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.

Water quality indexes are a tool used to evaluate the physicochemical characteristics of a water body according to its use. The present study proposes the inclusion of oil and grease (OG) as a new water quality index (ICAM_PFF-GA) parameter for the preservation of marine biota in tropical areas, since it is a typical pollutant found and measured in water bodies, causing damage to the aquatic environment. The normalized curve for OG was defined based on the percentage of surviving microorganism under a lethal concentration exposure of OG. The ICAM_PFF-GA suitability was evaluated by its application to analyze marine water quality in the area of the sea outfall in the city of Cartagena, Colombia and comparing the trends of the outfall flow and the rainfall for 2017. Physical chemical data analyzed for the year 2017 shows that OG varies from 0.0 to 3.8 mg/L. The results show that the water quality index increases when rainfall and flow values increase for the rainy season. The ICAM_PFF-GA can be a tool to evaluate the water quality of marine waters affected by the discharge of waters with oil and grease.

This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies.

This paper presents a systematic and data-driven approach to prioritize interventions in urban areas with asbestos cement (AC) roofs, addressing the urgent need to mitigate asbestos-related risks. The objective is to propose a comprehensive methodology that considers multiple criteria at the neighborhood level, allowing for a nuanced assessment of intervention priorities. The methodology involves the normalization of various parameters, including population density, facility density, and the area covered by asbestos-cement roofs. In addition, an innovative aspect is introduced by incorporating weathering status identification data, represented as an index, validated in previous research, further enriching the evaluation process. The integration of these diverse factors allows for a holistic understanding of the risk landscape associated with AC roofs in urban settings. The cornerstone of the proposed approach is the development of a Priority Intervention Index (PII) at the neighborhood level. This index serves to standardize the assessment of intervention priorities, enabling a fair and transparent comparison across different regions. To enhance practical application, the PII is discretized into three categories, low, mid and high intervention priority. The results obtained are robust, replicable in other scenarios, and practical for decision-makers. The new methodology provides a structured and quantifiable approach to identify and prioritize areas for asbestos-related interventions based on well-defined criteria at the neighborhood level. The resulting prioritization strategy offers urban planners and local officials a clear and evidence-based tool to allocate resources efficiently and effectively manage the inherent risks associated with AC roofs in urban environments. The paper will describe how the prioritization can be applied "at the neighborhood level" by urban planners and local officials.