Ana Paula Marques Ramos

@fct.unesp.br

Ph.D. Assistant Professor in the Department of Cartography
São Paulo State University (Unesp)

Ana Paula Marques Ramos


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https://researchid.co/anaramos
Ph.D. in Cartographic Sciences from the São Paulo State University (Unesp). Currently, she is an Assistant Professor at Unesp in the Department of Cartography. Her experience is regarding Geosciences, with an emphasis on Remote Sensing of Vegetation, and spatial analysis. Recently she started to develop applied research by integrating Geomatics (mainly Remote Sensing of Vegetation, and spatial analysis) and Machine Learning (shallow and deep learning) areas into environmental and precision agriculture issues studies. She is a CNPq Research Productivity Scholarship (2021-2024) in the area of Geosciences (PQ level -two).

EDUCATION

2011-04-01 to 2015-04-30 | Ph.D. (Cartographic Sciences Post-Graduation Program);
2009-03-01 to 2011-03-31 | Master (Cartographic Sciences Post-Graduation Program);
2004-03-01 to 2008-12-31 | Graduated Cartographic Engineer (Cartographic Engineering)

RESEARCH INTERESTS

Develops research in the Geomatics area, focusing on Remote Sensing of Vegetation and Cartography. Has been involved in research focused on the application of Machine Learning (shallow and deep algorithms) in Remote Sensing data.
71

