navigation satellite systems (GNSSs)
  • remote sensing and photogrammetry
  • spatial data analysis
  • geospatial technologies
  • AI in surveying and mapping

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Published Papers (4 papers)

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Research

19 pages, 7282 KB  
Article
Localized Debris Detection in Post-Disaster Aerial Imagery Using YOLO-SDD
by Hassan Al-Derham, Mahitha Veeramachaneni, Lu Gao, Yunpeng Zhang, Jingran Sun, Ahmed Senouci and Kevin Fu
Algorithms 2026, 19(7), 568; https://doi.org/10.3390/a19070568 - 10 Jul 2026
Viewed by 349
Abstract
Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with shadows, vegetation, roofs, vehicles, and damaged structures. This study proposes YOLO-SDD, a [...] Read more.
Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with shadows, vegetation, roofs, vehicles, and damaged structures. This study proposes YOLO-SDD, a YOLO-based Shape-Guided Debris Detector built on YOLOv8 for localized debris identification in high-resolution post-disaster aerial imagery. YOLO-SDD combines a high-resolution P2 detection pathway with a shape-guided feature refinement module that uses box-supervised pseudo-mask and pseudo-boundary cues to refine P2-level features before final debris detection. A multi-event aerial imagery dataset was constructed from NOAA Emergency Response Imagery using images collected after hurricanes and a tornado in the United States. The model was evaluated using an image-level split, an event-level holdout test, component-level ablation studies, COCO-style scale-specific evaluation, and multi-seed stability analysis. On the image-level test set, YOLO-SDD achieved a precision of 0.959, recall of 0.933, mAP@50 of 0.970, and mAP@50:95 of 0.755, remaining competitive with larger YOLO-family models at lower computational complexity. In the event-level holdout test, YOLO-SDD achieved an AP@50 of 0.80 and an F1 score of 0.79, outperforming the YOLOv8s baseline and the selected large YOLO-family comparison model. The scale-specific evaluation showed improved AP@50 and recall for small and medium debris groups, while failure cases remained associated with shadows, vegetation, low contrast, and highly fragmented debris. The results indicate that shape-guided P2 refinement can improve localized debris screening under the tested conditions, although broader datasets, workflow integration, and human-in-the-loop validation are still needed before operational deployment. Full article
(This article belongs to the Special Issue Algorithms and Application for Spatiotemporal Data Processing)
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19 pages, 31453 KB  
Article
Performance Evaluation of Burn Area Indices for Effective Fire Detection Using Sentinel-2 Satellite Imagery
by Juan C. Valdiviezo-Navarro, Miguel Ángel Castillo-Santiago, Alejandro Téllez-Quiñones and Alejandra A. López-Caloca
Algorithms 2026, 19(2), 157; https://doi.org/10.3390/a19020157 - 16 Feb 2026
Viewed by 1267
Abstract
In recent years, different spectral indices have been adapted or proposed for burn area (BA) extraction from satellite imagery. Many such indices have been particularly designed for specific satellite sensors, which could limit their applicability to other platforms. This research aims to explore [...] Read more.
In recent years, different spectral indices have been adapted or proposed for burn area (BA) extraction from satellite imagery. Many such indices have been particularly designed for specific satellite sensors, which could limit their applicability to other platforms. This research aims to explore the performance of spectral indices for burn area detection and post-fire recovery evaluation tasks in forest ecosystems. For this purpose, nine vegetation and burn area indices, commonly used in the current literature, were chosen to perform different experiments using Sentinel-2 images collected from three study areas characterised by large fire events. A separability analysis using the Spectral Discrimination index (SDI) led us to determine that A New Burned Area Index (ABAI), the Normalised Burn Radio Plus (NBR+), and the Normalised Burn Radio (NBR) indices were capable of discriminating burn areas when clouds and shadows were present in the imagery. Moreover, a short-term time series analysis allowed the identification of particular spectral index methods that could be useful for post-fire recovery evaluation in forest ecosystems. Full article
