Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Deep Learning for Remote Sensing and Geodata
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Deep Learning for Remote Sensing and Geodata

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: 20 March 2025 | Viewed by 5984

Special Issue Editors

Dr. Levente Klein

E-Mail Website
Guest Editor
IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
Interests: remote sensing; deep learning; big data; geo-spatial data
Special Issues, Collections and Topics in MDPI journals
Dr. Conrad M. Albrecht

E-Mail Website
Guest Editor
TUM School of Engineering and Design, Technical University of Munich, München, Germany
Interests: machine learning and numerical optimization; development of scalable algorithms and compute pipelines for scientific big data analytics; remote sensing archeology; contribution to open-source software
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Martin Werner

E-Mail Website
Guest Editor
Department of Aerospace and Geodesy, Big Geospatial Data Management, Technical University of Munich, Munich, Germany
Interests: big geospatial data management; distribution mathematics; computer learning; image and text analysis; random data structures; high-speed computing; quantum algorithms
Dr. Salman Khan

E-Mail Website
Guest Editor
Computer Vision Faculty, Mohamed bin Zayed University of Artificial Intelligence, Building 1B, Masdar City P.O. Box 5224, Abu Dhabi, United Arab Emirates
Interests: artificial intelligence; deep learning; computer vision; remote sensing

Special Issue Information

Dear Colleagues,

With the volume and the variety of remote sensing data increasing exponentially, it has become possible to observe the same location on the Earth multiple times per day. The richness of remote sensing data enables land-use observation, change and anomaly detection, water quality monitoring and observation of greenhouse gases. While the satellite and aerial data are readily available, one obstacle for the deployment of global-scale Artificial Intelligence (AI) models is the lack of labeled data used for training machine learning algorithms and the lack of benchmark datasets that allow for side-by-side comparison of different models.

In the last decades, AI has evolved from classical machine learning models (Random Forest, Support Vector Machines, etc.) to deep learning (Convolutional Neural Networks, etc.) and then to foundational models. Foundational models based on self-supervised learning trained on massive heterogeneous datasets can be tuned to various downstream tasks on the backbone of the same model. While the foundational models are exposed to different types of data (multispectral, radar, LiDAR, crowdsource data), there is ongoing interest in understanding the performance and generalizability of these models for multiple applications. Of ongoing interest is the comparison of foundational models with classical deep learning models and benchmarking the accuracy and reproducibility of these modes.

We encourage submissions of original manuscripts that focus on scalable AI methodologies and benchmark dataset creation to develop foundational models and to characterize network architecture and applications based on remote sensing data. Topics can include, but are not limited to:

  • Self-supervised neural networks applied to remote sensing data;
  • Development of new algorithms and applications using heterogeneous data sources;
  • Automated data label generation for AI models;
  • Remote sensing of land, water, and air using multiscale deep learning models;
  • Integration of physical and statistical constrain to model continental scale systems;
  • Multi-sensor fusion and deep learning algorithms for detection and anomaly identification;
  • Application of AI models in agriculture, forestry, water quality, food security, and infrastructure monitoring.

Dr. Levente Klein
Dr. Conrad Albrecht
Prof. Dr. Martin Werner
Dr. Salman Khan
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

  • foundational models
  • deep learning
  • artificial intelligence
  • satellite and aerial remote sensing
  • LiDAR and point cloud processing
  • earth digital twin

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

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

Research

16 pages, 5554 KiB  
Article
Improving the ERA5-Land Temperature Product through a Deep Spatiotemporal Model That Uses Fused Multi-Source Remote Sensing Data
by Lei Xu, Jinjin Du, Jiwei Ren, Qiannan Hu, Fen Qin, Weichen Mu and Jiyuan Hu
Remote Sens. 2024, 16(18), 3510; https://doi.org/10.3390/rs16183510 - 21 Sep 2024
Viewed by 743
Abstract
Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook [...] Read more.
Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Figure 1

19 pages, 12043 KiB  
Article
Collection of a Hyperspectral Atmospheric Cloud Dataset and Enhancing Pixel Classification through Patch-Origin Embedding
by Hua Yan, Rachel Zheng, Shivaji Mallela, Randy Russell and Olcay Kursun
Remote Sens. 2024, 16(17), 3315; https://doi.org/10.3390/rs16173315 - 6 Sep 2024
Viewed by 589
Abstract
Hyperspectral cameras collect detailed spectral information at each image pixel, contributing to the identification of image features. The rich spectral content of hyperspectral imagery has led to its application in diverse fields of study. This study focused on cloud classification using a dataset [...] Read more.
Hyperspectral cameras collect detailed spectral information at each image pixel, contributing to the identification of image features. The rich spectral content of hyperspectral imagery has led to its application in diverse fields of study. This study focused on cloud classification using a dataset of hyperspectral sky images captured by a Resonon PIKA XC2 camera. The camera records images using 462 spectral bands, ranging from 400 to 1000 nm, with a spectral resolution of 1.9 nm. Our preliminary/unlabeled dataset comprised 33 parent hyperspectral images (HSI), each a substantial unlabeled image measuring 4402-by-1600 pixels. With the meteorological expertise within our team, we manually labeled pixels by extracting 10 to 20 sample patches from each parent image, each patch consisting of a 50-by-50 pixel field. This process yielded a collection of 444 patches, each categorically labeled into one of seven cloud and sky condition categories. To embed the inherent data structure while classifying individual pixels, we introduced an innovative technique to boost classification accuracy by incorporating patch-specific information into each pixel’s feature vector. The posterior probabilities generated by these classifiers, which capture the unique attributes of each patch, were subsequently concatenated with the pixel’s original spectral data to form an augmented feature vector. We then applied a final classifier to map the augmented vectors to the seven cloud/sky categories. The results compared favorably to the baseline model devoid of patch-origin embedding, showing that incorporating the spatial context along with the spectral information inherent in hyperspectral images enhances the classification accuracy in hyperspectral cloud classification. The dataset is available on IEEE DataPort. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Graphical abstract

18 pages, 232655 KiB  
Article
SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation
by Gyutae Hwang, Jiwoo Jeong and Sang Jun Lee
Remote Sens. 2024, 16(17), 3278; https://doi.org/10.3390/rs16173278 - 3 Sep 2024
Viewed by 973
Abstract
Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual [...] Read more.
Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Figure 1

19 pages, 14227 KiB  
Article
A New Multimodal Map Building Method Using Multiple Object Tracking and Gaussian Process Regression
by Eunseong Jang, Sang Jun Lee and HyungGi Jo
Remote Sens. 2024, 16(14), 2622; https://doi.org/10.3390/rs16142622 - 18 Jul 2024
Cited by 1 | Viewed by 894
Abstract
Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in [...] Read more.
Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Graphical abstract

15 pages, 3521 KiB  
Article
Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values
by Jovan M. Tadić, Velibor Ilić, Slobodan Ilić, Marko Pavlović and Vojin Tadić
Remote Sens. 2024, 16(10), 1707; https://doi.org/10.3390/rs16101707 - 11 May 2024
Viewed by 951
Abstract
Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often [...] Read more.
Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Figure 1

21 pages, 19045 KiB  
Article
Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy
by Lei Wang, Yingjun Xu, Qiang Chen, Jidong Wu, Jianhui Luo, Xiaoxuan Li, Ruyi Peng and Jiaxin Li
Remote Sens. 2024, 16(7), 1161; https://doi.org/10.3390/rs16071161 - 27 Mar 2024
Cited by 1 | Viewed by 859
Abstract
The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel [...] Read more.
The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method’s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop