Carmem Lúcia da Silva Surmani

Mrs. Carmem Lúcia da Silva Surmani


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 July 2024
Verified
Conference Paper
Open access
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CSCarmem Lúcia da Silva Surmani
Carmem Lúcia da Silva Surmani
LSLucas G. M. Santos
Lucas G. M. Santos
MOMariane G. C. Oliveira
Mariane G. C. Oliveira
DPDaniella S. Paiva
Daniella S. Paiva
JNJoanna R. Nogueira
Joanna R. Nogueira
JXJoicymara S. Xavier
Joicymara S. Xavier
A disparidade de gênero é evidente em áreas como a tecnologia. Para enfrentar esse desafio, surgiu o projeto Código X, que promove o empoderamento e a inclusão digital entre meninas de 9 a 14 anos. Inicialmente com aulas presenciais, o projeto se adaptou à pandemia com o 'Código X em Casa' e a 'Trilha Online'. Em 2022, foram introduzidas novas etapas, como a 'Maratona Tech', seguida pela 'Pré-Trilha' em 2023. Formalmente estabelecido como ONG em 2023, o projeto está desenvolvendo material para replicar sua metodologia, visando ampliar o interesse das alunas na Jornada Código X.

23 June 2026 in Agriengineering
Verified
Journal Article
Open access
PDF file
LSLucas Gabryel Maciel dos Santos
Lucas Gabryel Maciel dos Santos
Lucas SantanaLucas Santana
Lucas Santana
MLMarcos David Lopes
Marcos David Lopes
JSJosiane Maria da Silva
Josiane Maria da Silva
CSCarmem Lúcia da Silva Surmani
Carmem Lúcia da Silva Surmani
APAndrea Pagliai
Andrea Pagliai
Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m2 area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially.