ArcGIS Deep Learning Land Cover Classification for Pipeline Expansion Support

doi:10.22167/2675-6528-202602687

Article

September 30, 2026

ArcGIS Deep Learning Land Cover Classification for Pipeline Expansion Support

Classification of Lands with ArcGIS Deep Learning to Support Pipeline Expansion

Antonio Marcos Pires Martins; Arthur Damasceno Vicente

DOI: 10.22167/2675-6528-202602687

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

Pipeline expansion demands territorial planning that reconciles technical feasibility and environmental constraints. The objective was to apply a preliminary territorial suitability approach to support initial pipeline expansion planning. Remote sensing data and spatial analysis were used. The methodology explored the classification of land use and cover by deep learning on Sentinel-2 images and evaluated physical terrain variables, such as slope and aspect, and socio-environmental constraints, including water bodies and quilombola areas. The validation of the deep learning thematic classification showed an overall accuracy of 44.4% and a Kappa index of 0.305, evidencing limitations of the pre-trained model against the seasonal conditions of the study area. This performance made the direct integration of the cover map into the suitability model unfeasible at this stage. However, a preliminary suitability model was structured, categorized into suitable, conditioned, and restricted areas, based on physical and restrictive variables. The point-based analysis verified the spatial distribution of these classes, with 46.3% of the areas classified as suitable, 37.0% as conditioned, and 12.0% as restricted. It was concluded that the individual exploration of these techniques constitutes an important initial support for the planning of linear infrastructures, offering a transparent framework of criteria and precedences for spatial screening.

Keywords: Spatial Analysis; Territorial suitability; Deep learning; Geoprocessing; Linear infrastructure.

1. Introduction

The expansion of energy infrastructures, such as gas pipelines, demands territorial planning processes that reconcile technical feasibility, environmental restrictions, and legal conditions. The definition of suitable areas for the implementation of this type of infrastructure involves the integrated analysis of multiple spatial variables, including physical terrain characteristics, land use and cover patterns, and the presence of sensitive areas. In this context, the use of Geographic Information Systems (GIS) has been consolidated as an essential tool to support decision-making in infrastructure projects, especially in the initial planning phases (Longley et al., 2015).

Traditionally, territorial suitability analyses for the deployment of pipelines and other linear infrastructures are based on the integration of thematic cartographic databases and previously defined technical criteria. However, the availability, updating, and level of detail of this information do not always meet the needs of regional-scale projects, which can introduce uncertainties into the decision-making process (Eastman, 2012). In this regard, remote sensing has proven to be a relevant alternative for obtaining updated information about the territory, contributing to more consistent and integrated spatial analyses (Jensen, 2016).

In recent years, deep learning techniques have been widely applied to the classification of remote sensing images, showing significant advances in terms of automation and accuracy when compared to traditional classification methods (Ma et al., 2019; Zhu et al., 2017). Convolutional neural network-based models allow the identification of complex patterns in multispectral images, enabling the generation of land use and land cover maps with a higher level of detail. These products can support spatial analyses aimed at planning linear infrastructures, including the identification of urban areas and water bodies.

Despite these advances, it is fundamental to distinguish the descriptive role of land use and land cover maps from that played by territorial suitability models. While image classification seeks to represent land occupation, suitability models aim to express technical and legal restrictions and conditions for the implementation of projects, based on the integration of different spatial variables (Malczewski, 2006). This distinction is essential to avoid misinterpretations and ensure that the generated products are used appropriately in the planning stages.

Given this context, and considering that the validation of thematic classification by deep learning presented limited performance (global accuracy of 44.4% and Kappa index of 0.305), which made its direct integration into the fitness model at this stage of the study unfeasible, this work is justified by the need for a spatial screening tool for linear infrastructure planning. Thus, the general objective of this work is to develop and apply a preliminary framework to support the assessment of territorial suitability for pipeline expansion, considering the characterization of land use and cover by deep learning, the physical variables of the terrain, and socio-environmental restrictions, with the suitability map elaborated from the physical variables and restrictions, without incorporating the classification obtained by deep learning at this stage of the study.

2. Material and Methods

The study adopted an exploratory approach, integrating remote sensing and spatial analysis to support the initial planning of pipeline expansion. A preliminary territorial suitability framework was developed, considering land use and cover by deep learning, physical terrain variables, and socio-environmental constraints. The methodology explored image classification and suitability modeling individually.

