The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has stimulated the search for new data processing and validation methods, considering the magnitude of the data collected during fieldwork and the absence of specific methodologies for each type of sensor. Regarding data processing, the use of deep learning techniques has become widespread, especially for the automation of processes that involve a great amount of data. However, just as with the increasing use of embedded sensors, deep learning necessitates the development of studies, particularly those focusing on neural networks that better represent the data to be analyzed. It also requires the enhancement of practices to be used in fieldwork, especially regarding data processing. In this context, the objective of this study is to review the existing literature on the use of embedded technologies in UAVs and deep learning for the identification and characterization of pathological manifestations present in building façades in order to develop a robust knowledge base that is capable of contributing to new investigations in this field of research.

The use of UAVs equipped with sensors has gained prominence due to their efficiency, safety, and ability to provide detailed data for hard-to-reach inspections, allowing for more precise and integrated analyses. Despite their great potential, these technologies still face a significant gap in understanding the actual capabilities of each piece of equipment to identify different pathological manifestations on building façades, as well as their advantages and limitations. In this context, this study aimed to analyze the capabilities and limitations of RGB, thermal, and multispectral sensors embedded in UAVs for detecting pathologies on building façades, addressing gaps in understanding their advantages, limitations, and effectiveness in identifying different anomalies. To this end, four façades of a building were mapped using these sensors, focusing on capturing pathologies such as cracks, moisture stains, coating detachment, and the presence of moss. The methodology involved using a UAV equipped with high-resolution RGB sensors (with GSD ranging from 0.867 mm to 0.985 mm per pixel), thermal sensors (with GSD ranging from 6.13 mm to 8.72 mm per pixel), and multispectral sensors (with GSD ranging from 6.49 mm to 9.80 mm per pixel), followed by the processing and creation of façade orthophotos and the visual identification of each pathology found on each face. Multiple image overlaps were performed to ensure adequate coverage of the analyzed surfaces. As a quantitative result, it was observed that the RGB sensor was the most effective in identifying surface pathologies, while thermal and multispectral sensors, although placing a smaller quantity of pathologies, stood out in detecting subsurface pathologies, such as infiltration and internal cracks. Complementarily, it was observed that the integration and complementarity of these devices could be of the utmost importance for a more comprehensive and precise analysis of pathologies, showing that combining different types of sensors significantly contributes to a more complete and detailed mapping of façade conditions.

Latossolos podem ser caracterizados como solos geralmente minerais e homogêneos que possuem pouca distinção entre seus horizontes. No Cerrado, eles ocupam as áreas planas a suave-onduladas, sejam chapadas ou vales. Este trabalho tem como objetivo realizar a correlação pedogeotécnica em um perfil de solo, situado na área da UEG no Campus Henrique Santillo, na cidade de Anápolis-GO, gerados dos processos pedogenéticos. A execução dos levantamentos do solo envolveu os trabalhos de campo e laboratório, por meio de caracterizações morfológicas, físicas e químicas de acordo com o Sistema Brasileiro de classificação – SiBCS, e com atributos geotécnicos, à título de conhecimento do seu comportamento e de suas aplicações. Os resultados servirão como indicadores para subsidiar o planejamento das ampliações na área do Câmpus, indicando de maneira mais evidente as limitações e potencialidades do meio físico para uso e ocupação no Câmpus da Universidade Estadual de Goiás.