Remote Sensing and Spatial Data Science

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 7904

Special Issue Editors


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Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: data science; spatial data science; remote sensing; information systems

E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: econometrics; financial time series; stochastic processes; nonlinear time series; statistics

Special Issue Information

Dear Colleagues,

The intersection between spatial data science and remote sensing holds the key to the solution of many world challenges. It is urgent to harness the ever more present and powerful land observation technology to mitigate problems such as wildland fires, deforestation, ocean and water resources monitorization, resource depletion, sustainable urbanization, and human settlements. This is pivotal to creating a more harmonious and sustainable future. Much of the quality of the interactions between humans and the environment relies on our ability to understand the impact of human activities on the environment. This is the only way to design and implement strategies that can lessen our environmental footprint. To achieve this, it is indispensable to tightly couple remote sensing, as a data acquisition technology, and spatial data science, as the appropriate toolbox, to make sense of spatially distributed data. There is urgency in bringing together these two fields, weaving successful strategies to ensure environmental sustainability while promoting sensible growth and development. Doing more with less will be essential for harmonious and balanced interactions between humans and the environment. However, this can only be achieved through the development of an information-rich environment that can support decision-making based on evidence and knowledge.

This Special Issue will accept original research papers on both applications and methodologies, as long as they focus on the intersection between remote sensing and spatial data science. The empirical outlets are within a wide range and often multidisciplinary, such as

  • Remote sensing for the smart city;
  • Urbanization and settlements;
  • Land cover and land use;
  • Agriculture;
  • Wildland fire;
  • Climate change;
  • Ocean monitorization;
  • Deforestation;
  • Archaeological prospection;
  • Heritage preservation;
  • Regional impact analysis;
  • Smart cities;
  • Spatial analysis of LIDAR data.

Concerning methodological approaches, the emphasis on spatial data science includes but is not limited to the following:

  • Automatic classification;
  • Machine learning;
  • Deep neural networks;
  • Time series;
  • Data fusion;
  • Outlier detection;
  • Change detection;
  • Efficient training sets;
  • Learning from imbalanced data;
  • Regression techniques;
  • Quasi-experimental methods;
  • Data preprocessing;
  • Feature extraction and engineering;
  • Land use/land cover change;
  • Mixed methods.

Dr. Fernando Bação
Prof. Dr. Eric Vaz
Dr. Bruno Damasio
Guest Editors

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Published Papers (2 papers)

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Research

20 pages, 3032 KiB  
Article
Improving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatures
by Joao Fonseca, Georgios Douzas and Fernando Bacao
Information 2021, 12(7), 266; https://doi.org/10.3390/info12070266 - 29 Jun 2021
Cited by 19 | Viewed by 3790
Abstract
Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for several years, but [...] Read more.
Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for several years, but it is still fraught with technical challenges. One such challenge is the imbalanced nature of most remotely sensed data. The asymmetric class distribution impacts negatively the performance of classifiers and adds a new source of error to the production of these maps. In this paper, we address the imbalanced learning problem, by using K-means and the Synthetic Minority Oversampling Technique (SMOTE) as an improved oversampling algorithm. K-means SMOTE improves the quality of newly created artificial data by addressing both the between-class imbalance, as traditional oversamplers do, but also the within-class imbalance, avoiding the generation of noisy data while effectively overcoming data imbalance. The performance of K-means SMOTE is compared to three popular oversampling methods (Random Oversampling, SMOTE and Borderline-SMOTE) using seven remote sensing benchmark datasets, three classifiers (Logistic Regression, K-Nearest Neighbors and Random Forest Classifier) and three evaluation metrics using a five-fold cross-validation approach with three different initialization seeds. The statistical analysis of the results show that the proposed method consistently outperforms the remaining oversamplers producing higher quality land cover classifications. These results suggest that LULC data can benefit significantly from the use of more sophisticated oversamplers as spectral signatures for the same class can vary according to geographical distribution. Full article
(This article belongs to the Special Issue Remote Sensing and Spatial Data Science)
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23 pages, 2705 KiB  
Article
Remote Sensing Image Change Detection Using Superpixel Cosegmentation
by Ling Zhu, Jingyi Zhang and Yang Sun
Information 2021, 12(2), 94; https://doi.org/10.3390/info12020094 - 23 Feb 2021
Cited by 11 | Viewed by 3301
Abstract
The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between [...] Read more.
The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original. Full article
(This article belongs to the Special Issue Remote Sensing and Spatial Data Science)
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