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Introductory Digital Image Processing

The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.

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Slums from Space: 15 Years of Slum Mapping Using Remote Sensing

Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state‐of‐the‐art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML‐based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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Machine learning and artificial neural network accelerated computational discoveries in materials science

One of the most important impacts of urbanisation in Indian cities is the conversion of green belts and agriculture land into the built-up area in the periphery (Chadchan and Shankar, Int J Sustainable Built Environ 1:36–49, 2012; Pandey and Seto, J Environ Manag 148:53–66, 2015). With these physical changes, i.e. decrease in green cover and increase in built-up, the land surface temperature (LST) is bound to increase. The green area is a basic need of any city because it is a must for a healthy life and also maintains the aesthetic and ecological beauty in the urban areas (Low et al. 2007). The present study aims to analyse the association between built-up, green cover and land surface temperature for which district-level analysis of the normalised differential built-up index (NDBI), normalised differential vegetation index (NDVI) and land surface temperature (LST) has been done over the urban area of Delhi. In this study, Landsat 7 (ETM+ SLC) for 2003, Landsat 5 (TM) for 2010, and Landsat 8 (OLI/TIRS) for 2017 have been used together with Survey of India (SOI) toposheet of Delhi at 1:25,000. Indices like NDBI, NDVI and LST are calculated for 2003, 2010 and 2017 using the spectral radiance model (SRM), the mono-window algorithm (MWA) and the split window algorithm (SWA). Thereafter, district-wise NDBI, NDVI and LST are extracted by using clip tools in ArcGIS 10.5 software. To analyse the relationship between built-up and green cover with LST, correlation is done in SPSS software and a scatter diagram is made to assess the correlation amongst the variables. The further surface temperature profile is created to know which part of the Delhi has the highest and lowest temperatures on a particular surface. The study shows that NDVI and LST are negatively correlated with each other as vegetation has a cooling effect on the land surface temperature whereas NDBI and LST are positively correlated with each other. The studies show a change in the distribution of vegetation cover and gradually increase in the built-up land which results in the increase in land surface temperature to about 3.31 °C in the last 14 years. The result shows that MWA give the most accurate result in this study since RMSE of MWA is the lowest (0.71 °C) amongst the three algorithms used in the study. Temporal analysis of land surface temperature by all the three algorithms shows the increase in land surface temperature of Delhi between 2003 and 2017.

Satellite-Driven Land Surface Temperature (LST) Using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) Data and Its Association with Built-Up and Green Cover Over Urban Delhi, India

Laws of the Federation of Nigeria: A Subject Analysis

From the 1990s to date, growing deterioration of urban environmental quality, and the developments of squatter settlements and squalors have gradually become the attributes of the development patterns in the Federal Capital City (FCC), Abuja and environs in Nigeria. The FCC, Abuja and the surrounding towns and settlements popularly referred to as ‘satellite towns’ in Nigeria, are characterized by inadequate urban infrastructure, high rural-urban migration, population pressure, high cost of living, poor economic lifestyle, informal settlements and failing infrastructure. In view of these urban development dynamics and the prevailing economic situations in Nigeria, many citizens have resorted to building and settling in unapproved lands within and around the city. In attempts to maintain the designed land use and master plan of the city and to control the quality of infrastructural developments in the area, the relevant government authorities often employ the use of mass demolition of illegal constructions, and informal settlements; resulting in displacement of many inhabitants. A further repercussion of this process is the emergence of several unplanned settlements dotting the surroundings of the city area assumed to be outside the immediate development interest of the FCC authorities. This paper is therefore aimed at assessing the urbanization of the FCC, Abuja as a catalyst for the emergence of squatter settlements and squalors in and around the city. To achieve this, Landsat satellite images of Abuja in 1987, 2001 and 2006 were used in mapping and analyzing the spatial growth of the city. The results of the study showed that squatter settlements and squalors have sprang up in and around the study area due to poor housing scheme, which made proper accommodation within the city unaffordable for low income residents of the city who were mostly civil servants. It was argued in the paper that, though demolition exercise may be agreed by the Abuja City Administration as a necessity, this could be largely avoided if proper housing infrastructure is included in the planning process.

