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Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective.

Social safety, security, and comfort of school-going children during their trip to school have become a subject of anxiety to the parents. It has become a crucial issue in recent times, specially in developing nations. Community-based transport can significantly address social security issues in travel at a reasonable cost and reduce the burden on private mode of transportation. In Dhaka city, school van service already exists to serve school-going children but the service has not been proven as an efficient and formal mode of transport to solve mobility problems. In this connection, this study seeks to identify the problems and prospects of the existing school van service and provide a unique, healthy, safe, and reliable transport mode for children. Applying different tools of the Participatory Rural Appraisal (PRA) method, the problems and solutions have been gleaned from the community. The findings of this study can be applied to a community-managed para-transit systems to improve utility and hence to be useful in resolving issues associated with local transportation. With widespread use, this service has the potential to reduce traffic congestion on major thoroughfares, especially during rush hour.

Purpose Social media creates a public sphere, which functions as the third wheel of governance. This study aims to examine the relationship between Facebook penetration and the World Bank’s World Governance Indicators. Design/methodology/approach First, a Pearson correlation was estimated between Facebook penetration and governance indicators. Second, ordinary least squares analysis was used to examine a variety of additional economic and population factors, including Facebook penetration. In both instances, the colinearity of the variables is examined. Findings The findings indicate that there is no conclusive correlation between Facebook penetration and governance; however, the opposite is true for developing, emerging and least developed countries, though the relationship is not uniform across regions. In addition, per capita gross domestic product and population dynamics, specifically the proportion of the population aged 15–64, have a significant impact on governance measures. The colinearity of the variables suggests that governance is a broad concept, and that the direct correlation between Facebook penetration and governance may be misleading. Research limitations/implications Future research should incorporate panel data from other social media platforms, such as Twitter and Reddit, to better understand temporal factors and the relationship between social media penetration and governance. Practical implications This paper opens up new avenue for investigation on the impact of social media on governance. Social implications This paper can contribute to the complexity of social media as means for voice and accountability. In addition, the paper mentions how social media can be used more neutrally to ensure exposure to diverse perspectives. Originality/value The growing importance of the internet and the popularity of social networking websites are generating a great deal of scholarly attention. To the best of the authors’ knowledge, there is almost no literature that links Facebook penetration and governance. This paper intends to fill this void.