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Urbanization is a defining feature of the 21st century, with Bangladesh experiencing rapidurban growth, particularly in its capital city, Dhaka. As urban areas expand, effective urbanplanning becomes crucial for ensuring sustainable development and addressing challengesrelated to infrastructure, housing, transportation, and environmental degradation. Monitoringand Evaluation (M&E) are essential tools in urban planning processes, providing feedback onproject implementation, assessing impact, and guiding decision-making. However, inBangladesh, the integration of M&E in urban planning faces challenges such as limitedinstitutional capacity, data deficiencies, and inadequate stakeholder engagement. This articleexplores the current state of M&E integration in urban planning in Bangladesh, identifieschallenges and gaps, and proposes strategies for improvement. By enhancing M&E integration,Bangladesh can maximize the impact and sustainability of urban development initiatives,ultimately building inclusive, resilient, and sustainable cities for future generations.

The economic circumstance of Bangladesh is crucial for the attainment of its development objectives, as globalization has prompted apprehensions regarding regional development. Bangladesh, the second-largest economy in South Asia, is currently ranked 35th in nominal GDP and 25th in nominal GDP based on purchasing power parity. The country's GDP increased by 5.82 percent in 2023-24, compared to 5.78 percent in 2022-23. The purpose of this investigation is to determine the formal regions of Bangladesh concerning their economic circumstances and to identify the factors that either promote or hinder economic development in different districts. The research was analysed across 64 districts using the Composite Weighted Index Method, which considered both economic and non-economic factors such as remittance inflow, literacy rate, solvency head count ratio (HCR), bank deposit, and employment rate. The study analysed key economic indicators such as employment, income, revenue, budget allocation, infrastructural development, educational opportunities, and healthcare facilities. It found a loop connectivity between government revenue, budget allocation, employment, and income across different districts, which is crucial for overall economic growth. This allows policymakers to more effectively allocate resources and to personalize interventions, thereby facilitating targeted efforts to stimulate economic growth. The findings can be a source of help for future policy decisions and contribute to the nation's development objectives.

In response to Bangladesh's severe water shortages, this research delves into the intricate dynamics of groundwater vulnerability. Integrating often-overlooked factors such as topography, meteorology, socio-economic conditions, and land use & geology, the study employs advanced Random Forest (RF) modeling. Rigorously analyzing 200 strategically chosen sample points, the research uncovers critical areas like Rajshahi, Nawabganj, Naogaon, and Dhaka, constituting 21% of the land, as highly vulnerable. In contrast, regions like Rangpur, Mymensingh, and Barisal, encompassing 31% of the area, exhibit lower vulnerability. Topographic factors, accounting for 45% of the vulnerability, including aspect, drainage density, and slope, significantly influence susceptibility. Socio-economic elements contribute 22%, particularly population density and industrial activities. The RF model achieves exceptional accuracy (over 90%), emphasizing the complexity of groundwater dynamics. By integrating geological, social, and economic aspects, the study provides actionable insights for nuanced and sustainable management strategies. This research not only unveils a highly accurate groundwater vulnerability map, enriching scientific understanding, but also offers a unique approach by incorporating often-overlooked variables and leveraging machine learning. These insights empower policymakers and urban planners to craft targeted and sustainable groundwater management strategies, ensuring a resilient water supply system for Bangladesh's growing population. Through this work, the aim is to significantly contribute to the scientific community while providing data-driven solutions for the nation's pressing water challenges.

This study examines how mobility-challenged persons (MCPs) navigate the limitations of transportation systems, using an integrated choice and latent variable (ICLV) model. Building on the experience of MCPs in a South Asian megacity context, that of Dhaka, Bangladesh, the empirical strategy accounts for perceptions of mode-specific challenges, which arguably vary by mobility impairment. These perceptions are posited as latent variables. Drawing on survey data collected from 400 MCPs living in Dhaka, the latent variables were constructed via a factor analysis of 18 statements about the experienced severity (ranked on a scale from 1 to 5) of mode-specific challenges. Holding socio-demographic and travel-related factors constant, we find that perceptions of mode-specific challenges significantly influence mode choices – while the degree of impairment alone, and related mobility aid needed, do not. Perceived limitations of the walking infrastructure shift MCPs' travel demand towards the bus, whereas bus fare-related issues encourage the use of non-motorized and powered three-wheelers. We recommend that mode choice models include latent variables related to MCPs’ perceptions of various modes to more accurately inform universal access policies.

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.