2nd International Electronic Conference on Remote Sensing

Welcome from the Chairs

Welcome from the Chair of the 2nd International Electronic Conference on Remote Sensing

22 March to 5 April 2018

It is my great pleasure to welcome you to the 2nd International Electronic Conference on Remote Sensing. ECRS-2 aims to promote and advance the exciting and rapidly changing field of remote sensing and contribute towards outlining the role of earth observation in monitoring the environment for a sustainable future.

During the last decade, we witnessed a remarkable increase in the number of disciplines and activities supported by remote sensing, with Earth Observation data being considered essential to most environmental monitoring activities worldwide. Indeed, remote sensing today is considered critical for addressing key challenges related to climate change, biodiversity loss, land degradation, industrial pollution, natural and anthropogenic hazards (e.g. earthquakes, floods, landslides, fires), water quality and availability, weather forecasting and early warnings, renewable energy, agriculture, forestry and natural ecosystems, coasts and oceans, topographic mapping and, national security among many others. New missions with sensors suitable for a large variety of different applications, progress in computer technology, development of new advanced digital image analysis techniques, improved access to and availability of images (satellite, airborne) andin-situ measurements, as well as the establishment of global initiatives such as the Global Earth Observation System of Systems (GEOSS) are expected to further increase the use of EO data to an even broader array of disciplines.

Hence, it seems that the future of remote sensing lies in providing numerous types of accurate, current, and multi-resolution data and derived geospatial information products readily available for every area of interest.

ECRS-2 covers research in key areas of opportunity and challenge in remote sensing sciences, including:

New Platforms and Sensors
Big Data Handling
New Image Analysis Approaches
Applications
Product Validation
Operational Applications and Services
Open Source Data, Code, Algorithm
Posters: In this section, posters can be presented without an accompanying proceedings paper. Posters will be available online on this website during and after the e-conference. However, will not be added to the proceedings of the conference.

Participants in this multidisciplinary conference will be able to examine, explore and critically engage with issues and advances in these and related areas. We hope that this event will facilitate debates on theoretical and applied aspects of remote sensing, which will yield novel ideas to the field.

The 2nd International Electronic Conference on Remote Sensing will be held at www.sciforum.net/conference/ecrs-2, the platform developed by MDPI to organize electronic conferences and discussion groups.

Accepted papers will be published in the proceedings of the conference, and selected papers will be considered for publication in Remote Sensing, which is an open access journal publication of MDPI in the field of remote sensing (https://www.mdpi.com/journal/remotesensing).

We are looking forward to receiving your contributions in this unique event!

Best regards,

Dr. Ioannis Gitas
Laboratory of Forest Management and Remote Sensing
School of Forestry and Natural Environment
Aristotle University of Thessaloniki, Greece
https://fmrs.web.auth.gr/

Ioannis Gitas is an Associate Professor at the Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, Greece, and an elected fellow of the Cambridge Philosophical Society. His research has focused on remote sensing and GIS applications in environmental monitoring, with emphasis on forest fire management and land cover/land use mapping and change detection. He has been involved in various national and international projects and has long experience working as a consultant in GIS/RS issues for national and international organisations, as well as for the industry. Also, he has served as a project proposal reviewer for a number of national and international research organisations. Dr. Gitas received his PhD and M.Phil. degrees in GIS and Remote Sensing from the Department of Geography, Cambridge University, U.K., and a B.Sc. degree in Forestry and Natural Environment from the Aristotle University of Thessaloniki, Greece. He is an Associate Editor of Remote Sensing and has edited special issues for a number of high impact factor journals. In addition, he has substantial experience in organising international workshops and conferences. Ioannis Gitas is currently the Chair of the Special Interest Group on Forest Fires of the European Association of Remote Sensing Laboratories (EARSeL, FFSIG), the FAO Forest Resources Assessment, Remote Sensing Survey contact point for Greece, and is a member of the GOFC-GOLD Fire Implementation Team.

Sponsors

Sciforum is a platform maintained by MDPI.

Conference Chairs

Ioannis Gitas

[Not defined]

Sessions

A. New Platforms and Sensors
B. Big Data Handling
C. New Image Analysis Approaches
D. Applications
E. Product Validation
F. Operational Applications and Services
G. Recent Trends in UAV Remote Sensing
H. Posters

Instructions for Authors

Procedure for Submission, Peer-Review, Revision and Acceptance of Conference Proceedings Papers

Submissions should be done by the authors online by registering with www.sciforum.net, and using the "Submit New Submission" function once logged into system.