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Urbanization: A Catalyst for the Emergence of Squatter Settlements and Squalor in the Vicinities of the Federal Capital City of Nigeria

Urban wetlands play important roles in providing several ecosystem services that support the urban environment. As such, scientists have studied them to understand the urban processes that lead to their continued decline. However, little attention has been given to the drivers of land-use change that may affect this fragile ecosystem in the future. Understanding this could serve as a critical step towards urban wetland management and sustainability. In this study, we utilized an integrated approach that combined Similarity Weighted Instance-based Machine Learning and Markov chain, both embedded in the IDRISI Land Change Modeler to simulate change in the landscape of three watersheds in the Kansas City Metropolitan area. The purpose was to assess the possible future impacts of urban expansion-induced landscape change on wetlands within the study area, using a retrospective approach. To achieve this, classified SPOT satellite data covering the three watersheds were used to generate historical land cover maps of the study area between 1992 and 2010 to analyze changes to the landscape. In addition, the study identified several drivers of land change associated with the historical change process in the study area, and accounted for their role in the modeling process. On this basis, the study made the prediction of urban landscape transformation to the end date of 2014. The prediction result was verified with a more accurate map that was derived from independently classifying a 2014 SPOT image of the study area. Results from this study show that impervious surfaces, which were used as an index of urban expansion, may increase by approximately the same magnitude experienced historically, which may result in a small but significant loss of wetlands and other land cover classes within the study area.

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Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Prairies or grasslands together with areas designated as agricultural lands are one of the largest types of land cover and land use that exist today. While prairies provide habitat to a wide variety of animals and organisms, and agricultural lands support human populations, these lands, especially those in the immediate vicinities of large urban areas, are giving way to urbanization at alarming rates. In particular, prairies are often viewed as wastelands because their benefit to the effective functioning of the urban ecosystem is often not fully understood. On the other hand, many agricultural lands are being converted for several urban uses because of the high economic returns from their sale. In this study, we classified SPOT (Satellite Pour l’Observation de la Terre) satellite data of the study area using the supervised maximum likelihood classification approach in order to investigate the loss of prairies and agricultural lands due to urban expansion in six sub-watersheds in the Kansas City metropolitan area of the States of Kansas and Missouri in the U.S. Based on the classified maps, we computed the magnitude and rate of urban expansion, and the proportion of loss in prairies and agricultural lands that was a result of urban expansion. Results from the 22-year study revealed that in all six sub-watersheds, agricultural lands and grassland were depleted at alarming rates with no sustainable effort to conserve them. These results provide baseline information that can support a data-driven and sustainable path for urban expansion in the examined sub-watersheds.

https://www.mdpi.com/2071-1050/11/17/4673/pdf

Urban Expansion and the Loss of Prairie and Agricultural Lands: A Satellite Remote-Sensing-Based Analysis at a Sub-Watershed Scale

 Accurate modeling of change in urban lands has been limited by many technical factors Although efforts have been put into improving many of the urban land change models and in developing new ones, little has been done to promote understanding the significance of methods of classifying the satellite images often used as input maps in many of these models In this study, the goal was to examine the significance of the impacts of classification methods of input land cover maps on the overall accuracy of the urban land change prediction To do this, the historical SPOT satellite images of Kansas City were classified using multilayer perceptron neural network, and maximum likelihood classification techniques The effects of these two classification methods on the overall accuracy of land change prediction were then assessed The study made use of an independently classified and more accurate map of a known year to validate the results of both predictions The study results show that the methods sele

Assessing the Impact of Land Cover Classification Methods on the Accuracy of Urban Land Change Prediction

Several studies have shown human impacts on urban wetlands. These impacts are mostly studied at broad scales, which may generalize and aggregate important information needed for landscape quantification or terrain analysis. This situation can weakly or inappropriately address the structure of wetland landscapes, thus affecting the assessment of the quantities and qualities of terrestrial wetland habitats. To address these issues for urban wetland dynamics, this study proposes the use of landscape and terrain indices to characterize the landscape structure of urban wetlands at a fine scale in order to assess its usefulness in contributing to wildlife sustainability. To achieve this goal, secondary terrain attribute data are integrated with an object-based satellite image classification at the wetland and watershed level. The result reveals a general swell in wetland coverage at the watershed level. Further analysis shows the size and shape complexities, and edge irregularities are increased significantly at the patch level but slightly at the watershed level. Terrain analysis further reveals a potential increase in wetness and decrease in stream power vulnerability for most of the major wetlands under study. These results suggest that terrain and landscape indices are effective in characterizing the structure of urban wetlands that supports socio-ecological sustainability.

Opeyemi A. Zubair

Papers

Urbanization: A Catalyst for the Emergence of Squatter Settlements and Squalor in the Vicinities of the Federal Capital City of Nigeria

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Urban Expansion and the Loss of Prairie and Agricultural Lands: A Satellite Remote-Sensing-Based Analysis at a Sub-Watershed Scale

Assessing the Impact of Land Cover Classification Methods on the Accuracy of Urban Land Change Prediction

Characterizing the Landscape Structure of Urban Wetlands Using Terrain and Landscape Indices