scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

scholarly journals THE ROLE OF PARENTS IN BOLO VILLAGE IN SUPPORTING DISTANCE LEARNING DURING THE COVID-19 PANDEMIC

2021 ◽  
Vol 4 (2) ◽  
pp. 69-75
Author(s):  
Ida Mawaddah ◽  
Zulhafriliya Zulhafriliya ◽  
Sudarsono Sudarsono
Keyword(s):  
Distance Learning ◽  
Junior High School ◽  
Healthy Lifestyle ◽  
Family Values ◽  
Learning Approaches ◽  
School Aged Children ◽  
Distant Learning ◽  
Role Of Parents ◽  
The Impact

The Indonesian government was concerned about the impact of a wider spread of Covid-19, so it moved quickly to break the chain of transmission by urging people to live a healthy lifestyle, avoid crowds, and keep a safe distance. As a result, the circumstance has a significant impact on education and learning. The goal of this study is to learn more about the role of parents in promoting distant learning and to identify the characteristics that encourage and limit distance learning in Bolo Village during the Covid-19 outbreak. Students are forced to study from home due to government regulations. Teachers can ensure students' learning activities in a variety of ways, one of which is by involving parents as the primary companion of students when they are at home. The participants in this study were parents and their junior high school-aged children. The information was gathered through organized interviews utilizing the researcher's prepared questions. The data in this study was analyzed utilizing qualitative data analysis approaches such as the Miles and Huberman model, which features a cycle that includes data reduction, data presentation, verification, and conclusion drafting. According to the findings of this study, parents in Bolo Village played four roles in supporting learning from home during the Covid-19 pandemic: 1) accompanying children in learning, 2) intense communication with children, 3) providing supervision to children, and 4) educating and supporting children motivation. The supporting variables discovered are: 1) responsibility, 2) family values, and 3) availability to satisfy the needs of children. There are also barriers, such as 1) internet network issues, 2) too much workload, and 3) boring and less diversified learning approaches.

Social or Group Learning Theories

2011 ◽  
pp. 1630-1633
Author(s):  
Gary A. Berg
Keyword(s):  
Distance Learning ◽  
Learning Process ◽  
Group Learning ◽  
Social Activity ◽  
Learning Theories ◽  
Learning Approaches ◽  
Self Reliance ◽  
Educational Environments ◽  
Computer Based ◽  
Learning Institutions

In both computer-based and traditional educational environments, there has been a growing organization of learning in groups with an increased use of teams and group projects (Berg, 2003). Goldman (1999) claims that traditionally education is seen as an activity of isolated thinkers pursuing truth in a spirit of American self-reliance. However, in practice education is very much a social activity, especially the research component that is heavily dependent on colleagues. In fact, some argue that the key to the learning process as a whole is the interaction among students, and between faculty and students (Palloff & Pratt, 1999). Group learning approaches have been widely adopted by many of the leading distance learning institutions, and consequently an understanding of this approach is important.

scholarly journals Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3929 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Anastasia Aidini ◽  
Konstantina Fotiadou ◽  
Michalis Giannopoulos ◽  
Anastasia Pentari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Body ◽  
Super Resolution ◽  
Imaging Systems ◽  
Learning Approaches ◽  
Language Understanding ◽  
Learning Tasks ◽  
Trade Offs ◽  
Sensing Platforms

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

scholarly journals Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

scholarly journals Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1809
Author(s):  
Muhammad Huzaifah Mohd Roslim ◽  
Abdul Shukor Juraimi ◽  
Nik Norasma Che’Ya ◽  
Nursyazyla Sulaiman ◽  
Muhammad Noor Hazwan Abd Manaf ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weed Management ◽  
Management Practices ◽  
Crop Yields ◽  
Supply And Demand ◽  
Learning Approaches ◽  
Future Challenges ◽  
Aerial Vehicle ◽  
Remote Sensing Systems ◽  
Remote Sensing Techniques

Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

scholarly journals Gather.town: An opportunity for self-paced learning in a synchronous, distance-learning environment

2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Colin Derek McClure ◽  
Paul N Williams
Keyword(s):  
Data Analysis ◽  
Distance Learning ◽  
Teaching Experience ◽  
Learning Approaches ◽  
Learning Context ◽  
Online Systems ◽  
Face To Face ◽  
Key Skills ◽  
Limited Opportunity

The COVID-19 pandemic has forced Higher Education to adopt distance-learning approaches in traditionally face-to-face and practical-based fields such as the Health and Life sciences. Such an abrupt change to distance-learning contexts brings a variety of challenges to student learning communities, and ensuring key skills are effectively transferred. Chief among these is the limited opportunity students have to discuss their individual needs with their educators and peers in a synchronous manner. Proximity-based video-conferencing platforms such as gather.town can offer a unique opportunity for learners to interact with educators as well as pre-developed materials in a self-paced manner to tailor the teaching experience, and develop these relationships in a distance-learning context. In this case study the concepts of statistical analysis and the use of the data analysis software R is introduced to 38 University students using the online platform gather.town. With the use of private spaces, pre-recorded videos, and demonstrators, students are trained in both the concepts and practical skills to undertake data analysis in a self-paced manner. Both students and demonstrators provide their opinions on the effectiveness of the platform, and identify its benefits, preferring it to alternative online systems such as MS Teams for their educational sessions.

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