Scholars interested in participating with the conference can submit their abstract (about 200-300 words covering the areas of manuscripts for the proceedings issue) online on this website until 17 December 2017.
The Conference Committee will pre-evaluate, based on the submitted abstract, whether a contribution from the authors of the abstract will be welcome for the 2nd International Electronic Conference on Remote Sensing. All authors will be notified by 20 December 2017 about the acceptance of their abstract.
If the abstract is accepted for this conference, the author is asked to submit his/her manuscript, optionally along with a PowerPoint and/or video presentation of his/her paper (only PDF), until the submission deadline of 10 February 2018.
The manuscripts and presentations will be available on https://sciforum.net/conference/ecrs-2 for discussion and rating during the time of the conference 22 March-5 April 2018 and will be published in Journal Proceedings.
The Open Access Journal Remote Sensing will publish a Special Issue of the conference proceedings papers and accepted papers will be published in the proceedings of the conference itself. Accepted papers will be published in the proceedings of the conference. After the conference, the Conference Committee will recommend manuscripts that may be included for publication in this Special Issue of the journal Remote Sensing (the submission to the journal is independent from the conference proceedings and will follow the usual process of the journal, including peer-review, APC, etc.).

Proceedings Paper

Manuscripts for the proceedings issue must have the following organization:

First page:

Title
Full author names
Affiliations (including full postal address) and authors' e-mail addresses
Abstract
Keywords
Introduction
Methods
Results and Discussion
Conclusions
(Acknowledgements)
References

Manuscripts should be prepared in MS Word or any other word processor and should be converted to the PDF format before submission. The publication format will be PDF. The manuscript should count at least 3 pages (incl. figures, tables and references) and should not exceed 6 pages.

Presentation Slides
Authors are encouraged to prepare a presentation in PowerPoint or similar software, to be displayed online along with the Manuscript. Slides, if available, will be displayed directly in the website using Sciforum.net's proprietary slides viewer. Slides can be prepared in exactly the same way as for any traditional conference where research results can be presented. Slides should be converted to the PDF format before submission so that our process can easily and automatically convert them for online displaying.

Video Presentations
Besides their active participation within the forum, authors are also encouraged to submit video presentations. If you are interested in submitting, please contact the conference organizer at [email protected] to get to know more about the procedure. This is an unique way of presenting your paper and discuss it with peers from all over the world. Make a difference and join us for this project!

Presentation of Posters
Authors that wish to present a poster only, i.e. without proceedings paper, can do so in section H - Posters of this conference. Posters will be available on this conference website during and after the event. Like papers presented on the conference, participants will be able to ask questions and make comments about the posters. Posters that are submitted without paper will not be included in the proceedings of the conference.

Submission of Manuscripts

Submission: Manuscripts should be submitted online at www.sciforum.net/login by registering and logging in to this website.

MS Word: Manuscript prepared in MS Word must be converted into a single file before submission. When preparing manuscripts in MS Word, the Electronic Conference on Remote Sensing Microsoft Word template file (see download below) must be used. Please do not insert any graphics (schemes, figures, etc.) into a movable frame which can superimpose the text and make the layout very difficult.

2nd International Electronic Conference on Remote Sensing Microsoft Word template file and LaTex template file

Manuscript Preparation

Paper Format: A4 paper format, the printing area is 17.5 cm x 26.2 cm. The margins should be 1.75 cm on each side of the paper (top, bottom, left, and right sides).
Paper Length: The conference proceedings paper should not be longer than 6 pages. The conference manuscript should be as concise as possible.
Formatting / Style: The paper style of the Journal Remote Sensing should be followed. You may download the template file to prepare your paper. The full titles and the cited papers must be given. Reference numbers should be placed in square brackets [ ], and placed before the punctuation; for example [4] or [1-3], and all the references should be listed separately and as the last section at the end of the manuscript.
Authors List and Affiliation Format: Authors' full first and last names must be given. Abbreviated middle name can be added. For papers written by various contributors a corresponding author must be designated. The PubMed/MEDLINE format is used for affiliations: complete street address information including city, zip code, state/province, country, and email address should be added. All authors who contributed significantly to the manuscript (including writing a section) should be listed on the first page of the manuscript, below the title of the article. Other parties, who provided only minor contributions, should be listed under Acknowledgments only. A minor contribution might be a discussion with the author, reading through the draft of the manuscript, or performing English corrections.
Figures, Schemes and Tables: Authors are encouraged to prepare figures and schemes in color. Full color graphics will be published free of charge. Figure and schemes must be numbered (Figure 1, Scheme I, Figure 2, Scheme II, etc.) and a explanatory title must be added. Tables should be inserted into the main text, and numbers and titles for all tables supplied. All table columns should have an explanatory heading. Please supply legends for all figures, schemes and tables. The legends should be prepared as a separate paragraph of the main text and placed in the main text before a table, a figure or a scheme.