Scopus Publications

4014

Scholar Citations

28

Scholar h-index

48

Scholar i10-index

Scopus Publications

  • Deep learning on segmenting large and narrow rivers with aerial RGB imagery: A comparison of convolutional and Vision-Transformer networks
    Mayara Maezano Faita Pinheiro, Lucas Yuri Dutra de Oliveira, Thiago Edgar Bauce Venancio, Keiller Nogueira, José Marcato Júnior, Wesley Nunes Gonçalves, Danillo Roberto Pereira, Lucas Prado Osco, Ana Paula Marques Ramos
    Remote Sensing Applications Society and Environment, 2026
    Accurate mapping of water resources from high-resolution RGB imagery is essential for environmental management but presents significant challenges, especially for narrow rivers with variable shapes and spectral complexities. While deep learning methods have advanced this field, a comprehensive performance analysis across different architectural paradigms for this specific task is needed. This study presents an investigation into segmenting rivers of varying widths. First, we evaluate the efficacy of established Convolutional Neural Network (CNN) architectures to benchmark their capabilities in mapping both large (¿10 m) and narrow (¡10 m) rivers. Second, addressing the limitations identified in the initial phase, we assess the state-of-the-art SegFormer (Vision Transformer - ViT) in a direct comparison with the versatile DeepLabV3+ and the Segment Anything Model (SAM). Our findings confirm that while CNNs excel in mapping large rivers, with U-Net achieving a 97.43% F1-Score, their performance deteriorates on narrow watercourses. The subsequent analysis reveals that SegFormer substantially outperforms the CNN benchmark on narrow rivers, achieving an F1-Score of 85.99% and an Intersection over Union (IoU) of 75.42%, representing a crucial 6.86% improvement in IoU over DeepLabV3+. Furthermore, while the Foundation Model (SAM) demonstrated superior sensitivity with a Recall of 91.53%, it suffered from severe over-segmentation, resulting in a remarkably lower IoU of 65.98%. This study demonstrates that while CNNs are effective for well-defined rivers and Foundation Models offer high sensitivity, specialized ViT architectures provide the most robust and precise solution for the automated mapping of varied and narrow river systems.
  • Identification of Leucaena leucocephala in Urban Landscapes Using Street-Level Images and Deep Learning
    Danielle Elis Garcia Furuya, Gleison Marrafon, Eduardo Lopes de Lemos, Michelle Tais Garcia Furuya, Robson Diego Silva Gonçalves, Wesley Nunes Gonçalves, José Marcato Junior, Édson Luis Bolfe, Veraldo Liesenberg, Lucas Prado Osco, Ana Paula Marques Ramos
    Urban Science, 2026
    Mapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning.
  • Mapping of urban tree canopy in high-resolution aerial imagery using deep neural networks
    Brian Leite Machado, Rafael Ochi Kikuti, Lucas Prado Osco, José Marcato Junior, Wesley Nunes Gonçalves, Ana Paula Marques Ramos
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2026
    While deep learning has proven effective for urban tree mapping, there is a critical lack of validated benchmarks and comparative methodological studies for the diverse urban landscapes of Brazil. To address this gap, this work presents a deep-learning workflow that produces such maps from 25 cm RGB orthophotos. Images covering ten São Paulo cities were compiled; seven were used for training/validation and three for independent testing. The DeepLabV3 architecture with a ResNet-152 backbone was assessed under three loss configurations: (i) Balanced Cross-Entropy (BCE) baseline, (ii) BCE plus PointRend boundary refinement, and (iii) BCE combined with a 0.5-weighted Dice term. The BCE baseline delivered the top mean IoU (0.83) and F1-Score (0.91). PointRend increased recall but introduced systematic false positives in heterogeneous roofs and shaded riparian zones. The BCE+Dice variant recovered recall without raising commission error, achieving the highest balanced accuracy (0.96). The workflow delineates canopy with fine spatial detail and processes 2.8 × 10⁶ m² in under 30 minutes on a single RTX 4000 Ada workstation, demonstrating a practical, scalable solution for statewide tree-inventory production.
  • Exploring the Segment Anything Model for Mapping Urban Tree Cover in Orbital Imagery
    Gleison Marrafon, Vagner Souza Machado, Lucas Prado Osco, José Marcato Junior, Wesley Nunes Gonçalves, Ana Paula Marques Ramos
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2026
    Urban tree vegetation plays a key role in sustainable urban planning and ecosystem service provision. This study evaluates the performance of the Segment Anything Model (SAM), developed by Meta AI, in the segmentation of urban tree vegetation from orbital PlanetScope imagery. These images were selected due to their high spatial and temporal resolution, which makes them particularly suitable for urban applications. SAM was applied in zero-shot mode, guided by geometric prompts over representative tree-covered areas. The analysis was conducted across three Brazilian cities—Corumbá (MS), Rio Verde (GO), and Valparaíso de Goiás (GO)—using different spectral band compositions. SAM’s performance was evaluated through a combined quantitative and qualitative approach, using reference masks derived from manually annotated tree canopy polygons. Although SAM had not been previously trained on satellite imagery, it achieved an F1-scores close to 70% and recall values around 75%, independently of the spectral band composition provided as input. These results demonstrate the model’s generalization ability—even under spectrally constrained scenarios involving only three bands. Qualitative analysis confirmed spatial consistency in tree crown delineation, particularly in homogeneous areas, while over-segmentation was observed in spectrally heterogeneous environments. While the results are promising for exploratory and semi-automated vegetation mapping, they also underscore need for fine-tuning SAM on satellite data to enhance spatial precision and thematic discrimination. Overall, SAM's modular and prompt-based architecture offers a robust foundation for scalable, supervised remote sensing workflows focused on urban vegetation monitoring.
  • A Machine Learning Framework for Classifying Thermal Stress in Bean Plants Using Hyperspectral Data
    Lucas Prado Osco, Érika Akemi Saito Moriya, Bruna Coelho de Lima, Ana Paula Marques Ramos, José Marcato Júnior, Wesley Nunes Gonçalves, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Ademir Sérgio Ferreira de Araújo, Nilton Nobuhiro Imai, Fábio Fernando de Araújo
    Agriengineering, 2025
    Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530–570 nm) and the red-edge region (700–710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices.
  • Accuracy of High Resolution Digital Cartographic Products with Elevation Control Points
    Mauricio De Souza, Henrique Lopes Siqueira, Márcio Santos Araujo, Lucas Oliveira, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, José Marcato Junior
    Anuario do Instituto De Geociencias, 2025
    The use of Unmanned aerial vehicles (UAVs) as a tool for image acquisition has been applied in several fields, some applications require cartographic products with high accuracy. With this comes the need for planning the acquisition of images and distribution of control points (GCP) so that digital products meet the required level of accuracy. The aim of this work was to investigate whether the quantity of control points as well as their distribution in different altitude planes in elevated ground can improve the accuracy of the generated cartographic products. RGB images captured by an onboard camera with a resolution of 20 MP were used. Images were captured by a multirotor UAV with an overlap of 80% (front and side) and estimated GSD of 0.017 m. The surveyed area of 5.5 ha overflown area had 31 targets surveyed with GNSS RTK, 21 defined as checkpoints (CP) and 12 as ground control points (GCP), which were used in image processing to generate orthomosaic. We evaluated the accuracy of the generated products based on the PEC-PCD. The results showed that when using only 2 GCPs the altimetric errors are high, being the single configuration that did not fit the PEC-PCD scale 1: 1,000 class A. With 5 GCPs we obtained the best RMSE in altimetry (0.026 m). With 6 GCPs we obtained the best RMSE in planimetry (0.046 m). Altimetry is the most sensitive aspect in generating cartographic products, and the use of GCPs in elevation improves altimetric accuracy.
  • Spatial Analysis and Seasonal Variation of Snakebites of the Genera Bothrops and Crotalus in the State of São Paulo
    Guilherme Picoli Sotocorno, Maria Carolina da Mota Serrano, Guilherme Heydi Haiachi, Ana Paula Marques Ramos, Edmur Azevedo Pugliesi
    Boletim De Ciencias Geodesicas, 2025
    Snakebites are classified as a neglected tropical disease and are associated with poverty and climatic oscillations. Accidents caused by snakes can lead to death and cause serious sequelae. This study aims to analyze the spatial distribution patterns of snakebites caused by the genera Bothrops and Crotalus in the State of Sao Paulo, between 2013 and 2022. Snakebite data were gathered from the National System of Notifiable Diseases (SINAN), cartographic and demographic data from the Brazilian Institute of Geography and Statistics (IBGE), and climatic data from the WorldClim platform. All data were organized according to the four seasons (spring, summer, fall, and winter). Spatial data analyses were conducted using ArcGIS Pro 3.4 and GeoDa 1.22, employing univariate and bivariate spatial autocorrelation techniques based on Global and Local Moran’s I indices. The results revealed spatial clustering patterns for both Bothrops and Crotalus in all seasons. The main clusters for Bothrops were in the southern and northwestern regions, while Crotalus clusters were concentrated in the central, northwestern, and northeastern regions. A positive spatial autocorrelation between precipitation and Bothrops incidence rates was observed in three seasons. Non-parametric statistical tests also indicated significant seasonal differences in incidence rates for both snake genera.
  • Assessment of UAV-Based Deep Learning for Corn Crop Analysis in Midwest Brazil
    José Augusto Correa Martins, Alberto Yoshiriki Hisano Higuti, Aiesca Oliveira Pellegrin, Raquel Soares Juliano, Adriana Mello de Araújo, Luiz Alberto Pellegrin, Veraldo Liesenberg, Ana Paula Marques Ramos, Wesley Nunes Gonçalves, Diego André Sant’Ana, Hemerson Pistori, José Marcato Junior
    Agriculture Switzerland, 2024
    Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and public managers to make informed decisions regarding crop health, yield estimation, and resource allocation in Midwest Brazil. The crops (corn) in this region are being damaged by wild pigs and other diseases. For the quantification of corn fields, this paper applies novel computer-vision techniques and a new dataset of corn imagery composed of 1416 256 × 256 images and corresponding labels. We flew nine drone missions and classified wild pig damage in ten orthomosaics in different stages of growth using semi-automatic digitizing and deep-learning techniques. The period of crop-development analysis will range from early sprouting to the start of the drying phase. The objective of segmentation is to transform or simplify the representation of an image, making it more meaningful and easier to interpret. For the objective class, corn achieved an IoU of 77.92%, and for background 83.25%, using DeepLabV3+ architecture, 78.81% for corn, and 83.73% for background using SegFormer architecture. For the objective class, the accuracy metrics were achieved at 86.88% and for background 91.41% using DeepLabV3+, 88.14% for the objective, and 91.15% for background using SegFormer.
  • Water Resources Monitoring in a Remote Region: Earth Observation-Based Study of Endorheic Lakes
    Jeremie Garnier, Rejane E. Cicerelli, Tati de Almeida, Julia C. R. Belo, Julia Curto, Ana Paula M. Ramos, Larissa V. Valadão, Frederic Satge, Marie-Paule Bonnet
    Remote Sensing, 2024
    In the western Andes, climate changes have led to drastic ecological changes during the Pleistocene and Holocene. Given the debate surrounding precipitation pattern changes and the lack of research on lakes in the Chilean Altiplano, this study aims to assess recent climate changes. The paper presents an innovative methodology based on Google Earth Engine (GEE), utilizing fluctuations in water levels in endorheic lakes as natural precipitation indicators. Three lakes (Chungará, Miscanti, and Miniques) in isolated drainage systems were studied, where changes in water levels directly reflect rainfall variations. Data from Landsat-OLI 8, Landsat-ETM+, Landsat-TM 5, and MODIS spanning 31 years were processed using the Google Earth Engine platform. The shapes of the water bodies were extracted using hue saturation value (HSV) composites. The surface areas of the lakes were compared with precipitation data from national meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) using linear regression analyses. Both lake area and rainfall volume showed a decrease over time, with varying trends depending on environmental conditions. However, the analysis consistently indicates a reduction in the area and volume of Chilean lakes corresponding to observed rainfall patterns over the past three decades.
