九州大学 · 환경과학
Muhammad Salem 교수의 연구실은 도시화가 급속히 진행되는 글로벌 남부 도시, 특히 쿠웨이트의 지방정도인 카이로 주변의 주변도시 지역을 중심으로 연구를 전개하고 있습니다. 주로 위성 영상과 지리정보시스템(GIS)을 활용해 도시 확장, 토지 이용 변화, 농지 감소 등의 환경적 영향을 분석하며, 로지스틱 회귀모델과 딥러닝 기법을 통해 도시 성장의 주요 원인과 자연재해에 의한 피해를 정량적으로 평가합니다. 특히, 도시 확장의 공간적 패턴과 지속 가능성 문제에 대한 통찰을 제공하고자 합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Land use/land cover (LULC) has changed dramatically in the peri-urban area (PUA) of greater Cairo (GC) since the Egyptian revolution of 2011. This study analyzes LULC change in the PUA of GC using two Landsat images from 2010 and 2018. The spatial trends of LULC change and visualizations of the gains and losses in LULC were analyzed using TerrSet software. The driving forces of LULC change from 2010–2018 were quantified using the logistic regression model (LRM). The results revealed that the pro
During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regre
The peri-urban area (PUA) of the Greater Cairo Region (GCR) in Egypt has witnessed a rapid urban expansion during the last few years. This urban expansion has led to the loss of wide, areas of agriculture lands and the annexation of many peripheral villages into the boundary of the GCR. This study analyzed the driving factors causing the urban expansion in the GCR during the period 2007–2017 using the logistic regression model (LRM). Eight independent variables were applied in this model: distan
Natural disasters cause extensive economic losses every year. Rapid detection of earthquake-induced building damages is crucial for disaster response. Remote sensing (RS) has been widely used to assess the impacts of natural disasters i.e. earthquakes and its implications on building damages. Deep Learning (DL) techniques have become increasingly popular for detecting building damages from RS data and have achieved significant success in detecting disaster implications. This paper examines the a
Cities in the Global South are experiencing profound demographic shifts, rapid economic growth, and unchecked urban sprawl, resulting in significant transformations in peri-urban landscapes. This paper focuses on assessing the impacts of chaotic urban expansion in the peri-urban areas (PUAs) of Greater Cairo (GC), serving as a notable case study in the Global South. By analyzing satellite imagery from 2001, 2011, and 2021, this study examines changes in land use/cover (LUC) within GC’s PUAs. Emp
Although there is no consensus on the definition of the peri-urban areas, there is growing recognition among development professionals that rural and urban features tend to increasingly co-exist within cities and beyond their limits. This research discusses the peri-urban area through reviewing previous literature. In addition, to define the criteria of delimitation of peri-urban areas in light of those literatures, therefore, delimitation of peri-urban areas of Greater Cairo.
During the last few decades, sustainable development (SD) has increasingly received attention globally. Therefore, international organizations and researchers sought to assess progress towards SD at different territorial levels. However, most of the studies were conducted at the city level and a very small number of studies has conducted at the urban periphery territory. This study aims to fill the current research gap through assessing the progress towards SD in the urban periphery of Greater C
Sustainable development (SD) has become a crucial challenge globally, particularly in developing countries and cities. SD of peri-urban areas (PUA) has been tackled by a limited number of studies, unlike that of urban areas or cities. The PUAs of Greater Cairo (GC) are no exception; no study had addressed the state of the PUAs in terms of SD. Thus, this study sought to measure and evaluate the progress towards the SD in the PUAs of Greater Cairo, Egypt. Thirteen indicators were extracted from se
Greater Cairo (GC) is the seventh-largest metropolitan city globally. In recent decades, GC has witnessed massive urban expansion, which has yet to be empirically measured or characterized. This research seeks to explore the patterns of urban growth and changes in urban form in GC from 1973 to 2021 using remote sensing and geospatial metrics. Six Landsat images in 1973, 1984, 1992, 2003, 2013, and 2021 were used to explore urban growth in GC. Urban Land Density Function (ULDF), Landscape Expansi
Peri-urban areas (PUAs) represent dynamic transition zones where urban expansion, rural livelihoods, and environmental sustainability intersect, posing complex land use management challenges. While effective land use management in these regions is vital for fostering sustainable urban growth, it remains one of the most difficult tasks for planners and policymakers. This systematic review synthesizes findings from 137 studies published over the past 25 years, employing bibliometric analysis, syst
Since the early 1980s, the Greater Cairo Metropolitan Region (GCMR) has witnessed a rapid urban expansion that has been mainly concentrated in the peri-urban areas (PUAs). Most of this expansion was against urban planning laws and has presented a critical challenge to the urban environment. It has also led to spatial fragmentation and loss of enormous agriculture lands. This research analyses the urban expansion in the PUAs of the GCMR, during the period (2001-2017) using GIS and remote sensing.
Slums are a global urban challenge, particularly in big cities in most developing countries where they are growing faster than governments control. However, detection of slums is a big challenge for such countries due to fast growing there and difficulty of field survey. To address this challenge, this study uses a novel method to detect slums from very high-resolution (VHR) satellite images using machine learning algorithms and roads network derived from OpenStreetMap. This method has been appl
This study advocates for the innovative use of advanced deep learning and remote sensing technologies in monitoring urban dynamics to enhance our understanding of the environmental implications of urbanization and formulate strategies for sustainable land management. Leveraging the potential of these technologies contributes to the realization of sustainable development goals (SDGs), nurturing the growth of sustainable and resilient cities for the future. Focused on mapping land cover and change