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.

A large part of the population in low-income countries (LICs) lives in fragile and conflict-affected states. Many cities in these states show high growth dynamics, but little is known about the relation of conflicts and urban growth. In Afghanistan, the Taliban regime, which lasted from 1996 to 2001, caused large scale displacement of the population. People from Afghanistan migrated to neighboring countries like Iran and Pakistan, and all developments came to a halt. After the US invasion in October 2001, all the major cities in Afghanistan experienced significant population growth, in particular, driven by the influx of internally displaced persons. Maximum pressure of this influx was felt by the capital city, Kabul. This rapid urbanization, combined with very limited capacity of local authorities to deal with this growth, led to unplanned urbanization and challenges for urban planning and management. This study analyses the patterns of growth between 2001 and 2017, and the factors influencing the growth in the city of Kabul with the help of high-resolution Earth Observation-based data (EO) and spatial logistic regression modelling. We analyze settlement patterns by extracting image features from high-resolution images (aerial photographs of 2017) and terrain features as input to a random forest classifier. The urban growth is analyzed using an available built-up map (extracted from IKONOS images for the year 2001). Results indicate that unplanned settlements have grown 4.5 times during this period, whereas planned settlements have grown only 1.25 times. The unplanned settlements expanded mostly towards the west and north west parts of the city, and the growth of planned settlements happened mainly in the central and eastern parts of the city. Population density and the locations of military bases are the most important factors that influence the growth, of both planned and unplanned settlements. The growth of unplanned settlement occurs predominantly in areas of steeper slopes on the hillside, while planned settlements are on gentle slopes and closer to the institutional areas (central and eastern parts of the city). We conclude that security and availability of infrastructure were the main drivers of growth for planned settlements, whereas unplanned growth, mainly on hillsides, was driven by the availability of land with poor infrastructure.

This chapter discusses how to define, detect, and evaluate rural communities’ Vulnerability. It starts by defining vulnerability and distinguishing the term from hazards, risks, exposure, susceptibility and resilience. It then continues by listing the types of vulnerabilities rural communities face, the common vulnerabilities they share, what differentiates them as well as the main parameters that can be used for rural community assessment. It concludes by listing some helpful resources for researchers and practitioners to evaluate, detect, and manage vulnerabilities for rural communities.

International trade hubs are vital for post-conflict economic recovery, particularly in landlocked and developing nations. This study examines how changes in land use and land cover (LULC) reflect the progress of reconstruction in Afghanistan’s three major trading hubs, Hairatan, Sherkhan Bandar, and Torghundi, between 2016 and 2023, along the international borders. Harmonized Sentinel-2 imagery (10 m resolution) is classified into five LULC classes: built-up, vegetation, swamp, barren land, and water using Artificial Neural Network (ANN) and Random Forest (RF) classifiers on Google Earth Engine and Google Colab to map and assess spatial changes. The RF classifier achieved higher accuracies (89–99%) than ANN across all towns. Built-up areas increased in all sites, with Hairatan expanding from 2.23 km² (15%) in 2016 to 3.22 km² (22%) in 2023. Torghundi showed a similar rise from 3.78 km² to 5.38 km², accompanied by a decline in vegetation from 2.54 km² to 1.10 km². Sherkhan Bandar recorded moderate built-up growth alongside seasonal increases in vegetation. A predictive RF model projects a further 53% built-up expansion along the Torghundi railway corridor by 2030. These findings demonstrate that LULC dynamics can serve as measurable indicators of post-conflict recovery while also revealing environmental trade-offs. The study highlights the role of remote sensing for monitoring reconstruction and emphasizes the need to integrate infrastructure development with sustainable land management. Graphical Abstract

Background: Thrombocyte is important and very essential component of blood and have significant role in maintenance of hemostasis. Thrombocyte count is an important investigation done in various acquired and congenital coagulable states which include conditions like pregnancy. Thrombocyte count is routinely done by automated hematology analyzer method. The automated hematology analyzer counters are not usually available at all centres especially in peripheral and rural side though thrombocytes can also be assessed from the peripheral blood smears, which can be easily and precisely done at any set up. Aim and objective of this study was to compare the thrombocyte estimation by peripheral blood smear method and automated hematology analyzer in pregnant women.Methods: Thrombocyte estimation was done from samples taken from 120 normal pregnant women between December 2018 to March 2019, where samples were Ethylene Diamine Tetra Acetic acid (EDTA) anticoagulated. Thrombocyte was counted manually using PBS (Leishman stain) and hematology analyzer (Sysmex XN1000 series). Thrombocyte counts were expressed in Mean and standard Deviation. Statistical analysis was done by student’s t test using MS excel and SPSS version 17.Results: Thrombocyte count by PBS have mean value of 2.04 lacs/mm3 with standard deviation of 0.56 lacs/mm3 and by automated method have mean value of 1.89 lacs/mm3 and standard deviation of 0.71 lacs/mm3 with p value 0.010. Thus, there was no statistically significant difference found between two methods.Conclusions: Estimation of thrombocyte count on the basis of manual thrombocyte count is a reliable technique and can be used to validate automated thrombocyte counts. It can also be used in under resourced laboratories, where there are no automated counters of good precision available. In fact, all the tests showing abnormal thrombocyte counts must be reported only after cross examining on PBS.

The village of Bayerisch Eisenstein in the State of Bavaria, Germany, experiences a declining population. Located in the middle of the Bavarian Forest National Park, the village is a popular tourist destination. Nevertheless, over the years, due to a sharp decline in tourism-related activities, socio-economic activities are shrinking fast. To understand how this relates to the vitality and versatility of rural towns, this chapter analyses the walkability of residents to essential public amenities, as a proxy of the basic infrastructure of the village. With OpenStreetMap datasets, along with geospatial analytical tools, one can perform a network analysis. Additionally, the study evaluates how spatial planning regulations for rural villages play in supporting infrastructure services. This chapter demonstrates an exemplary procedure of how villages could collect and analyse openly accessible information for one or more aspects of vitality assessments, which in turn helps to support developing rural development strategies.