For further enquiries please contact us at [email protected].

Potential Conflicts of Interest

It is the authors' responsibility to identify and declare any personal circumstances or interests that may be perceived as inappropriately influencing the representation or interpretation of clinical research. If there is no conflict, please state here "The authors declare no conflict of interest." This should be conveyed in a separate "Conflict of Interest" statement preceding the "Acknowledgments" and "References" sections at the end of the manuscript. Financial support for the study must be fully disclosed under "Acknowledgments" section. It is the authors' responsibility to identify and declare any personal circumstances or interests that may be perceived as inappropriately influencing the representation or interpretation of clinical research. If there is no conflict, please state here "The authors declare no conflict of interest." This should be conveyed in a separate "Conflict of Interest" statement preceding the "Acknowledgments" and "References" sections at the end of the manuscript. Financial support for the study must be fully disclosed under "Acknowledgments" section.

MDPI AG, the publisher of the Sciforum.net platform, is an open access publisher. We believe that authors should retain the copyright to their scholarly works. Hence, by submitting a Communication paper to this conference, you retain the copyright of your paper, but you grant MDPI AG the non-exclusive right to publish this paper online on the Sciforum.net platform. This means you can easily submit your paper to any scientific journal at a later stage and transfer the copyright to its publisher (if required by that publisher).

List of accepted submissions (50)