  • A deep learning approach based on graphs to detect plantation lines
    Diogo Nunes Gonçalves, José Marcato Junior, Mauro dos Santos de Arruda, Vanessa Jordão Marcato Fernandes, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya, Lucas Prado Osco, Hongjie He, Lucio André de Castro Jorge, Jonathan Li, Farid Melgani, Hemerson Pistori, Wesley Nunes Gonçalves
    Heliyon, 2024
    Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.
  • The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot
    Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, Jonathan Li, José Marcato
    International Journal of Applied Earth Observation and Geoinformation, 2023
  • The Potential of Visual ChatGPT for Remote Sensing
    Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, José Marcato Junior
    Remote Sensing, 2023
  • A machine learning approach for mapping surface urban heat island using environmental and socioeconomic variables: a case study in a medium-sized Brazilian city
    Michelle Taís Garcia Furuya, Danielle Elis Garcia Furuya, Lucas Yuri Dutra de Oliveira, Paulo Antonio da Silva, Rejane Ennes Cicerelli, Wesley Nunes Gonçalves, José Marcato Junior, Lucas Prado Osco, Ana Paula Marques Ramos
    Environmental Earth Sciences, 2023
  • Defining priorities areas for biodiversity conservation and trading forest certificates in the Cerrado biome in Brazil
    Samuel Fernando Schwaida, Rejane Ennes Cicerelli, Tati de Almeida, Edson Eyji Sano, Carlos Henrique Pires, Ana Paula Marques Ramos
    Biodiversity and Conservation, 2023
  • Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery
    Diogo Nunes Gonçalves, José Marcato, André Caceres Carrilho, Plabiany Rodrigo Acosta, Ana Paula Marques Ramos, Felipe David Georges Gomes, Lucas Prado Osco, Maxwell da Rosa Oliveira, José Augusto Correa Martins, Geraldo Alves Damasceno, Márcio Santos de Araújo, Jonathan Li, Fábio Roque, Leonardo de Faria Peres, Wesley Nunes Gonçalves, Renata Libonati
    International Journal of Applied Earth Observation and Geoinformation, 2023
  • Distribution of cases of congenital heart disease in a hospital in Oeste Paulista
    Bruna Maria Casachi Bernardes de Melo Carapeba, Sérgio Marques Costa, Rogério Giuffrida, Ana Paula Alves Favareto, Ana Paula Marques Ramos, Fabíola de Azevedo Mello, Marcus Vinicius Pimenta Rodrigues, Renata Calciolari Rossi
    Medicina Brazil, 2023
  • A deep learning-based mobile application for tree species mapping in RGB images
    Mário de Araújo Carvalho, José Marcato, José Augusto Correa Martins, Pedro Zamboni, Celso Soares Costa, Henrique Lopes Siqueira, Márcio Santos Araújo, Diogo Nunes Gonçalves, Danielle Elis Garcia Furuya, Lucas Prado Osco, Ana Paula Marques Ramos, Jonathan Li, Amaury Antônio de Castro, Wesley Nunes Gonçalves
    International Journal of Applied Earth Observation and Geoinformation, 2022
  • Using a convolutional neural network for fingerling counting: A multi-task learning approach
    Diogo Nunes Gonçalves, Plabiany Rodrigo Acosta, Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Michelle Taís Garcia Furuya, Jonathan Li, José Marcato Junior, Hemerson Pistori, Wesley Nunes Gonçalves
    Aquaculture, 2022
  • Counting and locating high-density objects using convolutional neural network
    Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta, Diogo Nunes Gonçalves, José Marcato Junior, Ana Paula Marques Ramos, Edson Takashi Matsubara, Zhipeng Luo, Jonathan Li, Jonathan de Andrade Silva, Wesley Nunes Gonçalves
    Expert Systems with Applications, 2022
  • An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods
    Lucas Prado Osco, Danielle Elis Garcia Furuya, Michelle Taís Garcia Furuya, Daniel Veras Corrêa, Wesley Nunes Gonçalvez, José Marcato Junior, Miguel Borges, Maria Carolina Blassioli-Moraes, Mirian Fernandes Furtado Michereff, Michely Ferreira Santos Aquino, Raúl Alberto Laumann, Veraldo Lisenberg, Ana Paula Marques Ramos, Lúcio André de Castro Jorge
    Infrared Physics and Technology, 2022
  • Multicriteria analysis and logistical grouping method for selecting areas to consortium landfills in Paraiba do Sul river basin, Brazil
    Caroline Souza Senkiio, Ana Paula Marques Ramos, Silvio Jorge Coelho Simões, Tatiana Sussel Gonçalves Mendes
    Environmental Earth Sciences, 2022
  • Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network
    Maximilian Jaderson de Melo, Diogo Nunes Gonçalves, Marina de Nadai Bonin Gomes, Gedson Faria, Jonathan de Andrade Silva, Ana Paula Marques Ramos, Lucas Prado Osco, Michelle Taís Garcia Furuya, José Marcato Junior, Wesley Nunes Gonçalves
    Computers and Electronics in Agriculture, 2022
  • Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements
    Ana Paula Marques Ramos, Felipe David Georges Gomes, Mayara Maezano Faita Pinheiro, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalvez, José Marcato Junior, Mirian Fernandes Furtado Michereff, Maria Carolina Blassioli-Moraes, Miguel Borges, Raúl Alberto Alaumann, Veraldo Liesenberg, Lúcio André de Castro Jorge, Lucas Prado Osco
    Precision Agriculture, 2022
  • Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping
    Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins, Maximilian Jaderson de Melo, Diogo Nunes Gonçalves, Daniel Matte Freitas, Ana Paula Marques Ramos, Michelle Taís Garcia Furuya, Lucas Prado Osco, Jonathan de Andrade Silva, Zhipeng Luo, Raymundo Cordero Garcia, Lingfei Ma, Jonathan Li, Wesley Nunes Gonçalves
    International Journal of Applied Earth Observation and Geoinformation, 2022
  • Environmental perception and space legibility: a study in the university context
    Samara Peruzzo Gusman, Ana Paula Marques Ramos, Alba Regina Azevedo Arana
    Atelie Geografico, 2022
  • Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data
    Danielle Elis Garcia Furuya, Lingfei Ma, Mayara Maezano Faita Pinheiro, Felipe David Georges Gomes, Wesley Nunes Gonçalvez, José Marcato Junior, Diego de Castro Rodrigues, Maria Carolina Blassioli-Moraes, Mirian Fernandes Furtado Michereff, Miguel Borges, Raúl Alberto Alaumann, Ednaldo José Ferreira, Lucas Prado Osco, Ana Paula Marques Ramos, Jonathan Li, Lúcio André de Castro Jorge
    International Journal of Applied Earth Observation and Geoinformation, 2021
  • Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
    Luciene Sales Dagher Arce, Lucas Prado Osco, Mauro dos Santos de Arruda, Danielle Elis Garcia Furuya, Ana Paula Marques Ramos, Camila Aoki, Arnildo Pott, Sarah Fatholahi, Jonathan Li, Fábio Fernando de Araújo, Wesley Nunes Gonçalves, José Marcato Junior
    Scientific Reports, 2021
  • Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models
    Diego Bedin Marin, Gabriel Araújo e Silva Ferraz, Lucas Santos Santana, Brenon Diennevan Souza Barbosa, Rafael Alexandre Pena Barata, Lucas Prado Osco, Ana Paula Marques Ramos, Paulo Henrique Sales Guimarães
    Computers and Electronics in Agriculture, 2021
  • Predicting days to maturity, plant height, and grain yield in soybean: A machine and deep learning approach using multispectral data
    Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Regimar Garcia dos Santos, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Lucas Prado Osco, Wesley Nunes Gonçalves, Alexsandro Monteiro Carneiro, José Marcato Junior, Hemerson Pistori, Luciano Shozo Shiratsuchi
    Remote Sensing, 2021
  • A review on deep learning in UAV remote sensing
    Lucas Prado Osco, José Marcato Junior, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gonçalves, Jonathan Li
    International Journal of Applied Earth Observation and Geoinformation, 2021
  • Semantic segmentation of tree-canopy in urban environment with pixel-wise deep learning
    José Augusto Correa Martins, Keiller Nogueira, Lucas Prado Osco, Felipe David Georges Gomes, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Diego André Sant’Ana, Ana Paula Marques Ramos, Veraldo Liesenberg, Jefersson Alex dos Santos, Paulo Tarso Sanches de Oliveira, José Marcato Junior
    Remote Sensing, 2021
  • Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery
    Lucas Prado Osco, Keiller Nogueira, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Lucio André de Castro Jorge, José Marcato Junior, Jefersson Alex dos Santos
    Precision Agriculture, 2021
  • A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery
    Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias, Juliana Batistoti, Mauricio de Souza, Felipe David Georges Gomes, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Lingfei Ma, José Marcato, Wesley Nunes Gonçalves
    ISPRS Journal of Photogrammetry and Remote Sensing, 2021
  • A new strategy to define consortium sanitary landfill siting location based on multicriteria analysis
    Mayara Maezano Faita Pinheiro, Lucas Prado Osco, Tatiana Sussel Gonçalves Mendes, Rejane Ennes Cicerelli, Ana Paula Marques Ramos
    Revista Brasileira De Geografia Fisica, 2021
  • Performance of the random forest image classifier for mapping land use and land cover in the Brazilian cerrado
    Davi Fernando Cho, Samuel Fernando Schwaida, Rejane Ennes Cicerelli, Tati Almeida, Ana Paula Marques Ramos, Edson Eyji Sano
    Anuario do Instituto De Geociencias, 2021
  • Implementation of ecological corridors in the federal district and surrounding based on weighted criteria
    Ryan Seiji Takahashi, Rejane Ennes Cicerelli, Tati Almeida, Edson Eyji Sano, Francisco Contreras, Ana Paula Marques Ramos
    Anuario do Instituto De Geociencias, 2021
  • Tuberculosis space-temporal distribution from 2011 to 2016 in the municipality of Maputo, Mozambique
    Antonio C. Titosse, Marcus Vinícius Pimenta Rodrigues, Ana Paula Marques Ramos, Lucas Prado Osco, Rogério Giuffrida, Elivelton Da Silva Fonseca, Alba Regina Azevedo Arana
    Poblacion Y Salud En Mesoamerica, 2021
  • INTEGRATION OF PHOTOGRAMMETRY AND DEEP LEARNING IN EARTH OBSERVATION APPLICATIONS
    Jose Marcato Junior, Pedro Zamboni, Mariana Campos, Ana Ramos, Lucas Osco, Jonathan Silva, Wesley Goncalves, Jonathan Li
    International Geoscience and Remote Sensing Symposium IGARSS, 2021
  • Predicting eucalyptus diameter at breast height and total height with uav-based spectral indices and machine learning
    Ana Karina Vieira da Silva, Marcus Vinicius Vieira Borges, Tays Silva Batista, Carlos Antonio da Silva Junior, Danielle Elis Garcia Furuya, Lucas Prado Osco, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Ana Paula Marques Ramos, Wesley Nunes Gonçalves, José Marcato Junior, Paulo Eduardo Teodoro, Hemerson Pistori
    Forests, 2021
  • Article atss deep learning-based approach to detect apple fruits
    Leonardo Josoé Biffi, Edson Mitishita, Veraldo Liesenberg, Anderson Aparecido dos Santos, Diogo Nunes Gonçalves, Nayara Vasconcelos Estrabis, Jonathan de Andrade Silva, Lucas Prado Osco, Ana Paula Marques Ramos, Jorge Antonio Silva Centeno, Marcos Benedito Schimalski, Leo Rufato, Sílvio Luís Rafaeli Neto, José Marcato Junior, Wesley Nunes Gonçalves
    Remote Sensing, 2021
  • A machine learning approach for mapping forest vegetation in riparian zones in an atlantic biome environment using sentinel-2 imagery
    Danielle Elis Garcia Furuya, João Alex Floriano Aguiar, Nayara V. Estrabis, Mayara Maezano Faita Pinheiro, Michelle Taís Garcia Furuya, Danillo Roberto Pereira, Wesley Nunes Gonçalves, Veraldo Liesenberg, Jonathan Li, José Marcato Junior, Lucas Prado Osco, Ana Paula Marques Ramos
    Remote Sensing, 2020
  • Brazilian midwest native vegetation mapping based on google earth engine
    N. V. Estrabis, L. Osco, A. P. Ramos, W. N. Gonçalves, V. Liesenberg, H. Pistori, J. Marcato Junior
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2020
  • Mapping utility poles in aerial orthoimages using atss deep learning method
    Matheus Gomes, Jonathan Silva, Diogo Gonçalves, Pedro Zamboni, Jader Perez, Edson Batista, Ana Ramos, Lucas Osco, Edson Matsubara, Jonathan Li, José Marcato Junior, Wesley Gonçalves
    Sensors Switzerland, 2020
  • A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
    Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior, Guilherme Fernando Capristo-Silva, Jonathan Li, Fábio Henrique Rojo Baio, José Marcato Junior, Paulo Eduardo Teodoro, Hemerson Pistori
    Computers and Electronics in Agriculture, 2020
  • Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques
    Lucas Prado Osco, José Marcato Junior, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Wesley Nunes Gonçalves, Fábio Henrique Rojo Baio, Hemerson Pistori, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro
    Remote Sensing, 2020
  • Land use/land cover change dynamics and their effects on land surface temperature in the western region of the state of São Paulo, Brazil
    Rosana Amaral Carrasco, Mayara Maezano Faita Pinheiro, José Marcato Junior, Rejane Ennes Cicerelli, Paulo Antônio Silva, Lucas Prado Osco, Ana Paula Marques Ramos
    Regional Environmental Change, 2020
  • Storm-drain and manhole detection using the retinanet method
    Anderson Santos, José Marcato Junior, Jonathan de Andrade Silva, Rodrigo Pereira, Daniel Matos, Geazy Menezes, Leandro Higa, Anette Eltner, Ana Paula Ramos, Lucas Osco, Wesley Gonçalves
    Sensors Switzerland, 2020
  • Climatic seasonality and water quality in watersheds: a study case in Limoeiro River watershed in the western region of São Paulo State, Brazil
