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Advances in Remote Sensing and Geographic Information Science and Their Uses in Geointelligence

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 8688

Special Issue Editors


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Guest Editor
Contoy 137, Col. Lomas de Padierna, Tlalpan 14240, CDMX, Mexico
Interests: geovisual analytics; data visualization; spatial and urban simulation; participatory processes; volunteer geographic information; geodemography

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Guest Editor
Parque Científico Tecnológico Yucatán Carretera Sierra Papacal - Chuburná Pto. Km 5 Sierra Papacal, Mérida C.P. 97302, Yucatán, Mexico
Interests: text mining; geoparsing; natural language processing; computational linguistics

E-Mail Website
Guest Editor
Parque Científico Tecnológico Yucatán Carretera Sierra Papacal - Chuburná Pto. Km 5 Sierra Papacal, C.P. 97302, Mérida, Yucatán, Mexico
Interests: pattern recognition; computer vision; similarity measures; expert systems

E-Mail Website
Guest Editor
Parque Científico Tecnológico Yucatán Carretera Sierra Papacal - Chuburná Pto. Km 5 Sierra Papacal, C.P. 97302, Mérida, Yucatán, Mexico
Interests: remote sensing; interferometry; computer vision; deep learning; machine learning

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Assistant Guest Editor
Parque Científico Tecnológico Yucatán Carretera Sierra Papacal - Chuburná Pto. Km 5 Sierra Papacal, C.P. 97302, Mérida, Yucatán, Mexico
Interests: synthetic aperture radar; interferometry; physical systems; applied mathematics

Special Issue Information

Dear Colleagues,

Many of today’s world problems are related to the geographic distribution of population, its activities, and available resources. Climate change, social inequality, or citizen security have a strong spatial component apart from their own characteristics. For this reason, the need to generate new knowledge through scientific research that will serve as the basis for implementing evidence-based public policies becomes evident. This new knowledge is generated through human capital formed in interdisciplinary ways, capable of working in all aspects of geospatial information management, from its acquisition and storage to its analysis, representation, and visualization.

In pushing this goal, many educational programs have now incorporated topics such as remote sensing, spatial analysis, and geographic information systems/science (GIS/Sc). This, in turn, has generated a growing interest in research of topics related to geospatial information analysis.

The responsible management and social use of geospatial information play important roles in today’s society, science, and government. Taking social demands and priorities to guide scientific research helps achieve a successful balance between the production of knowledge and its direct use by different social actors through collaborative projects.

The aim of this Special Issue is to showcase different research papers that articulate scientific developments that are tied to societal needs and are centered around the issues addressed by geospatial information sciences. However, this Special Issue welcomes the submission of contributions dealing with topics related, but not limited, to remote sensing and image processing, computational geointelligence, spatial analysis and modeling, earth sciences and natural resources, and urban studies, network analysis, and mobility.

Dr. Rodrigo Tapia-McClung
Dr. Alejandro Molina Villegas
Prof. Dr. Oscar Gerardo Sánchez Siordia
Mr. Alejandro Téllez Quiñones
Dr. Adán Salazar-Garibay
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. 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 2700 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

  • Remote Sensing
  • Computational Geointelligence
  • Geospatial Information Science
  • Data Mining

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Published Papers (2 papers)

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19 pages, 1699 KiB  
Article
Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text
by Edwin Aldana-Bobadilla, Alejandro Molina-Villegas, Ivan Lopez-Arevalo, Shanel Reyes-Palacios, Victor Muñiz-Sanchez and Jean Arreola-Trapala
Remote Sens. 2020, 12(18), 3041; https://doi.org/10.3390/rs12183041 - 17 Sep 2020
Cited by 18 | Viewed by 4584
Abstract
The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task [...] Read more.
The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task could be approached through a machine learning approach, in which case a model is trained to recognize a sequence of characters (words) corresponding to geographic entities. The second task consists of assigning such entities to their most likely coordinates. Frequently, the latter process involves solving referential ambiguities. In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation. Once place names are recognized in an input text, they are solved using a grammar, in which a set of rules specifies how ambiguities could be solved, in a similar way to that which a person would utilize, considering the context. As a result, we have an assignment of the most likely geographic properties of the recognized places. We propose an assessment measure based on a ranking of closeness relative to the predicted and actual locations of a place name. Regarding this measure, our method outperforms OpenStreetMap Nominatim. We include other assessment measures to assess the recognition ability of place names and the prediction of what we called geographic levels (administrative jurisdiction of places). Full article
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16 pages, 2438 KiB  
Technical Note
Assessment of Wind Direction Estimation Methods from SAR Images
by Alexandre Corazza, Ali Khenchaf and Fabrice Comblet
Remote Sens. 2020, 12(21), 3631; https://doi.org/10.3390/rs12213631 - 5 Nov 2020
Cited by 8 | Viewed by 3198
Abstract
Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient [...] Read more.
Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area. Full article
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