Special Issue "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: 30 June 2021.

Special Issue Editors

Dr. Fernando Bação
Website
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
Prof. Dr. Eric Vaz
Website
Guest Editor
Department of Geography and Environmental Studies and the Graduate Program Director for the Master of Spatial Analysis at Ryerson University, Jorgensen Hall, JOR620, CA
Interests: GIS; regional science; spatial analysis; land use change; complex systems
Special Issues and Collections in MDPI journals
Dr. Bruno Damasio
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

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 papers will be 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. Information 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 1400 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.

Published Papers (1 paper)

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Research

Open AccessArticle
Remote Sensing Image Change Detection Using Superpixel Cosegmentation
Information 2021, 12(2), 94; https://doi.org/10.3390/info12020094 - 23 Feb 2021
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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