The research was conducted in the Far West of Bahia, covering pilot stretches in the municipalities of Buritirama, Barra, Mansidão, Cotegipe, Wanderley, Ibotirama, Morpará, and Xique-Xique. This region was selected due to its diversity of land uses and coverages, varied terrain, and the presence of environmentally sensitive areas, making it representative for the analysis of linear infrastructure planning. The spatial delimitation of the area was carried out in a GIS environment, with a compatible cartographic reference system.

For the land use and land cover classification, multispectral images from the Sentinel-2 satellite (August 2023) were used, obtained free of charge by the Copernicus program (COPERNICUS PROGRAMME – ESA & European Union, 2026). Images with low cloud cover (30%) were selected (Hofig; Araujo Junior, 2015). Slope orientation was incorporated as an exploratory physical determinant.

Proximity analyses were performed with the Distance Accumulation tool from ArcGIS Pro (Esri, 2026; Longley et al., 2015), applied to vector data of water bodies and quilombola areas. These variables were considered absolute restrictions for spatial exclusion. Binary masks (0 for restricted, 1 for unrestricted) were constructed with the Raster Calculator tool (Esri, 2026; Malczewski, 2006), standardizing the restrictions for integration into the suitability model.

The preliminary territorial suitability map was constructed by integrating the binary restriction masks with the reclassified variables of slope and aspect. The Raster Calculator tool with conditional logic (Con) in ArcGIS Pro (Esri, 2026; Malczewski, 2006) was used. This logic prioritized absolute restrictions and, in their absence, considered moderate restrictions from the physical terrain variables (Longley et al., 2015). The resulting map was classified into three categories: Suitable, Conditioned/Low Suitability, and Restricted.

To support the initial analysis of the territorial aptitude map, 1,000 random sample points were generated with the Create Random Points tool, distributed across the study area. The values of the raster layers were extracted for these points using the Extract Multi Values to Points tool (Esri, 2026; Longley et al., 2015). This procedure allowed the samples to be labeled according to the aptitude classes and the percentage distribution of the points to be quantified.

3. Results and Discussion

This research explored a preliminary approach to territorial suitability assessment for pipeline planning, integrating remote sensing and spatial analysis techniques. The results obtained are presented and discussed in two main fronts: the evaluation of the performance of land use and land cover classification by deep learning and the analysis of the preliminary territorial suitability map, built from physical variables and socio-environmental constraints. The distinction between these two approaches is crucial for understanding the meaning of the findings and their implications for linear infrastructure planning.

Initially, the land use and land cover classification, performed using a pre-trained deep learning model applied to Sentinel-2 images, generated a thematic map with several classes. A preliminary visual analysis, comparing this map with reference data from MapBiomas, revealed both thematic convergences and divergences in the spatial distribution of classes. This visual comparison was fundamental for a qualitative understanding of the map’s characteristics before its quantitative validation, aiding in the identification of patterns and inconsistencies in the territory’s representation.

Evaluation of Land Use and Land Cover Classification by Deep Learning

The quantitative validation of the thematic classification, essential for verifying the quality and reliability of the results, was performed using a confusion matrix. The performance indicators revealed an overall accuracy of 44.4% and a Kappa index of approximately 0.30. These values indicate limited model performance and only a modest agreement between the generated classification and the reference data. Although the overall accuracy surpassed the benchmark of 20% expected for a random classification among five classes, the result is not considered satisfactory for operational use (Olofsson et al., 2014; Jensen, 2016).

The detailed analysis of performance by thematic class showed heterogeneous behavior of the model. Class C4, corresponding to intensive anthropic areas, presented producer and user accuracies equal to zero, indicating a complete failure in its identification. This result suggests a significant difficulty for the model in differentiating urbanized areas and other modified surfaces within the study area, which compromises the representation of crucial elements for infrastructure planning.

For class C1, which represents open areas with low vegetation cover, a producer’s accuracy of 60% and a user’s accuracy of 26% were observed. This discrepancy suggests a tendency of the model to overestimate the presence of open areas, possibly assigning this class to locations that, in reality, had other types of cover or plantings. The low reliability for the user (26%) indicates that a significant portion of the areas classified as C1 did not correspond to the reality on the ground, generating commission errors.

