Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review

Deforestation poses a critical global threat to Earth’s ecosystem and biodiversity, necessitating effective monitoring and mitigation strategies. The integration of deep learning with remote sensing offers a promising solution for precise deforestation segmentation and detection. This paper provides a comprehensive review of deep learning methodologies applied to deforestation analysis through satellite imagery. In the face of deforestation’s ecological repercussions, the need for advanced monitoring and surveillance tools becomes evident. Remote sensing, with its capacity to capture extensive spatial data, combined with deep learning’s prowess in recognizing complex patterns to enable precise deforestation assessment. Integration of these technologies through state-of-the-art models, including U-Net, DeepLab V3, ResNet, SegNet, and FCN, has enhanced the accuracy and efficiency in detecting deforestation patterns. The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. Additionally, attention mechanisms combat overfitting, while group and shuffle convolutions further enhance accuracy by reducing dominant filters’ contribution. These strategies collectively fortify the robustness of deep learning models in deforestation analysis. The integration of deep learning techniques into remote sensing applications serves as an excellent tool for deforestation identification and monitoring. The synergy between these fields, exemplified by the reviewed models, presents hope for preserving invaluable forests. As technology advances, insights from this review will drive the development of more accurate, efficient, and accessible deforestation detection methods, contributing to the sustainable management of the planet’s vital resources

OBE course analysis and learning reflections / Arnida Jahya...[et al.]

This paper aims to share the findings of outcome-based education (OBE) analysis done on Human Resource Development (HRM549) course from the Faculty of Business Management that was conducted in one of the public universities. Generally, the curriculum documentation of an academic program includes written program educational objectives (PEOs) at the program level together with the course information for all courses in the program. Course information is a document that provides the details of the course outcomes, teaching methodology, assessment, details of students learning time (SLT), program outcomes-course outcomes (POs-COs) matrix, taxonomy and soft skills matrix that serve as an official guideline to a lecturer to conduct the course, deliver the learning activities and assessment of the students. This OBE course analysis was done as a part of an assignment for the Certificate of Education program conducted by Institute of Leadership and Quality Management (ILQAM) in 2014. Findings of the analysis on this HRM549 course showed that there were several items were not mapped to the course learning outcomes. Henceforth, it was recommended that the lecturer should identify the key important elements in the course analysis that will guide them to map the course learning outcomes with program educational objectives. In conclusion, doing OBE course analysis provides an insight of how the curriculum of a course is being designed (or formulated) using OBE approach

An inside view on Blended Learning: The relationship between blended learning and service quality / Norida Abu Bakar...[et al.]

The purpose of this paper is to review the application and the development of blended learning from the student perspective. Blended learning combines both the classroom and technology to engage learners in meaningful learning experiences, has led to new teaching models and learning styles that embraces the latest technology.As the number of the student intake are growing, it is important to the university to identify the level of saticfaction on Blended learning. The paper use quantitative research methodology to answer the three research questions of this study. Both descriptive and inferential statistics were used. Data collected was sorted, classified, coded and tabulated for an ease of analysis by using student statistical package SPSS 20.The result shows only two variable are significant to the blended learning. The conclusion shows the topic on blended learning was good because in the developmental stage and it needs effective improvement from the organization in terms of infrastructure and training of instructors and learners with efficient skills in teaching and learning online

A comparative analysis on multiple intelligences between science technology and social sciences students at Universiti Teknologi MARA / Nor Fuzaina Ismail...[et al.]

This paper describes the comparison of multiple intelligence indexes between science technology students and social science students at Universiti Teknologi MARA. The multiple intelligence indexes were based on Howard Gardner's MI Model and Walter Mackenzie's Questionnaires. It was found that science social students attained higher MI index as compared to science technology students. The multiple intelligence profiling represents a significant departure from the traditional view of intelligence between these two groups. It is hoped that the indexes could offer a key consideration to match the learning strategies approaches by giving some insights about students' strength and weakness