Special Issue "Data Science in Remote Sensing"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (1 May 2020).
Interests: remote sensing; functional traits; biodiversity; data mining
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Remote Sensing of Inland Waters and Their Catchments
Topical Collection in Remote Sensing: Teaching and Learning in Remote Sensing
Special Issue in Remote Sensing: Upscaling and Downscaling Modelling and/or Identification of Relevant Scales and Thresholds for Environmental Impacts in Ecology by Remote Sensing
Special Issue in Remote Sensing: Monitoring of Status and Disturbances of Bio- and Geodiversity, Their Traits and Interactions Using Remote Sensing
Special Issue in Remote Sensing: Thermal Infrared Remote Sensing for the Climate Adaption of Landscapes and Urban Areas
Interests: linked open data; knowledge graphs; data mining; data analytics; data profiling for Web data; data quality in (Linked) open data; representation learning for knowledge graphs; anomaly detection
Interests: image computing; image analysis; data science; machine learning; interactive machine learning in geosciences; graph-based analyses; data integration; data uncertainty
Scientific methods in Remote Sensing (RS) are changing because of the impact of information technology and data science advancements to extract knowledge or insights from data. The data deluge, triggered by ever-increasing data acquisition rates in combination with data curation improvements, leads to scientific methods using complex machines, which, on the one hand, requires access to comprehensive data sources and archives and, on the other hand, also supplements the linking via Linked Open Data (LOD) with a fundamental understanding of the information stored there by machines.
Open Science has been pioneered by the provision and implementation of open data and data policy of RS data and data products, respectively, for example like Landsat TM/ETM+, the Copernicus-RS mission with Sentinel 1–6, the EnMAP-hyperspectral imager mission, and the opening of further archived data and newly-recorded RS data from IRS-1C-, IRS-1D-, Resourcesat-1, Resourcesat-2, and Cartosat-1 missions. Moreover, LOD technology links complex spatial data taken from local in-situ measurements right up to regionally and globally-linked data. The philosophy behind this is that free access to data (Open Data) constitutes an enormous gain in information for understanding complex patterns and multidimensional processes in ecosystems and, ultimately, for acquiring a better understanding of the complexity of our landscape and the processes going on in it.
The following Special Issue focuses on Data Science and Linked Open Data technology in remote sensing. The following topics are considered for this Special Issue:
- Data Science, Open Science techniques in RS
- Linked Open Data (LOD), Linked Data, Spatial Linked Open Data (SLOD) approaches in RS
- Classification of RS Data with LOD, Semantics and Ontology approaches
- Machine Learning and Data Mining on Linked Data with integration of RS Data
- Methods of Phenotyping, Datafication, Semantification, Ontologization in RS
- Thematic Exploitation Platforms (TEP), Open Science Cloud approaches by RS
- Cloud- and web-based multidimensional, multi-complex and multi-source approaches by RS
- Design, development and evaluation of web- and cloud-based architectures, services and applications in RS
- Neogeography, neoecosystems and map mashups; new RS theories in coupling with other and complex resources and interfaces for web mapping
- Quality assessment of web-based RS information sources, processes and applications
- Application, development and utilization of virtual globes for geospatial data sharing, integration, visualization and analysis by RS data and platforms
- Mobile and location-based search and services; adaptive, context-aware, multidimensional, augmented reality
- Web-based geovisualisation and virtual geospatial RS based environments for dynamics in space-time phenomena on different spatial scales
- Open source solutions and open standards, specifications of RS data
- Geospatial web semantics and ontology; intelligent web mapping/RS services
- Web/cloud-based geospatial database management with RS information
- Anomaly and Outlier Detection in RS
- Multidimensional image computing and multidimensional machine learning techniques by RS
Prof. Dr. Heiko Paulheim
Dr. Hendrik Paasche
Dr. Sebastian Scheuer
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. 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 2400 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.
- Data Mining Linked Open Data
- Spatial Linked Data
- Metadata Generation
- Knowledge Graphs
- Multidimensional image computing
- Multidimensional machine learning techniques
- Anomaly and Outlier Detection