Id	Title	Authors	Presentation
sciforum-015997	Vertical Segmentation of Airborne LiDAR for Select Australian Vegetation Communities John Tasker , Stuart Phinn Submitted: 01 Dec 2017 Abstract: Show Abstract	John Tasker , Stuart Phinn		Show Abstract
A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia’s vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1 – 10 m²), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km², were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5 - 4 m tall. Exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced sub-canopy returns. Further development and application of vertical segmentation methods in multi-story vegetation communities should be evaluated due to the potential for targeted management and monitoring of vegetation communities and wildlife populations.
sciforum-015686	Classification of Sentinel-2 Images Utilizing Abundances Representation Eleftheria Mylona , Vassiliki Daskalopoulou , Olga Sykioti , Konstantinos Koutroumbas , Athanasios Rontogiannis Submitted: 21 Nov 2017 Abstract: Show Abstract	Eleftheria Mylona , Vassiliki Daskalopoulou , Olga Sykioti , Konstantinos Koutroumbas , Athanasios Rontogiannis	N/A	Show Abstract
This paper deals with (both supervised and unsupervised) classification in Sentinel-2 images, utilizing the abundances representation of the pixels of interest. The latter pixel representation uncovers the hidden structured regions which are not available in the reference maps. Additionally, it reduces the dimensionality of the original spectral band space, it encourages data distinctions and bolsters accuracy. The proposed methodology involves two main stages: (I) the determination of the pixels abundance representation and (II) the employment of a classification algorithm applied on the abundance representations. More specifically, stage (I) incorporates two key processes namely: (a) endmember extraction utilizing spectrally homogeneous regions of interest (ROIs) and, (b) spectral unmixing, which hinges upon the endmember selection. The adopted spectral unmixing process assumes the Linear Mixing Model (LMM), where each pixel is expressed as a linear combination of the endmembers. The pixel’s abundance vector is estimated via a variational Bayes algorithm that is based on a suitably defined hierarchical Bayesian model. The resulting abundance vectors are then fed to stage (II) where two off-the-shelf supervised classification approaches (namely nearest neighbor (NN) classification and support vector machines (SVM)) as well as an unsupervised classification process (namely online adaptive possibilistic c-means (OAPCM) clustering algorithm), are adopted. Experiments are performed on a Sentinel-2 image acquired for a specific region of the Northern Pindos National Park in the northwestern Greece containing water, vegetation and soil areas. The experimental results demonstrate that the ad-hoc classification approaches utilizing the abundance representations of the pixels outperform the ones utilizing the spectral signatures of the pixels, in terms of accuracy.
sciforum-016071	Data Mining Using NDVI Time Series Applied to Change Detection Andeise Dutra , Yosio Shimabukuro , Maria Isabel Escada Submitted: 06 Dec 2017 Abstract: Show Abstract	Andeise Dutra , Yosio Shimabukuro , Maria Isabel Escada	N/A	Show Abstract
Information about the land cover and land use of a region are fundamental in studies such as mapping of deforestation and forest degradation. Quantifying and monitoring woody cover distribution in semiarid regions is challenging, due to their scattered distribution. Data mining has been widely used in remote sensing data for information extraction of spectral and temporal data in the analysis of change detection. The main objective of this study was to characterize the land cover and land use over 2000-2010 time period for the Brazilian Caatinga seasonal biome using a temporal NDVI series and Geographic Object-Based Image Analysis. For each of the target years was obtained NDVI images derived from MODIS (MOD13Q1, at 250 m spatial and 16 day temporal scale) sensor during the dry season to predict wood cover in the municipality of Buriti dos Montes, in the state of Piauí, Northeast region of Brazil (H13V09 tile). The images were automatically pre-processed and in the GEOBIA approach was performed image segmentation, spatial and spectral attribute extraction and labelled according to the following legend: Tree Cover (TC) and Cropland/Grass (CG), to obtain a classification using the decision tree supervised algorithm. Our results showed that approach using GEOBIA presented Kappa Index of 0.58 and Global Accuracy (GA) of 0.81% and showed better accuracy for the Tree Cover. Finally, we recommend new studies using a higher spatial resolution data, as well as the addition of other parameters strongly related to vegetation of semiarid regions.
sciforum-016187	Performance Analysis of Detector Algorithms using Drone-Based Radar Systems for Oil Spill Detection Bilal Hammoud , Ghaleb Faour , Hussam Ayad , Fabien Ndagijimana , Jalal Jomaah Submitted: 15 Dec 2017 Abstract: Show Abstract	Bilal Hammoud , Ghaleb Faour , Hussam Ayad , Fabien Ndagijimana , Jalal Jomaah	N/A	Show Abstract
Large tankers collisions with rocky shoals, platform accidents, pipelines ruptures and operative discharges are main contributors to oil pollution in the World’s oceans. Consequently, the release of the petroleum pollutants into coastal waters harms severely the environmental ecosystem. According to the European Space Agency (ESA), an estimate of 4.5 million tons are spilled on annual basis worldwide. Therefore, having oil pollution monitoring system is something crucial for the preservation of the coastal ecosystem. Recent remote sensing techniques combine between aircrafts and satellite surveillance in order to increase the probability of early spill detection, as well as to cover large spill areas. We are working on a project that targets eventually to incorporate MIMO radar on drone for oil spill detection. The project will provide a quick assessment tool for oil spill accidents similar to what happened in the summer of 2006 in Lebanon, where 15000 tons of heavy fuel oil spilled in the Mediterranean Sea. In addition, the MIMO radar drones will be prominent by its providence to high spectral resolution, its allowance to parallel scanning, and its relative low cost. The project is composed of three phases. The first phase deals with the problem formulation and treatment using theoretical calculation and numerical simulations. The second phase deals with the prototype leading to the product in the third phase. As researchers, we are focusing on the first and second phases. Experimental measurements conducted in the laboratories in France are used to validate the models, to study the effectiveness of the algorithms and to provide the prototype. The product development in the final phase is left for application engineers. Specifically for this abstract, we develop radar’s algorithms that take into account both the mathematical and the physical modeling of the sea surface covered by oil slicks. In these detection algorithms, we use the statistical characterization of the power reflectivity and its distribution under various scenarios (noise levels, oil thicknesses and electromagnetic wave’ frequencies). We first propose a single frequency oil spill detector that uses multiple observations of power reflection coefficients over several scanning iterations for the sea area. Increasing the number of observations leads to an increase in the certainty of the detector. Second, we address the correctness and the effectiveness of this detector for different scenarios using Monte- Carlo simulations. Results show the inability of this detector to effectively distinguish between oil slicks and oil-free slicks. An upgrade of this detector is the multi-frequency multi-snapshot detector where several electromagnetic frequencies are used when scanning the area. Performance analysis of the second detector proves its ability to overcome the drawbacks of the first detector by providing accurate detection.
sciforum-015960	Satellite Based Temporal Analysis of Local Weather Elements Along N-S Transect Across Jharkhand, Bihar & Eastern Nepal Shanti Shwarup Mahto , Arvind Chandra Pandey Submitted: 01 Dec 2017 Abstract: Show Abstract	Shanti Shwarup Mahto , Arvind Chandra Pandey		Show Abstract
The study area has chosen along the N-S transect across the Himalaya, Gangetic plains and Chotanagpur plateau. We basically tried to observe the variation in the most important climatic variables i.e. Net Surface Radiation (Rn), Temperature, Rainfall, Evapotranspiration (ET) etc. during 2000-2016. Generally the meteorological parameters shows its variability over a large spatial domain and remains invariable for closer ground based observations. In contrast, the satellite data is capable to present a synoptic view in a single image. Therefore it is more reliable to use the satellite dataset for the study of such meteorological parameters. The TRMM monthly average precipitation (0.25° X 0.25°), MODIS-Terra 8 day average LST product (1km X 1km), MERRA-2 radiation (0.5° x 0.625°) and GLDAS reanalysis model data (0.25°X0.25°) has been used to study and analyse the spatial variability and distribution of rainfall, surface temperature, energy fluxes and evapotranspiration, respectively. The results have shown that the overall annual average rainfall has a gradual decreasing trend. It results after the correlation with temperature (>35°C) that the regions with low rainfall (<1000mm) have to witness warmer temperature conditions (>43°C). The difference in maximum and minimum temperature is increasing at the rate of 1°C per five years. The east-west central line of the Bihar, along the river Ganga is found to be the line of division i.e. almost 80% of the area which witness >35°C temperature lies below this line and few 10-20% lies above it. The results for Rn have shown an overall increasing trend over the period of time. The Nepal has a wider stretch of Rn due to its mountain topography followed by the Jharkhand (plateau) and Bihar (plain). The ET has also an increasing trend over the period of time and the results are noticeable for western Bihar-Jharkhand. There is an upward latitudinal shifting of the low rainfall bands in both the pre-monsoon and monsoon conditions. Due to the lack of availability of ground truth data, we have to restrict with the remotely sensed dataset. Since the dataset chosen are well accepted globally, the data inaccuracy is minimal. Hence, the study will help to understand the fluctuation in local weather phenomenon may useful for adapting sustainable strategies to mitigate the climate change effect on local scale.

