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Special Issue "Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editor

Prof. Dr. Alfred Colpaert
Website
Guest Editor
Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 7, Metria-building, P.O. Box 111, FI-80101 Joensuu, Finland
Interests: physical geography; geology; geoinformatics; UAVs

Special Issue Information

Dear Colleagues,

Satellite and UAV (Unmanned Aerial Vehicle) imagery has become an important source of data for Geographic Information Systems (GISs). Remote Sensing and GISs are part of the broader concept of Geoinformatics. Satellite imagery in a wide range of spatial, spectral, and temporal resolutions provides the scientific community with rapidly available global data to be used as an integral part of spatial data structures and analyses. Remote sensing platforms, such as Modis and Landsat, have a unique historical record of providing tens of years of uninterrupted global data.

For local applications, the rapid evolution of unmanned aerial vehicles and lightweight sensors has provided the scientific community with a tool for acquiring extremely high-resolution data covering areas that vary from several hectares to hundreds of square kilometres in size.

The Special Issue intends to highlight advances in satellite and UAV data applications and the use of these to expand and improve data integration in Geoinformatics. UAVs and drones are often cost-effective when compared to the use of manned helicopters or fixed-wing aircraft.

Possible topics include, but are not limited to:

  • ecosystem monitoring;
  • vegetation monitoring;
  • forest inventory;
  • animal habitat analysis;
  • land use/land cover analysis and monitoring;
  • hyperspectral and three-dimensional (3D) mapping;
  • urban mapping and planning;
  • mapping;
  • open source software for unmanned aerial vehicle (UAV) mosaicking and 3D solutions.

Prof. Dr. Alfred Colpaert
Guest Editor

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. Sensors 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 2000 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

  • hyperspectral data
  • multispectral data
  • monochromatic data
  • radar data applications
  • data integration and fusion
  • new UAV platforms and instruments
  • automated mapping and updating
  • real-time applications.

Published Papers (1 paper)

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Research

Open AccessArticle
Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network
Sensors 2020, 20(7), 1832; https://doi.org/10.3390/s20071832 - 26 Mar 2020
Abstract
Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic [...] Read more.
Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency. Full article

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Cumulative effects of infrastructure and human disturbance: a case study within a semi-domesticated reindeer herd

Authors: Sindre Eftestøl, Diress Tsegaye, Kjetil Flydal and Jonathan E.Colman

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