The agricultural and low-growing vegetation areas (C2) showed 49% accuracy in identifying real areas (producer’s accuracy) and 61% reliability (user’s accuracy). This class demonstrated greater difficulty in differentiation, which may be related to the spectral similarity between soil preparation areas, crops, and the common low-growing vegetation in the region. The difficulty in distinguishing these categories is a known challenge in remote sensing classifications, especially in dynamic agricultural landscapes (Jensen, 2016).

Regarding mixed and forest vegetated areas (C3), the model correctly identified only 37% of existing areas (producer’s accuracy), but showed a reliability of 71% (user’s accuracy). This pattern suggests a more conservative behavior of the model, where the classification was more precise in the areas it actually identified as forest, but with a high omission of real areas. This implies that many forest areas were not detected, which can be problematic in planning contexts that require the identification of all sensitive areas.

Class C5, which comprises water and wetlands, achieved the best performance in identifying actual areas, with 76% producer’s accuracy. This indicates that the model has a good capacity to recognize the presence of water bodies. However, the user’s accuracy of 56% suggests that a portion of the areas classified as water may have been confused with other elements with similar spectral characteristics, such as shadows or wet soils, resulting in commission errors.

The main classification errors observed, particularly between natural vegetation areas and planted areas, can be attributed to the similarity of the spectral characteristics of these covers for the satellite sensor. Furthermore, the use of a global pre-trained model, which was not adjusted with specific examples from Bahia’s landscapes, likely contributed to the limited performance. The time of year of the satellite images (August), a dry period in Western Bahia, may also have influenced the divergences, as the native vegetation shows less vigor and resembles harvested or preparation agricultural areas (Jansen; Di Gregorio, 2008).

The difference in temporal resolution between the data used also represented a limitation. While the Sentinel-2 images corresponded to August 2023, the MapBiomas reference data were consolidated annually. This temporal incompatibility hindered a direct and precise comparison, introducing uncertainties in the validation. In summary, the deep learning classification, although exploratory, demonstrated the limitations of transferring a pre-trained global model to a specific regional context without local adjustments or calibrations.

Due to limited performance in the classification validation, data derived from the deep learning model were not quantitatively incorporated into the preliminary territorial suitability map at this stage of the study. They remained as a complementary analytical product, used for the characterization of land use and cover and for the interpretation of the territorial context. This methodological decision was crucial to preserve the traceability of the suitability model and avoid the propagation of thematic classification uncertainties to the final spatial screening result.

Territorial Aptitude Map Assessment

The construction of the preliminary territorial suitability map for pipeline expansion was carried out by integrating physical terrain variables and socio-environmental constraints. The slope and aspect variables, derived from the Digital Terrain Model, were reclassified into suitability categories. Slope was categorized as suitable (less than 10%), conditional (between 10% and 30%), and restricted (greater than 30%), based on suitability criteria (Hofig; Araujo Junior, 2015).

Socio-environmental restrictions, such as quilombola areas and bodies of water, were treated as absolute restrictions, represented by binary masks where the value zero indicated a restricted area and the value one, an unrestricted area. These masks were generated through proximity analyses, using geoprocessing tools. The integration logic prioritized absolute restrictions, and in their absence, considered moderate restrictions associated with the physical variables of the terrain (Longley et al., 2015).

The resulting suitability map classified the territory into three decision support categories: Suitable, Conditional/Low Suitability, and Restricted. This explicit hierarchy of criteria and precedences ensures the model’s traceability and reproducibility, making it a transparent tool for spatial screening. It is important to emphasize that the thresholds adopted for the physical variables are indicative and exploratory in nature, and should be parameterized according to technical standards and specific engineering requirements in operational applications.

For a quantitative analysis of the spatial distribution of aptitude classes, one thousand random sample points were generated. The percentage distribution of these points revealed that the Apt class is predominant, corresponding to 46.3% of occurrences. The Conditioned/Low Aptitude category represented 37.0% of the total points, indicating areas that require additional considerations or more complex engineering solutions for the implementation of pipelines.

Restricted Areas accounted for 12.0% of the sample points, reflecting the application of exclusion rules based on absolute restrictions, such as the presence of water bodies and quilombola areas. The remaining 4.7% corresponded to points with no information or other unclassified categories. These percentages describe the distribution of aptitude classes in the analyzed territory, serving to verify the behavior and spatial distribution of the rules implemented in the preliminary model.