(This article belongs to the Special Issue Algorithms and Application for Spatiotemporal Data Processing)
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13 pages, 2254 KB  
Article
Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
by Xianyang Yang, Wei Shu, Huoping Wang, Haifeng Li, Yi Wang, Di Zhang, Jiayu Liu, Deyang Wang and Wangsui Xiao
Algorithms 2025, 18(5), 292; https://doi.org/10.3390/a18050292 - 19 May 2025
Viewed by 1123
Abstract
The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the jacket is gradually assembled, its extreme [...] Read more.
The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the jacket is gradually assembled, its extreme weight will cause the widespread deformation of the surrounding ground surface, and each control point may be affected to varying degrees, resulting in the non-uniform deformation of the entire network. For adjustments of control networks in the subsequent phases, if the same starting point as the first phase is chosen without careful analysis, the starting points’ non-uniform deformation will degrade the whole network’s accuracy. Considering the particularities of the free station control network, this paper proposes an adjustment algorithm consisting of a three-step analytical method. Firstly, the initial coordinates of the points of the current phase are obtained through classical free network adjustment; second, stable and unstable points are identified via coordinate similarity transformation between the current and the first phase; and finally, quasi-stable adjustment is conducted. The experimental data analysis of a jacket control network shows that this method can effectively identify stable and unstable points, thereby ensuring construction accuracy and jacket stability. Full article
(This article belongs to the Special Issue Algorithms and Application for Spatiotemporal Data Processing)
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23 pages, 8305 KB  
Article
Ultra-Low-Cost Real-Time Precise Point Positioning Using Different Streams for Precise Positioning and Precipitable Water Vapor Retrieval Estimates
by Mohamed Abdelazeem, Amgad Abazeed, Hussain A. Kamal and Mudathir O. A. Mohamed
Algorithms 2025, 18(4), 198; https://doi.org/10.3390/a18040198 - 1 Apr 2025
Cited by 2 | Viewed by 2084
Abstract
This article aims to examine the real-time precise point positioning (PPP) solution’s accuracy utilizing the low-cost dual-frequency multi-constellation U-blox ZED-F9P module and real-time GNSS orbit and clock products from five analysis centers, including Bundesamt für Kartographie und Geodäsie (BKG), Centre National d’Etudes Spatiales [...] Read more.
This article aims to examine the real-time precise point positioning (PPP) solution’s accuracy utilizing the low-cost dual-frequency multi-constellation U-blox ZED-F9P module and real-time GNSS orbit and clock products from five analysis centers, including Bundesamt für Kartographie und Geodäsie (BKG), Centre National d’Etudes Spatiales (CNES), International GNSS Service (IGS), Geo Forschungs Zentrum (GFZ), and GNSS research center of Wuhan University (WHU). Three-hour static quad-constellation GNSS measurements are collected from ZED-F9P modules and geodetic grade Trimble R4s receivers over a reference station in Aswan City, Egypt, for a period of three consecutive days. Since a multi-GNSS PPP processing model is applied in the majority of the previous studies, this study employs the single-constellation GNSS PPP solution to process the acquired datasets. Different single-constellation GNSS PPP scenarios are adopted, namely, GPS PPP, GLONASS PPP, Galileo PPP, and BeiDou PPP models. The obtained PPP solutions from the low-cost module are validated for the positioning and precipitable water vapor (PWV) domains. To provide a reference positioning solution, the post-processed dual-frequency geodetic-grade GNSS PPP solution is applied; additionally, as the station under investigation is not a part of the IGS reference station network, a new technique is proposed to estimate reference PWV values. The findings reveal that the GPS and Galileo 3D position’s accuracy is within the decimeter level, while it is within the meter level for both the GLONASS and BeiDou models. Additionally, millimeter-level PWV precision is obtained from the four PPP models. Full article
(This article belongs to the Special Issue Algorithms and Application for Spatiotemporal Data Processing)
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