Showing posts with label urban environment. Show all posts
Showing posts with label urban environment. Show all posts

Saturday, 29 November 2025

GeoAI-enabled Transformation of Urban Environmental Aesthetics in Nigerian Cities: Challenges, Opportunities, and Implementation Strategies | Chapter 1 | Current Research on Geography, Earth Science and Environment Vol. 4

 

Urban aesthetics encompasses the visual and spatial quality of a city, significantly affecting residents' well-being and economic growth. Theories of urban design emphasise the importance of spatial organisation, green spaces, and architectural coherence in fostering aesthetically appealing cities. Nigeria’s economic diversification hinges on transforming its urban centres into aesthetically vibrant, sustainable hubs, moving beyond reliance on crude oil. Geospatial technology has been shown to be affordable and efficient in understanding the varying forms of cities, their continuous expansion, and the processes responsible for their growth pattern. Cities like Lagos, once celebrated for their cultural and architectural heritage, now grapple with aesthetic decline driven by rapid urbanisation, inadequate planning, environmental degradation, and socio-economic disparities.

 

This study critically examines the challenges undermining urban environmental quality in Nigeria, focusing on hotspot cities like Lagos, Port Harcourt, Abuja, Kano, among others, as a case study, and identifies gaps in knowledge, infrastructure, and institutional support that exacerbate these issues. Key obstacles include limited geospatial data, high costs of GeoAI adoption, technical expertise deficits, and socio-cultural resistance to technology-driven urban interventions.

 

Employing a multidisciplinary approach, such as a systematic literature review, the study proposes novel contributions to innovative solutions, integrating sustainable urban planning, inclusive governance, public participation, and advanced geospatial technologies to restore and enhance urban beauty. The systematic literature review involves an extensive examination of 80 published academic sources, policy documents, and case studies relevant to urban aesthetics, GeoAI, governance, and sustainable city planning.

 

The study highlights the role of GeoAI in enabling precise urban design, green space optimisation, and infrastructure monitoring, drawing on best practices from cities like Singapore and Kigali that have successfully revitalised their urban aesthetics.

 

Advanced GeoAI technologies— encompassing satellite imagery, predictive analytics, and real-time urban monitoring—offer transformative potential to redeem the environmental aesthetics of Nigerian cities, fostering tourism, employment, and foreign exchange. However, incomplete data remains a critical barrier, undermining the reliability of spatial predictive models and their applications in urban planning and social sensing. Challenges such as missing features, sparse observations, temporal snapshot limitations, and mismatched spatial scales hinder GeoAI’s ability to capture the complex interplay of human–environment interactions in cities like Lagos.

 

This study argues that successful implementation in Nigeria demands robust policy frameworks, capacity building, and equitable technology access to bridge systemic inequalities. Through addressing these challenges, this study provides a new roadmap for policymakers, city planners, and stakeholders to leverage GeoAI for urban renewal, ensuring Nigerian cities reclaim their aesthetic allure while fostering economic and environmental resilience. This study's recommendations underscore that structuring Nigeria’s urban environmental aesthetics requires not only technological innovation but also a commitment to social equity and community-driven urban transformation.

 

Author(s) Details

 

Ugochukwu Udonna Okonkwo
Department of Geography and GIS / Department of Computer Management and Information Systems, Southern Illinois University, Edwardsville, U.S.A.

 

Ezekiel Tosin Babatunde
Department of Environmental Sciences, Southern Illinois University Edwardsville, U.S.A.

 

Philip Ugbede-Ojo Onuche
Department of Chemistry / Department of Management and Marketing, Southern Illinois University, Edwardsville, U.S.A.

 

Enoh Martha Francis
Department of Geography and Geospatial Sciences, Oregon State University, U.S.A.

 

Paul Osazuwa
Illinois Environmental Protection Agency (EPA), U.S.A.

 

Olumide S. Ogungbemi
Department of Geography and Geospatial Sciences, Oregon State University, U.S.A.

 

Please see the book here :- https://doi.org/10.9734/bpi/crgese/v4/6261

Monday, 21 September 2020

Remotely Sensed Multispectral Satellite Image for Land Cover Classification | Chapter 1 | Recent Developments in Engineering Research Vol. 3

 

In an urban environment natural and human-induced environmental changes are of concern today
because of deterioration of environment and human health. The number of water bodies is declining
day by day whereas the concrete jungle is increasing. The study of land use and cover changes is
very important to have proper planning and utilization of natural resources and their management.
Remote sensing has become an important tool to develop and understand the global, physical
processes affecting the earth. In this chapter, we describe two machine learning algorithms which can
be used to analyse the change in the land cover for a period of over 10 years. We compare the
accuracies of the two algorithms applied to the same datasets and plot the same. We conclude by
stating that the performance of any algorithm depends on the dataset. It is very important to know the
characteristics of the input data before applying any classification process. The algorithms were
designed using Python programming language. The details about the dataset used, the relevant
equations, methodology and results have been provided in this chapter.

Author(s) Details

Dr. P. A. Vijaya
Department of ECE, BNMIT, Bangalore, Karnataka, India.

Keerti Kulkarni
Department of ECE, BNMIT, Bangalore, Karnataka, India.

View Book :-
http://bp.bookpi.org/index.php/bpi/catalog/book/261