The preliminary territorial suitability model, therefore, is not configured as a definitive route design solution for pipelines, but as a spatial screening tool that supports the initial planning stages. It offers a transparent framework of criteria and precedences for organizing spatial information, aiding in the prioritization of areas for more detailed studies. The methodology demonstrated the potential to integrate remote sensing and geoprocessing data to provide robust initial support for decision-making in linear infrastructure projects.

In summary, the research achieved its objective of developing a preliminary framework to support the assessment of territorial suitability, using physical terrain variables and socio-environmental constraints. Although the classification of land use and cover by deep learning showed limited performance and was not directly integrated into the suitability model at this stage, the individual exploration of these techniques, together with spatial analysis, constitutes an important initial support for the planning of linear infrastructures, offering a transparent basis for spatial screening and the identification of priority areas for future investigation.

4. Conclusion

The study aimed to develop and apply a preliminary framework to support the assessment of territorial suitability for pipeline expansion, integrating land use and land cover characterization by deep learning, physical terrain variables, and socio-environmental restrictions. It was found that the land use and land cover classification by deep learning, although exploratory, showed limited performance, with a global accuracy of 44.4% and a Kappa index of 0.305. This performance made its direct integration into the suitability model at this stage unfeasible, due to challenges such as spectral similarity between classes and the absence of local calibration of the pre-trained model. However, a preliminary territorial suitability model was structured, categorized into suitable, conditional, and restricted areas, based on slope, aspect, and absolute socio-environmental restrictions, such as water bodies and quilombola territories. The analysis by sample points indicated that 46.3% of the areas were classified as suitable, 37.0% as conditional, and 12.0% as restricted. The main contribution lies in the proposal of a transparent framework of criteria and precedences for spatial screening, offering robust initial support for the planning of linear infrastructures.

The study’s limitations included the restricted performance of the pre-trained deep learning model, which was not adjusted to regional specificities, and the temporal incompatibility between satellite images and reference data, introducing uncertainties in validation. The aptitude thresholds adopted for the physical variables are indicative and exploratory in nature, requiring parameterization according to engineering standards in operational applications. For future studies, it is recommended to train and fine-tune deep learning models with local samples, perform temporal validation across different seasonal periods, and incorporate new factors into the aptitude model, such as proximity to the road network, geotechnical data, and expropriation costs, in addition to applying multi-criteria and sensitivity analyses. Such advancements would enhance the robustness and reliability of the results, consolidating the methodology as a more comprehensive tool for territorial planning of pipelines.

Bibliographic References

Copernicus Programme; European Union. 2026. Copernicus Data Space Ecosystem: acesso a dados de observação da Terra e modelos digitais de elevação. Disponível em: <https://dataspace.copernicus.eu/>. Acesso em: 08 fev. 2026.

Departamento Nacional de Infraestrutura de Transportes [DNIT]. 2026. VGeo: Visualizador de Informações Geográficas do DNIT. Disponível em: <https://servicos.dnit.gov.br/vgeo/>. Acesso em: 06 fev. 2026.

Di Gregorio, A.; Jansen, L.J.M. 2000. Land Cover Classification System (LCCS): classification concepts and user manual. FAO, Rome, Italy. Disponível em: <https://www.fao.org/3/x0596e/x0596e00.htm>. Acesso em: 12 abr. 2026.

Eastman, J.R. 2012. IDRISI Selva Manual. Clark University, Worcester, MA, USA. Disponível em: <http://uhulag.mendelu.cz/files/pagesdata/eng/gis/idrisi_selva_tutorial.pdf>. Acesso em: 03 fev. 2026.

Esri. 2025. Deep learning using the ArcGIS Image Analyst extension. Disponível em: <https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/deep-learning-in-arcgis-pro.htm>. Acesso em: 11 out. 2025.

Esri. 2026. ArcGIS Pro: Deep Learning Land Cover Classification. Esri, Redlands, CA, USA. Disponível em: <https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview>. Acesso em: 10 fev. 2026.

Jensen, J.R. 2016. Introductory Digital Image Processing: a remote sensing perspective. 4ed. Pearson, New York, NY, USA.

Longley, P.A.; Goodchild, M.F.; Maguire, D.J.; Rhind, D.W. 2015. Geographic Information Science and Systems. 4ed. Wiley, Hoboken, NJ, USA.

Ma, L.; Liu, Y.; Zhang, X.; Ye, Y.; Yin, G.; Johnson, B.A. 2019. Deep learning in remote sensing applications: a meta analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing 152: 166-177.

Malczewski, J. 2006. GIS based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science 20(7): 703-726.

