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, et al.
    Remote Sensing Applications Society and Environment, 2026
  • 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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    Heliyon, 2024
  • 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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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, et al.
    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

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