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Remote Sensing
Special Issues
Data Science, Artificial Intelligence and Remote Sensing
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Data Science, Artificial Intelligence and Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 11424

Special Issue Editors

Prof. Dr. Heiko Balzter

E-Mail Website
Guest Editor
NERC National Centre for Earth Observation, Leicester Institute for Space and Earth Observation, School of Geography, Geology and Environment, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: landscape and climate research; land surface modelling; terrestrial remote sensing; synthetic aperture radar (SAR); light detection and ranging (LIDAR); forest monitoring, carbon cycle and climate change
Special Issues, Collections and Topics in MDPI journals
Prof. Ivan Tyukin

E-Mail Website
Guest Editor
Department of Mathematics, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: data science and AI

Special Issue Information

Dear Colleagues,

Data science, the multidisciplinary field of developing algorithms and approaches for deriving insights from big data, artificial intelligence (AI), the simulation of human intelligence with computers, and remote sensing, the collection of large amounts of data from a distance, have all witnessed rapid advances. This Special Issue invites manuscripts that present new data science or AI approaches for deriving inferences from remote sensing data or apply existing data science or AI methods to challenging problems in the broad area of remote sensing. There are no constraints regarding the field of application. Rather, this Special Issue will present the state-of-the-art in data science and AI for the analysis of remote sensing data across various application domains.

Related References

  1. Balzter, H.; Cole, B.; Thiel, C.; Schmullius, C.; Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens. 2015, 7, 14876-14898.
  2. Onojeghuo, A.O.; Blackburn, G.A.; Wang, Q.; Atkinson, P.M.; Kindred, D.; Miao, Y. Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. J. Remote Sens. 2018, 39,1042-1067.
  3. Gorban, A.N.; Tyukin, I.Y. Blessing of dimensionality: mathematical foundations of the statistical physics of data. Trans. R. Soc. 2018, 376, doi:10.1098/rsta.2017.0237.

Prof. Heiko Balzter
Prof. Ivan Tyukin
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

  • data science
  • artificial intelligence
  • machine learning
  • uncertainty quantification
  • remote sensing
  • high-dimensional data analysis

Published Papers (2 papers)

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Research

31 pages, 60219 KiB  
Article
Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images
by Clément Dechesne, Pierre Lassalle and Sébastien Lefèvre
Remote Sens. 2021, 13(19), 3836; https://doi.org/10.3390/rs13193836 - 25 Sep 2021
Cited by 19 | Viewed by 5654
Abstract
In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are [...] Read more.
In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases. Full article
(This article belongs to the Special Issue Data Science, Artificial Intelligence and Remote Sensing)
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27 pages, 6796 KiB  
Article
Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks
by Alysha van Duynhoven and Suzana Dragićević
Remote Sens. 2019, 11(23), 2784; https://doi.org/10.3390/rs11232784 - 26 Nov 2019
Cited by 13 | Viewed by 3957
Abstract
Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as [...] Read more.
Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC. Full article
(This article belongs to the Special Issue Data Science, Artificial Intelligence and Remote Sensing)
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