MapBiomas. 2026. Plataforma MapBiomas Brasil: coleções de cobertura e uso da terra no Brasil. Disponível em: <https://brasil.mapbiomas.org/>. Acesso em: 05 fev. 2026.

Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148: 42-57.

Zhu, X.X.; Tu, W.; Mou, L.; Xia, G.S.; Zhang, L.; Xu, F.; Fraundorfer, F. 2017. Deep learning in remote sensing: a comprehensive review. IEEE Geoscience and Remote Sensing Magazine 5(4): 8-36.

Article originating from the Final Course Work of the Specialization in Data Science and Analytics of the MBA USP/Esalq

To learn more about the course, click here and access the MBX Academy platform

Most recent

Economy, Investments And Banking

October 23, 2026

Financial Education, Behaviors and Biases in Financial Decision Making

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

Personal financial management represents a growing challenge, influenced by cognitive biases that affect decision-making, even in individuals with high knowledge. The present study analyzed the relationship between the level of financial education and the occurrence of behavioral biases in the financial decision-making of adults residing in Brazil. An applied, quantitative, and descriptive-correlational field survey was conducted by administering a structured questionnaire to 163 respondents. The instrument included sociodemographic variables, financial knowledge and habits, and hypothetical situations to identify cognitive biases. The results indicated that higher levels of financial education were associated with better financial control, saving, and investment practices, with significant correlations with income and schooling. However, the presence of behavioral biases (anchoring, loss aversion, scarcity, optimism/negativity, present bias, status quo, overconfidence, and mental accounting) persisted even among individuals with higher schooling and income, with no statistically significant association with the analyzed sociodemographic variables. It was concluded that financial education, although essential for financial organization and control, was not sufficient to neutralize the influence of cognitive biases. The incorporation of behavioral approaches into financial education programs is suggested.

Keywords: Financial behavior; Behavioral economics; Personal finance; Cognitive heuristics; Cognitive biases.

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

The inclusion of children with Autism Spectrum Disorder (ASD) in Early Childhood Education presents significant challenges, given the difficulty of adapting to new environments and the intensity of stimuli. The objective was to analyze the teaching performance in Early Childhood Education in the initial schooling process of children with ASD. A case study was conducted, using narrative interview as a data production technique. The results revealed that the teaching performance was characterized by mediation and affection practices, with the flexibility of routine and the use of playful resources as essential strategies for the child’s development and sensory organization. The importance of the school support professional for the success of the pedagogical work was also highlighted. The child’s integral development was promoted through intentional teaching actions that prioritized environmental adaptation, strengthening of bonds, and attention to the specific needs of ASD. As a contribution, a guide of pedagogical strategies aimed at welcoming children with ASD in public daycare centers was developed.

Keywords: Early Childhood Education; School Inclusion; Teacher Narratives; Inclusive Pedagogical Practices; Autism Spectrum Disorder.

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

The expansion of e-commerce and technological transformation have driven the need to understand consumer behavior regarding infoproducts, whose perceived experience has become crucial for professional development. The study aimed to analyze the factors that influenced the purchase decision of infoproducts in the digital environment, considering the stages of the consumer journey. An applied, quantitative, and descriptive research was conducted through a field survey with 100 consumers from Serra Gaúcha. Data collection occurred via a structured questionnaire, using non-probabilistic sampling. The results revealed a qualified audience with strong “mobile-first” behavior. It was found that the availability of detailed information and social approval were the main attraction triggers in the pre-purchase phase. In the transactional and subsequent stages, the minimization of operational friction, the agility of immediate access, and system security proved crucial for conversion and satisfaction, although gaps in human support efficiency were identified. Retention was based on the trust generated by positive past experiences, overcoming the price factor. It was concluded that the infoproduct consumer journey operates as an integrated system, requiring alignment between technological infrastructure, content curation, and support humanization to ensure engagement.

Keywords: E-commerce; Consumer behavior; Consumer experience; Loyalty; Virtual business.

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

Mental health in the workplace is a topic of growing relevance, impacting professional development and employee well-being. A study was conducted to identify aspects and situations that can influence absenteeism levels in a banking institution. The research involved 108 participants and used a structured questionnaire, based on the short version of COPSOQ II by Gonçalves (2021), which contained 45 closed Likert scale questions and two open-ended questions. Data were collected via an online platform and analyzed with a descriptive and quantitative approach. The results revealed a scenario that indicated the need for adjustments in the distribution of activities and in managers’ perception of behaviors and signs of disorders. Important evidence was identified in the participants’ open-ended responses. It was concluded that the study contributed to promoting the perception of a safe environment for mental health in the organization.

