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 8076
Special Issue Editors
Interests: data science; spatial data science; remote sensing; information systems
Interests: GIS; regional science; spatial analysis; land use change; complex systems
Special Issues, Collections and Topics in MDPI journals
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 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. Information is an international peer-reviewed open access monthly journal published by MDPI.
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