Full List

Scientific Advisory Committees

Executive Chairman

Prof. Ioannis Gitas
Laboratory of Forest Management and Remote Sensing
School of Forestry and Natural Environment
Aristotle University of Thessaloniki, Greece

Section Chairs

Section A: New Platforms and Sensors

Adjunct Professor Vincent Ambrosia, Senior Research Scientist / Adjunct Faculty, California State University - Monterey Bay, co-located at: NASA-Ames Research Center, MS 245-4, Bld. 245, Rm. 128, P.O. Box 1, Moffett Field, California, 94035 USA

Prof. Alexander Braun, Department of Geological Sciences and Geological Engineering, Queens's University, Miller Hall, 520 Bruce Wing, Kingston, Ontario, K7L 3N6, Canada

Section B: Big data Handling

Prof. Mingnin Chi, Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, 314-2 Computer Building, 825 Zhang Heng Road, Shanghai 201203, China

Prof. Jón Atli Benediktsson, Faculty of Electrical and Computer Engineering, University of Iceland, Sæmundargata 2, 101 Reykjavik, Iceland

Prof. Mihai Datcu, German Aerospace Center (DLR), Remote Sensing Technology Institute, Photogrammetry and Image Analysis Oberpfaffenhofen, 82234 Weßling, Germany

Section C: New Image Analysis Approaches

Dr. Yuliya Tarabalka, INRIA Sophia Antipolis, 2004, route des Lucioles, 06902 Sophia Antipolis Cedex, France