Keywords: Absences due to disorders; Professional development; Operational leadership; Medical leave; Mental health.

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

The state of Amazonas faced historical challenges in consolidating the Unified Health System (SUS), stemming from geographical barriers, socioeconomic inequalities, and structural limitations. This study described the development and implementation of a health information geoportal, designed to support monitoring, evaluation, and planning in the Amazonian context. A descriptive case study approach was adopted, based on documentary analysis and secondary data, covering the mapping of functionalities, technical infrastructure, and validation steps of the tool. The Portal was developed with open technologies and integrated multiple national databases, offering interactive dashboards, thematic maps, and a digital document repository. Among the results, highlighted were the georeferenced visualization of key indicators, the possibility of temporal comparative analyses, and the use of the platform in reviewing health plans and redirecting strategic actions. The portal can represent an advance in digital governance and evidence-based management, with potential for replication in other highly complex territories, provided it is accompanied by investments in technological infrastructure, professional training, and inter-institutional coordination.

Keywords: Amazon; Health Evaluation; Digital Health; Health Information Systems.

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

The impact of continuous learning and knowledge management on organizational growth was studied in a context of globalization and constant changes. The study’s objective was to analyze employees’ perception of learning culture and its importance for personal and professional development. Data were obtained through an anonymous survey, with a quantitative approach, using a form with “survey” type questions applied to 87 professionals from the technology, health, education, commerce, and services sectors. The results indicated that employees had a positive perception of the contribution of continuous learning to personal and organizational growth, recognizing its benefits for the development of new skills, innovation, and competitiveness. However, it was observed that the incentive for internal knowledge exchange and the offering of practical training still presented moderate levels of agreement. It was concluded that continuous learning significantly contributes to individual and organizational development, suggesting that companies should develop strategic policies for learning focused on new skills, aiming for greater competitive advantage and innovation.

Keywords: Continuous learning; Self-development; Organizational growth; Knowledge Management; Innovation.

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

A multiple statistical regression model was proposed to estimate the operational performance of UNICAMP’s CampusGrid microgrid, based on internal electrical quantities. The microgrid integrates photovoltaic generation, batteries, and a gas generator. Energy management system data were collected at five-minute intervals over one month. The Operational Reliability Index, defined as the ratio between supplied and demanded energy, was established as the dependent variable. The model was adjusted for the islanded operation mode, evaluating the impact of demand, state of charge, solar generation, and supplied energy. The results revealed high explanatory power, indicating that operational reliability depends on the interaction between supply and demand. It was observed that the nighttime period concentrated the highest rate of partial supply, attributed to the reduction in solar generation. It was concluded that multiple regression offers an interpretable and useful framework for microgrid monitoring and control strategy support.

Keywords: Battery storage; Photovoltaic generation; Reliability Index; Islanded operation; Energy Management System.

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

Aggregate non-payment in the Brazilian financial system is a crucial indicator of credit risk, the prediction of which is regulated by CMN Resolutions n.° 4.557/2017 and 4.966/2021 for the prospective measurement of expected losses. Aggregate non-payment was modeled and predicted using the SARIMAX model, projecting its trajectory until December 2026 under optimistic, base, and pessimistic scenarios. 164 monthly observations (July/2012 to February/2026) and macroeconomic variables such as unemployment, Selic, IPCA, IBC-Br, and household debt were used. The specification with contemporaneous variables was selected based on information criteria and coefficient stability, and residual diagnostics confirmed statistical adequacy, with a MAPE of 3.12%. Historical non-payment ranged between 4.33% and 7.64%. The estimation identified unemployment (β = 0.2534; p < 0.001) and Selic (β = 0.2128; p < 0.001) as the main positive determinants, with strong temporal persistence (AR(1) = 0.8965). Selic showed increasing correlation with non-payment (r = 0.62 at lag 12), while unemployment demonstrated a contemporaneous effect (r = 0.33). Projections indicated non-payment between 6.30% and 7.98% in December 2026, with a range of 1.68 percentage points, highlighting direct implications for provisioning in financial institutions.

Keywords: Unemployment; Predictive modeling; Monetary policy; Loss provisioning; Time series.

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]