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Remote Sensing
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Big Geo-Spatial Data and Advanced 3D Modelling in GIS and...
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Big Geo-Spatial Data and Advanced 3D Modelling in GIS and Satellite

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 5298

Special Issue Editors

Dr. Lidia M. Ortega Alvarado

E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: photogrammetry; computational geometry; visibility; urban modeling; GIS
Dr. María I. Ramos Galan

E-Mail Website
Guest Editor
Cartographic, Geodetic and Photogrammetric Engineering Department, University of Jaén, 23071 Jaén, Spain
Interests: precision farming; remote sensing; spatial data mining; geospatial data

Special Issue Information

Dear Colleagues,

Remote sensing has become widespread in the last decade thanks to the advancement of sensing devices, coupled with both satellites and aerial vehicles such as UAVs (unmanned aerial vehicles). They are able to generate massive datasets with a high spatial resolution, which involves many different challenges for their processing. The captured information has a marked spatial character and can change over time across different captures, requiring spatiotemporal information systems. On the other hand, the capture of a unique data type may not be sufficient, requiring multi-source data fusion of heterogeneous data. Furthermore, the real world is three-dimensional, and 3D modelling describes the geometry and appearance of real scenarios, providing the user with a more accurate scene understanding.  In summary, challenges are focused on techniques for storage, data mining, spatiotemporal analysis, edge computing, machine and deep learning, object detection or semantic classification, among many others. Advances in this area have a direct impact on broad fields of knowledge such as precision agriculture, ecology or territorial configuration.

Dr. Lidia M. Ortega Alvarado
Dr. María I. Ramos Galan
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 from satellite and UAV (unmanned aerial vehicles) sources
  • massive dataset processing
  • spatiotemporal information systems
  • data analysis including data mining and machine and deep learning
  • imagery and 3D point cloud fusion
  • ecology and precision agriculture

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

Published Papers (2 papers)

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Research

30 pages, 11330 KB  
Article
Distance Transform-Based Spatiotemporal Model for Approximating Missing NDVI from Satellite Data
by Amirhossein Mirtabatabaeipour, Lakin Wecker, Majid Amirfakhrian and Faramarz F. Samavati
Remote Sens. 2025, 17(20), 3399; https://doi.org/10.3390/rs17203399 - 10 Oct 2025
Viewed by 377
Abstract
One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns [...] Read more.
One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article
(This article belongs to the Special Issue Big Geo-Spatial Data and Advanced 3D Modelling in GIS and Satellite)
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27 pages, 17596 KB  
Article
Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning
by Gonzalo Muradás Odriozola, Klaas Pauly, Samuel Oswald and Dries Raymaekers
Remote Sens. 2024, 16(5), 794; https://doi.org/10.3390/rs16050794 - 24 Feb 2024
Cited by 5 | Viewed by 4161
Abstract
Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent [...] Read more.
Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images. Full article
(This article belongs to the Special Issue Big Geo-Spatial Data and Advanced 3D Modelling in GIS and Satellite)
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