Remote sensing is the technique of acquiring data from the earth’s surface from sensors installed on satellites or on manned or unmanned aircrafts. Its use is common in dozens of sectors of science and technology, agriculture, atmosphere, soil, water, land surface, oceans and coasts, snow and ice, and natural disasters, among others. This article focuses on an in-depth literature review of some of the most common and promising disciplines, which are asbestos–cement roof identification, vegetation identification, the oil and gas industry, and geology, with the aim of having clarity on the trends in research on these issues at the international level. The most relevant problems in each sector have been highlighted, evidencing the need for future research in the area in light of technological advances in multi- and hyperspectral sensors and the availability of satellite images with more precise spatial resolution. A bibliometric analysis is proposed for each discipline and the network of related keywords is discussed. Finally, the results suggest that policymakers, urban planners, mine, and oil and gas companies should consider remote sensing as primary tool when planning comprehensive development strategies and in field parameter multitemporal analysis.

The discernment of asbestos-cement (AC) roofs within urban areas stands as a pivotal concern pertinent to communal well-being and ecological oversight, particularly in emerging nations where asbestos continues to be extensively employed. Conventional methodologies entailing the recognition of asbestos-cement roofs and the characterization of their degradation status, such as tangible examinations and laboratory assays, prove to be temporally protracted, financially demanding, and arduous to extrapolate comprehensively across expansive urban domains. In this paper, it is presented a novel approach for identifying asbestos-cement roofs in urban areas using hyperspectral airborne acquisition and carry out a diagnosis that allows to identify the state of asbestos-cement roofs and thus provide a tool for the competent authorities to develop and prioritize intervention strategies to mitigate the problem. Four different methodologies were implemented and compared, three of which are new in the literature, to identify the deterioration of asbestos-cement (AC) roof state in large urban areas. This, in turn, furnishes a tool for competent authorities to identify the state of AC roofs, develop and prioritize intervention strategies to mitigate the problem.

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM_2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM_2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM_2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM_2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R² of 0.47), wind speed (R² of 0.59) and temperature (R² of 0.64).

The present study reports a critical review of NIO′s business model considering the evolving landscape of the electric vehicle market and servicing. The objective of this study is to develop a comprehensive framework that facilitates the identification of key elements characterizing a company’s business model and highlights ongoing transformations crucial for adaptation and survival in a rapidly changing environmental context. Focusing on the case study of NIO, a relatively young Chinese original equipment manufacturer (OEM) specializing in high-tech electric cars, the research delves into the challenging scenario of the Chinese electric vehicle market, which recently faced a bubble in 2023. The market proliferation, supply chain disruptions, and price wars triggered by Tesla have resulted in a survival struggle for numerous automotive startups, leaving larger companies with increasing market shares. Despite facing adversities, NIO managed to secure a promising segment catering to premium-range battery electric vehicles (BEVs), establishing a competitive advantage through differentiation. By pursuing ambitious investments, the company aims to create economies of scope and achieve cost leadership, venturing into new market sectors and vertically integrating the production chain. Given NIO’s agility in adapting to market conditions, aggressive entry into new segments, and a strategic vision for the future, it serves as an excellent candidate for testing and validating the proposed framework. The research sheds light on NIO’s trajectory and offers insights into its potential for sustained growth in the dynamic electric vehicle market.