Dr. Sangram Ganguly, NASA Ames Research Center and Bay Area Environmental Research Institute Bldg. 19, Suite 2031, NASA Ames Research Center, Moffett Field, CA 94035, USA

Section D: Applications

Prof. Mario Caetano, NOVA IMS, Universidade Nova de Lisboa and DGT, Portugal

Dr. Igor Savin, V.V. Dokuchaev Soil Science Institute, Agrarian-Technological Institute of RUDN University, Moscow, Russia

Section E: Product Validation

Prof. Jing M. Chen, Department of Geography and Planning, University of Toronto, Toronto, Canada

Dr. Jesus San Miguel, European Commission, Rue du Champ de Mars 21, 1050 Brussels, Belgium

Section F: Operational Applications and Services

Prof. Iain H. Woodhouse, Edinburgh Earth Observatory, School of GeoSciences, UK

Section G: Recent Trends in UAV Remote Sensing

Dr. Kyriacos Themistocleous, Department of Civil Engineering and Geomatics, Cyprus University of Technology

Scientific Committees

Norman

Prof. Norman Kerle

Department of Earth Systems Analysis (ESA)
Faculty of Geo-Information Science and Earth Observation (ITC)
University of Twente, Enschede, Netherlands

kenvin

Prof. Kevin Tansey

GIS & Remote Sensing Group
Dept. of Geography
University of Leicester, UK

Dr. Yuliya Tarabalka

INRIA Sophia Antipolis
2004, route des Lucioles
06902 Sophia Antipolis Cedex, France

Dr. Francesco Nex

Department of Earth Observation Science (EOS)

ITC Faculty, University of Twente PO Box 217

7500 AE, Enschede, The Netherlands

Prof. Clive Oppenheimer

Department of Geography
University of Cambridge
Downing Place
Cambridge CB2 3EN

Prof. Gui-Song Xia

State Key Lab. LIESMARS,

Wuhan University

No.129, Luoyu Road, Wuhan 430079, China

Prof. Daniele Riccio

Department of Electrical Engineering and Information Technology
Faculty of Engineering
University of Napoli Federico II, Napoli, Italy

Dr. Chandra Giri

Remote Sensing and Spatial Analysis Branch
Office of Research and Development
United States Environmental Protection Agency, NC, USA

Conference Organizers

Chair of the 2nd International Electronic Conference on Remote Sensing

Prof. Ioannis Gitas

Associate Editor of the Journal Remote Sensing
Laboratory of Forest Management and Remote Sensing
School of Forestry and Natural Environment
Aristotle University of Thessaloniki, Greece

Scientific Advisory Committee

Prof. Norman Kerle, Department of Earth Systems Analysis (ESA), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands

Prof. Kevin Tansey, GIS & Remote Sensing Group, Dept. of Geography, University of Leicester, UK

Prof. Daniele Riccio, Department of Electrical Engineering and Information Technology, Faculty of Engineering, University of Napoli Federico II, Napoli, Italy

Dr. Chandra Giri, Remote Sensing and Spatial Analysis Branch, Office of Research and Development, United States Environmental Protection Agency, NC, USA

Prof. Clive Oppenheimer, Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN

Dr. Francesco Nex, Department of Earth Observation Science (EOS), ITC Faculty, University of Twente PO Box 217, 7500 AE, Enschede, The Netherlands

Prof. Gui-Song Xia, State Key Lab. LIESMARS, Wuhan University, No.129, Luoyu Road, Wuhan, 430079, China

Dr. Yuliya Tarabalka, INRIA Sophia Antipolis, 2004, route des Lucioles, 06902 Sophia Antipolis Cedex, France

Adjunct Professor Vincent Ambrosia, Senior Research Scientist / Adjunct Faculty, California State University - Monterey Bay
co-located at: NASA-Ames Research Center, MS 245-4, Bld. 245, Rm. 128, P.O. Box 1, Moffett Field, California, 94035 USA

Prof. Alexander Braun, Department of Geological Sciences and Geological Engineering, Queens's University, Miller Hall, 520 Bruce Wing, Kingston, Ontario, K7L 3N6, Canada

Prof. Mingmin Chi, Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, 314-2 Computer Building, 825 Zhang Heng Road, Shanghai 201203, China