    Felipe David Georges Gomes, Lucas Prado Osco, Patrícia Alexandra Antunes, Ana Paula Marques Ramos
    Environmental Science and Pollution Research, 2020
  • Brazilian Midwest Native Vegetation Mapping Based on Google Earth Engine
    N. V. Estrabis, L. Osco, A. P. Ramos, W. N. Goncalves, V. Liesenberg, H. Pistori, J. Marcato
    2020 IEEE Latin American Grss and ISPRS Remote Sensing Conference Lagirs 2020 Proceedings, 2020
  • A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements
    Lucas Prado Osco, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Nayara Estrabis, Felipe Ianczyk, Fábio Fernando de Araújo, Veraldo Liesenberg, Lúcio André de Castro Jorge, Jonathan Li, Lingfei Ma, Wesley Nunes Gonçalves, José Marcato Junior, José Eduardo Creste
    Remote Sensing, 2020
  • A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
    Lucas Prado Osco, Mauro dos Santos de Arruda, José Marcato Junior, Neemias Buceli da Silva, Ana Paula Marques Ramos, Érika Akemi Saito Moryia, Nilton Nobuhiro Imai, Danillo Roberto Pereira, José Eduardo Creste, Edson Takashi Matsubara, Jonathan Li, Wesley Nunes Gonçalves
    ISPRS Journal of Photogrammetry and Remote Sensing, 2020
  • Semiautomatic extraction of arboreal vegetation in urban areas using aerial imagery of high spatial resolution
    Jakeline Jesus Silva, Lucas Prado Osco, Ana Paula Marques Ramos, Wesley Barbosa Dourado
    Revista Brasileira De Geografia Fisica, 2020
  • Machine learning erosion and vulnerability map validation
    Renata Cristina Mafra, Mayara Maezano Faita Pinheiro, Rejane Ennes Cicerelli, Lucas Prado Osco, Marcelo Rodrigo Alves, Ana Paula Marques Ramos
    Revista Brasileira De Geografia Fisica, 2020
  • Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery
    Lucas Prado Osco, Ana Paula Marques Ramos, Danilo Roberto Pereira, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Edson Takashi Matsubara, Nayara Estrabis, Maurício de Souza, José Marcato Junior, Wesley Nunes Gonçalves, Jonathan Li, Veraldo Liesenberg, José Eduardo Creste
    Remote Sensing, 2019
  • Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks
    Lucas Prado Osco, Ana Paula Marques Ramos, Érika Akemi Saito Moriya, Lorrayne Guimarães Bavaresco, Bruna Coelho de Lima, Nayara Estrabis, Danilo Roberto Pereira, José Eduardo Creste, José Marcato Júnior, Wesley Nunes Gonçalves, Nilton Nobuhiro Imai, Jonathan Li, Veraldo Liesenberg, Fábio Fernando de Araújo
    Remote Sensing, 2019
  • Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images
    Lucas Prado Osco, Ana Paula Marques Ramos, Érika Akemi Saito Moriya, Maurício de Souza, José Marcato Junior, Edson Takashi Matsubara, Nilton Nobuhiro Imai, José Eduardo Creste
    International Journal of Applied Earth Observation and Geoinformation, 2019
  • Mother-to-child transmission and gestational syphilis: Spatial-temporal epidemiology and demographics in a Brazilian region
    Joyce Marinho de Souza, Rogério Giuffrida, Ana Paula Marques Ramos, Glilciane Morceli, Camila H. Coelho, Marcus Vinícius Pimenta Rodrigues
    Plos Neglected Tropical Diseases, 2019
  • Remote sensing applied to the environmental regularization of rural properties in mato grosso do sul
    É. C. C. PINHEIRO, A. P. M. RAMOS, J. MARCATO
    Anuario do Instituto De Geociencias, 2019
  • Analysis of the altimetric accuracy of alos aw3d30 digital surface model for mato grosso do sul
    M. SOUZA, A. P. M. RAMOS, J. MARCATO
    Anuario do Instituto De Geociencias, 2019
  • Pontos de Referência para Navegação por Motoristas com Deficiência na Visão de Cores
    Edmur Azevedo Pugliesi, Aline Moretti, Ana Paula Marques Ramos, Vilma Mayumi Tachibana
    Revista Brasileira De Cartografia, 2019
  • Evaluation of the effects of land use and occupation on superficial water quality of the hydrographic basin of the Pirapozinho river (SP)
    Bruno Magro Rodrigues, Lucas Prado Osco, Patricia Alexandra Antunes, Ana Paula Marques Ramos
    Revista Brasileira De Geografia Fisica, 2019
  • Vertical accuracy assessment of digital surface models using the land management system data base: A case study in the west of são paulo state
    V. S. MACHADO, J. MARCATO, M. K. G. SOUZA, L. P. OSCO, A. P. M. RAMOS
    Anuario do Instituto De Geociencias, 2019
  • Workflow to spectral imagery calibration obtained in remotely piloted aircrafts (RPAs) by the empirical line method
    L. P. OSCO, É. A. S. MORIYA, V. S. MACHADO, G. REZENDE, N. N. IMAI, E. T. MATSUBARA, J. MARCATO, A. P. M. RAMOS, J. E. CRESTE
    Anuario do Instituto De Geociencias, 2019
  • Evaluation of usability of maps of different scales presented in an in-car route guidance and navigation system
    Ana Paula Marques Ramos, Edmur Azevedo Pugliesi, Renan Furlan de Oliveira, Vilma Mayumi Tachibana, Mônica Modesta Santos Decanini
    Boletim De Ciencias Geodesicas, 2018
  • Driver preference concerning in-car route guidance and navigation system maps for drivers with color vision deficiency
    Renan Furlan de Oliveira, Edmur Azevedo Pugliesi, Ana Paula Marques Ramos, Mônica Modesta Santos Decanini
    Boletim De Ciencias Geodesicas, 2018
  • Evaluation of the soil quality at the proximities of the solid waste landfill in Pirapozinho, SP
    L.P. OSCO, M.N. BOIN, M.J. FELÍCIO, A.P.M. RAMOS
    Anuario do Instituto De Geociencias, 2018
  • Characterization of the fragility degrees in geoecological states of the management unit of water resources of the pontal do paranapanema (SP, Brazil)
    Lucas Prado OSCO, Marcos Norberto BOIN, Munir Jorge FELÍCIO, Ana Paula Marques RAMOS
    Pesquisas Em Geociencias, 2017
  • Analysis of the altimetric accuracy of SRTM and aster digital surface models and their application to the 3D representation of nhecolândia pantanal
    L. B. MARINI, J. M. JUNIOR, A. P. M. RAMOS, A. C. P. FILHO, A. P. M. BARROS, A. C. P. FILHO, W. M. BARROS, L. T. HIGA
    Anuario do Instituto De Geociencias, 2017
  • Preference for map scale of in-car route guidance and navigation system
    Ana Paula Marques Ramos, Mônica Modesta Santos Decanini, Edmur Azevedo Pugliesi, Vilma Mayumi Tachibana, Renan Furlan De Oliveira, Jader Mozella Marton Soares
    Boletim De Ciencias Geodesicas, 2016
  • The gender effect on visual demand for maps at different scales in route guidance and navigation system
    Ana Paula Marques Ramos, Mônica Modesta Santos Decanini, Edmur Azevedo Pugliesi, Vilma Mayumi Tachibana
    Boletim De Ciencias Geodesicas, 2015
  • Color preference for route and direction arrow in maps of route guidance systems
    Ana Paula Marques Ramos, Edmur Azevedo Pugliesi, Mônica Modesta Santos Decanini, Vilma Mayumi Tachibana
    Boletim De Ciencias Geodesicas, 2014

