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Buildings are the most striking artificial features, and extracting buildings becomes critical for many service-providing agencies. Although improvements have been achieved, building detection from remotely sensed images is still challenging. In the past few years, many building extraction methods have been put forward by researchers, such as line- or edge-based, template matching, knowledge- and auxiliary data-based, machine learning, morphological operations-based, and GEographic Object-Based Image Analysis (GEOBIA) -based. GEOBIA is a paradigm for analyzing high-resolution images; however, GEOBIA-based building extraction methods encounter problems in the segmentation and classification stage. Thus, the accuracy of those methods is lesser than other building detection approaches. This research introduced several modifications to the previously proposed hybrid segmentation methods, such as using the reference polygon to identify optimal parameters, a donut filling technique to reduce over-segmentation caused by roof elements, and illumination differences to restrict merging with shadow. The proposed methodology was tested on a UAV image with visible bands only. Better results were achieved using this approach when compared to the multiresolution proposed by Baatz and Schäpe (2000) and the other two-hybrid methods proposed by Wang et al. (2018a) and Yang et al. (2017). This hybrid segmentation method was also applied to subsets of the Wuhan University buildings dataset and produced similar results. One of the great strengths of the proposed method was that there were no parameter tuning and user interaction at running time. In addition, it was able to segment both small and large buildings without using any scale or object size parameters.

Rajshahi City Corporation (RCC) has been marked as a geologically vulnerable in terms of earthquake. However, no scientific method has been determined to figure out the influencing factors. This is the first approach to detect the vulnerability of RCC area. In this point of view, the liquefaction hazard map has the innate power to evaluate the potentiality of earthquake-prone areas. The liquefaction map was prepared using sixty borehole profiles data among 30 wards of RCC. The most widely popular Seed’s method (1971) is employed to calculate liquefaction potential index (LPI) using an earthquake scenario of magnitude M7.0 with peak ground acceleration of 0.12 g (BNBC 2010 draft version). Based on LPI value, Rajshahi city area has been divided into four different liquefaction severity categories (i.e., very low, low, high, and very high) and finally mapped through ArcGIS for the complete visualization. This initial study might be helpful for engineers and planners to plan and execute different structural scheme in this region. This paper also summarizes the calculation procedure of determining LPI from available literature to build up a single framework.

The literature on low-cost housing has paid considerable attention to the economic factors influencing the residential stability of the urban poor. However, tenants' perceptions of residential stability in poor neighbourhoods still represent major gaps within the existing knowledge. This paper investigates migration, length of residency and perceived risk of eviction among urban poor, with the aim of understanding residential stability within the context of Least Developed Countries (LDCs). We frame this study within the literature on ‘social capital’, and analyse the aspects of residential stability while focusing on tenants' perceptions of eviction risk as the key outcome of interest. We used data from 1800 households, obtained from 18 poor neighbourhoods across three cities in Bangladesh. The findings suggest that social capital has a potential role to play in increasing residential stability among the urban poor. These are critical in the upgrading and redevelopment of housing, and in promoting the residential stability of the urban poor in Bangladesh and other LDCs. The potential engagement of social capital in housing the poor could make a valuable contribution to the literature on international housing policy. We discuss the implications at length, and draw links with international housing policy discourse.

Environmental degradation can be predicted and managed in a sustainable manner by the perodic analysis of the Land Use/Land Cover (LULC) change pattern, which not only helps to revitalize the environment but also helps to improve future land-use policies. With the Rohingya influx in 2017, the Kutupalong Mega Camp area in Bangladesh is at a severe risk of environmental degradation as the area is experiencing remarkable LULC change. The aim of this research is to illustrate the LULC change in the Kutupalong Mega Camp before and after the refugee influx, as well as its impact on the surrounding environment because of this change. The spatial and temporal variation of the LULC is analyzed from the classified multi-temporal Landsat images for years-2015, 2018, and 2021. The study reveals gradual decrease in forest cover of the area, which is replaced by the increasing human settlements. The study found an inverse relation between the refugee influx and the vegetation cover, where a positive relation to the bare land and settlement exists. The area experienced about ten times increase in human settlements during 2015–2021, which resulted deforestation of surrounding forest cover. Between 2015 to 2021, 74 % of forest cover of the studied area has been cleaned up for newer settlements, with an increase of wetland to meet the needs of increasing refugee population which has made the scenario worse. We also noticed an increase of Land Surface Temperature (LST) within a short period, where the average temperature increase rate is 0.06% during 2015–2018 and 0.01% during 2018–2021. The ecosystem, wild-habitat, and the thermal environment has been disturbed to a great extent due to this drastic change of forest cover mostly by the increasing anthropogenic activities in this area. The study represents the present scenario in comparison to its natural setting just a few years ago, and may serve as a guidance for the concerned authorities and international humanitarian organizations to develop a sustainable, comprehensive, and environment-friendly land management plan in order to protect the surrounding forest-ecology as well as the humanitarian works.

Particulate matter (PM2.5) is one of the major threats to public health, particularly Dhaka City in Bangladesh, frequently cited as one of the worst cities in the World in terms of air quality. This study examines the effects of six environmental (land surface temperature (LST), digital elevation model (DEM), water vapor concentration, wind speed, rainfall, and normalized difference vegetation index (NDVI)) and six economic factors (population density, road density, gross domestic product (GDP), poverty rate level, and percentage of low-income groups in rural and urban setting) on PM2.5 concentration in five industrial cities of Bangladesh using geographically weighted regression modelling (GWR) and machine learning (ML) tools. The mean annual rate of PM2.5 concentration increased by > 42% during 2002–2020 in all cities. Dhaka and Narayanganj districts were affected the most. Goodness-of-fit (R2) was 93% (environmental factors) and 73% (economic factors). Environmental factors: LST (100%) and water vapor concentration (100%) were correlated positively with PM2.5, while DEM (100%), rainfall (83%), NDVI (81%), and wind speed (84%) had a negative relationship at 95% confidence level. β-coefficients of DEM (p < 0.02), LST (p < 0.01), water vapor concentration (p < 0.01), NDVI (p < 0.02), and poverty rate (p < 0.01) were correlated negatively. Moreover, machine learning has extracted a good prediction of PM2.5, ranging the R2 between 0.79 and 0.86%. This study can be replicated in other cities by incorporating socio-economical, local geo-environmental, and meteorological with other air pollutants.

Urban density not only represents the intensity of travel demand, but also serves as a useful tool for quantifying the elements that influence travel behavior and demand. In this study, Khulna City of Bangladesh has been selected as a case study to explore the impacts of urban density on travel behavior in a city of a developing country. The average characteristics of urban density, as well as their impacts on travel behavior and demand, have been studied using geospatial analysis techniques. Urban densities have been categorized into Single Density (Population Density, Employment Density, and Building Density) and Composite Density (Household to Work trip Density, Household to School trip Density and Household to Shop trip Density) based on characteristics of travel behavior and demand. Descriptive statistics have been used to explore the impacts of urban densities on the various trip characteristics such as trip production, trip distance, trip cost, trip time, and trip mode. Diversity of trips and overall trip attraction areas have been identified using the Shannon diversity index and other spatial analyses. Trip production, trip distance, trip time, trip cost, and mode choice all show a positive linear relationship with urban single-density, according to this study. The everyday activities of residents are closely related to urban composite densities, and trip production increase in tandem with composite densities. It has been concluded that different urban densities have significant impacts on travel demand and travel behavior.