Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

Land acquisition in Ghana is fraught with challenges of multiple sales, numerous unofficial charges, unnecessary bureaucracies, intrusion of unqualified middlemen, and lack of transparency among others. Studies have suggested digitization as a way forward to improve Ghana’s land management system and to address these acquisition challenges. However, none of these studies have specifically provided a clear conceptual digital framework for land acquisition. Most contemporary land literature globally appraise blockchain technology as a potential solution to these challenges in Ghana’s land acquisition process. This article applies an integrative review, mixed with strengths, weaknesses, opportunities, and threats (SWOT) analysis, and deductive lessons from a digital land registry concept to develop a blockchain-based smart land acquisition framework solution in view of Ghana’s land acquisition challenges. However, it is identified that threats of sabotage of this framework exist among some customary land owners, land officials, and private blockchain-based land experts for various reasons. Among others, a legal basis for a public–private partnership is recommended particularly to discourage sabotage from private blockchain-based land experts. We recommend future research works to delve into establishing a framework that can be used as a guide to assess the readiness of land management and land administration systems for blockchain consideration in sub-Sahara Africa, particularly Ghana.

The presence of land use conflicts is often unavoidable as land is finite and a scarce resource. With development as a prime goal, the increasing demands for specific uses make the situation more serious than it was before. In the context of land uses, suitability determines the inherent capacity of the land to perform a defined use with optimum efficiency and sustainability. However, single land use suitability analysis could not answer the overall objective of land allocation. Thus, this study considers the primary and general land uses with the valuable evaluation criteria necessary for simultaneous land use suitability analyses. This paper aims at establishing the relevant and necessary evaluation criteria for Multicriteria Evaluation (MCE) using the Analytic Hierarchy Process (AHP) for land use suitability analysis for residential, industrial, commercial, agricultural, and forest land uses. The factors which could be used as indicators in land suitability analysis were derived from both literature review and through experts’ knowledge. Correspondingly, the relative importance (weights) of the criteria established were derived using pairwise comparisons through the AHP technique readily available for subsequent GIS analysis. Last, the criteria developed are general in nature and could be replicated and/or altered depending upon the local needs and situations.