RECENT SCHOLAR PUBLICATIONS

  • Identification of Leucaena leucocephala in Urban Landscapes Using Street-Level Images and Deep Learning
    DEG Furuya, G Marrafon, EL Lemos, MTG Furuya, RDS Gonçalves, ...
    Urban Science 10 (4), 192 , 2026
    2026
  • Discriminative Spectral Regions for Detecting Huanglongbing in Citrus Plants through Statistical Analysis
    M Morata Bortoloto Junior, LP Osco, LAC Jorge, APM Ramos
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2026
    2026
  • Exploring the Segment Anything Model for Mapping Urban Tree Cover in Orbital Imagery
    G Marrafon, VS Machado, LP Osco, J Marcato Junior, WN Gonçalves, ...
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2026
    2026
  • Mapping of urban tree canopy in high-resolution aerial imagery using deep neural networks
    BL Machado, RO Kikuti, LP Osco, J Marcato Junior, WN Gonçalves, ...
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information … , 2026
    2026
  • Deep learning on segmenting large and narrow rivers with aerial RGB imagery: A comparison of convolutional and Vision-Transformer networks
    MMF Pinheiro, LYD de Oliveira, TEB Venancio, K Nogueira, JM Júnior, ...
    Remote Sensing Applications: Society and Environment, 101970 , 2026
    2026
  • Spatial Analysis and Seasonal Variation of Snakebites of the Genera Bothrops and Crotalus in the State of São Paulo
    GP Sotocorno, MCM Serrano, GH Haiachi, APM Ramos, EA Pugliesi
    Boletim de Ciências Geodésicas 31, e2025011 , 2025
    2025
  • A Machine Learning Framework for Classifying Thermal Stress in Bean Plants Using Hyperspectral Data
    LP Osco, ÉAS Moriya, BC de Lima, APM Ramos, JM Júnior, ...
    AgriEngineering 7 (11), 376 , 2025
    2025
    Citations: 3
  • MAPEAMENTO DE FRAGMENTOS ARBÓREOS URBANOS COM IMAGENS ORBITAIS E APRENDIZADO PROFUNDO
    LA Silva, F Gomes, LY Oliveira, V Machado, J Júnior, W Gonçalves, ...
    Revista Tamoios 21 (2), 165-185 , 2025
    2025
  • Accuracy of High Resolution Digital Cartographic Products with Elevation Control Points
    M Souza, HL Siqueira, MS Araujo, LYD Oliveira, WN Gonçalves, ...
    Anuário do Instituto de Geociências 48, e59100 , 2025
    2025
    Citations: 1
  • Data augmentation and resolution enhancement using GANs and diffusion models for tree segmentation
    AS Ferreira, APM Ramos, JM Junior, WN Gonçalves
    arXiv preprint arXiv:2505.15077 , 2025
    2025
    Citations: 4
  • Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation.
    A dos Santos Ferreira, APM Ramos, JM Junior, WN Gonçalves
    arXiv preprint arXiv:2505.15077 , 2025
    2025
  • MAPEAMEAMENTO E ANÁLISE MULTITEMPORAL DE CASOS NOTIFICADOS DE HANSENÍASE NA 11ª REDE REGIONAL DE ATENÇÃO DE SAÚDE DO ESTADO DE ASÃO PAULO
    TS de Sousa, APM Ramos, MVP Rodrigues, AR de Azevedo Arana, ...
    Revista Políticas Públicas & Cidades 14 (1), e1661-e1661 , 2025
    2025
  • EVALUATION OF ALTIMETRIC ACCURACY OF DIGITAL SURFACE MODELS IN THE URBAN AREA OF CAMPO GRANDE/BRAZIL
    M De Souza, MKG de Souza, APM Ramos, L Oliveira, WN Gonçalves, J Li, ...
    Revista Tamoios 21 (1) , 2025
    2025
  • TÉCNICAS DE SENSORIAMENTO REMOTO PARA DETECÇÃO DE FEIÇÕES EROSIVAS EM ÁREAS VERDES URBANAS: UMA REVISÃO CRÍTICA
    KS Barros, S Tibcherani, BM Massimino, APM Ramos, LP Osco
    IV CONGRESSO INTERNACIONAL AMBIENTE E SUSTENTABILIDADE (IV CIAS 2025) 1, 182-206 , 2025
    2025
  • A SYSTEMATIC REVIEW OVER MACHINE AND DEEP LEARNING APPLICATIONS ON REMOTE SENSING FOREST CARBON MONITORING
    AOA Correia, VS Machado, APM Ramos, LP Osco
    IV CONGRESSO INTERNACIONAL AMBIENTE E SUSTENTABILIDADE (IV CIAS 2025) 1, 115-128 , 2025
    2025
  • Acurácia de Produtos Cartográficos Digitais de Alta Resolução com Pontos de Controle em Elevação
    M Souza, HL Siqueira, MS Araujo, LYD Oliveira, WN Gonçalves, ...
    Anuário do Instituto de Geociências 48, e59100 , 2025
    2025
  • AVALIAÇÃO DA ACURÁCIA ALTIMÉTRICA DE MODELOS DIGITAIS DE SUPERFÍCIE NA ÁREA URBANA DE CAMPO GRANDE/BRASIL.
    M de Souza, MK Gonçalves de Souza, AP Marques Ramos, L Oliveira, ...
    Revista Tamoios 21 (1) , 2025
    2025
  • Retraction notice to “A deep learning approach based on graphs to detect plantation lines”[Heliyon Volume 10, Issue 11, 15 June 2024, e31730]
    DN Gonçalves, JM Junior, MS de Arruda, VJM Fernandes, APM Ramos, ...
    Heliyon 10 (23) , 2024
    2024
  • Assessment of UAV-Based Deep Learning for Corn Crop Analysis in Midwest Brazil
    JAC Martins, AY Hisano Higuti, AO Pellegrin, RS Juliano, AM de Araújo, ...
    Agriculture 14 (11), 2029 , 2024
    2024
    Citations: 5
  • HOSPITALIZAÇÕES POR INSUFICIÊNCIA RENAL NO ESTADO DE SÃO PAULO: TENDÊNCIAS TEMPORAIS E PADRÕES ESPACIAIS, 2008-2021
    AB Silva, M Souza, A Solera, AP Favareto, R Rossi, E Pugliesi, AP Ramos
    Estudos Geográficos: Revista Eletrônica de Geografia 22 (2), 62-78 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • A review on deep learning in UAV remote sensing
    LP Osco, JM Junior, APM Ramos, LA de Castro Jorge, SN Fatholahi, ...
    International Journal of Applied Earth Observation and Geoinformation 102 … , 2021
    2021
    Citations: 636
  • The segment anything model (sam) for remote sensing applications: From zero to one shot
    LP Osco, Q Wu, EL De Lemos, WN Gonçalves, APM Ramos, J Li, ...
    International Journal of Applied Earth Observation and Geoinformation 124 … , 2023
    2023
    Citations: 492
  • A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
    APM Ramos, LP Osco, DEG Furuya, WN Gonçalves, DC Santana, ...
    Computers and Electronics in Agriculture 178, 105791 , 2020
    2020
    Citations: 343
  • A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
    LP OSCO, MS ARRUDA, J MARCATO JUNIOR, NB SILVA, ...
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 160, 97-106 , 2020
    2020
    Citations: 224
  • Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery
    L Prado Osco, AP Marques Ramos, D Roberto Pereira, ...
    Remote Sensing 11 (24), 2925 , 2019
    2019
    Citations: 170
  • Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques
    LP Osco, JM Junior, APM Ramos, DEG Furuya, DC Santana, ...
    Remote Sensing 12 (19), 3237 , 2020
    2020
    Citations: 163
  • A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery
    LP Osco, MS de Arruda, DN Gonçalves, A Dias, J Batistoti, M de Souza, ...
    ISPRS Journal of Photogrammetry and Remote Sensing 174, 1-17 , 2021
    2021
    Citations: 155
  • A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements
    LP Osco, APM Ramos, MM Faita Pinheiro, ÉAS Moriya, NN Imai, ...
    Remote Sensing 12 (6), 906 , 2020
    2020
    Citations: 134
  • Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery
    LP Osco, K Nogueira, AP Marques Ramos, MM Faita Pinheiro, ...
    Precision Agriculture 22 (4), 1171-1188 , 2021
    2021
    Citations: 102
  • Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models
    DB Marin, LS Santana, BDS Barbosa, RAP Barata, LP Osco, APM Ramos, ...
    Computers and Electronics in Agriculture 190, 106476 , 2021
    2021
    Citations: 96
  • ATSS deep learning-based approach to detect apple fruits
    LJ Biffi, E Mitishita, V Liesenberg, AA Santos, DN Goncalves, NV Estrabis, ...
    Remote Sensing 13 (1), 54 , 2020
    2020
    Citations: 93
  • Semantic segmentation of tree-canopy in urban environment with pixel-wise deep learning
    JAC Martins, K Nogueira, LP Osco, FDG Gomes, DEG Furuya, ...
    Remote Sensing 13 (16), 3054 , 2021
    2021
    Citations: 86
  • Predicting days to maturity, plant height, and grain yield in soybean: A machine and deep learning approach using multispectral data
    PE Teodoro, LPR Teodoro, FHR Baio, CA da Silva Junior, RG dos Santos, ...
    Remote sensing 13 (22), 4632 , 2021
    2021
    Citations: 85
  • Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping
    PO Bressan, JM Junior, JAC Martins, MJ de Melo, DN Gonçalves, ...
    International Journal of Applied Earth Observation and Geoinformation 108 … , 2022
    2022
    Citations: 75
  • The potential of visual ChatGPT for remote sensing
    LP Osco, EL Lemos, WN Gonçalves, APM Ramos, J Marcato Junior
    Remote Sensing 15 (13), 3232 , 2023
    2023
    Citations: 68
  • Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks
    LP Osco, APM Ramos, ÉAS Moriya, LG Bavaresco, BC Lima, N Estrabis, ...
    Remote Sensing 11 (23), 2797 , 2019
    2019
    Citations: 67
  • AS; Imai, NN; Pereira, DR; Creste, JE; Matsubara, ET; et al. A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
    LP Osco, MS De Arruda, J Marcato Junior, NB Da Silva, APM Ramos, ...
    ISPRS J. Photogramm. Remote Sens 160, 97-106 , 2020
    2020
    Citations: 45
  • Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery
    DN Gonçalves, JM Junior, AC Carrilho, PR Acosta, APM Ramos, ...
    International Journal of Applied Earth Observation and Geoinformation 116 … , 2023
    2023
    Citations: 44
  • Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images
    LP Osco, APM Ramos, ÉAS Moriya, M de Souza, JM Junior, ...
    International Journal of Applied Earth Observation and Geoinformation 83, 101907 , 2019
    2019
    Citations: 42
  • Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data
    DEG Furuya, L Ma, MMF Pinheiro, FDG Gomes, WN Gonçalvez, ...
    International Journal of Applied Earth Observation and Geoinformation 105 … , 2021
    2021
    Citations: 41