Ana Paula Marques Ramos

@fct.unesp.br

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



                             

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.

67

Scopus Publications

2790

Scholar Citations

27

Scholar h-index

43

Scholar i10-index

Scopus Publications

  • 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, and José Marcato Junior
    Universidade Federal do Rio de Janeiro
    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.

  • Retraction notice to “A deep learning approach based on graphs to detect plantation lines” [Heliyon Volume 10, Issue 11, 15 June 2024, e31730] (Heliyon (2024) 10(11), (S2405844024077612), (10.1016/j.heliyon.2024.e31730))
    Diogo Nunes Gonçalves, Jos'e 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 Andr'e de Castro Jorge, Jonathan Li,et al.
    Elsevier BV

  • 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,et al.
    MDPI AG
    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, and Marie-Paule Bonnet
    MDPI AG
    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,et al.
    Elsevier BV

  • Prototypical Contrastive Network for Imbalanced Aerial Image Segmentation
    Keiller Nogueira, Mayara Maezano Faita-Pinheiro, Ana Paula Marques Ramos, Wesley Nunes Gonçalves, José Marcato Junior, and Jefersson A. Dos Santos
    IEEE
    Binary segmentation is the main task underpinning several remote sensing applications, which are particularly interested in identifying and monitoring a specific category/object. Although extremely important, such a task has several challenges, including huge intra-class variance for the background and data imbalance. Furthermore, most works tackling this task partially or completely ignore one or both of these challenges and their developments. In this paper, we propose a novel method to perform imbalanced binary segmentation of remote sensing images based on deep networks, prototypes, and contrastive loss. The proposed approach allows the model to focus on learning the foreground class while alleviating the class imbalance problem by allowing it to concentrate on the most difficult background examples. The results demonstrate that the proposed method outperforms state-of-the-art techniques for imbalanced binary segmentation of remote sensing images while taking much less training time.

  • 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, and José Marcato
    Elsevier BV

  • The Potential of Visual ChatGPT for Remote Sensing
    Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, and José Marcato Junior
    MDPI AG
    Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. These are known as Visual LLMs and one notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. These models’ abilities to process images based on textual inputs can revolutionize diverse fields, and while their application in the remote sensing domain remains unexplored, it is important to acknowledge that novel implementations are to be expected. Thus, this is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

  • 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, and Ana Paula Marques Ramos
    Springer Science and Business Media LLC

  • 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, and Ana Paula Marques Ramos
    Springer Science and Business Media LLC

  • 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,et al.
    Elsevier BV

  • 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, and Renata Calciolari Rossi
    Universidade de São Paulo. Agência de Bibliotecas e Coleções Digitais
    O objetivo deste trabalho foi analisar a distribuição espaço-temporal dos pacientes com cardiopatias congênitas atendidos no Ambulatório de Cardiologia Pediátrica do Hospital de referência do Oeste Paulista. Realizamos um estudo retrospectivo com análise de dados de base eletrônica e prontuários dos pacientes diagnosticados com cardiopatiacongênita entre os períodos de julho de 2013 a julho de 2018. Foram selecionados 298 prontuários para análise das variáveis de CID-10, gênero, distribuição espacial e série temporal. Foi possível observar que os defeitos septais foram as cardiopatias mais prevalentes, não houve diferença entre os gêneros. Notou-se aumento do diagnóstico a partir de 2014, com implementação do teste do coraçãozinho e 51% dos casos eram da cidade de Presidente Prudente,com maior concentração de casos na região do parque industrial. Há uma relação na incidência das malformações cardíacas com o meio ambiente desfavorável. Os resultados encontrados podem guiar políticas de saúde pública, visando reduzir a exposição da população mais vulnerável, na busca da melhora nos índices de saúde.

  • 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,et al.
    Elsevier BV

  • 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, and Wesley Nunes Gonçalves
    Elsevier BV

  • 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,et al.
    Elsevier BV

  • 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,et al.
    Elsevier BV

  • 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, and Tatiana Sussel Gonçalves Mendes
    Springer Science and Business Media LLC

  • 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,et al.
    Elsevier BV

  • 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, and Wesley Nunes Gonçalves
    Elsevier BV

  • 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,et al.
    Springer Science and Business Media LLC

  • Environmental perception and space legibility: a study in the university context
    Samara Peruzzo Gusman, Ana Paula Marques Ramos, and Alba Regina Azevedo Arana
    Universidade Federal de Goias
    A produção desordenada do espaço urbano gera consequências negativas no bem-estar e na qualidade de vida dos habitantes, promovendo, danos ambientais que podem der irreversíveis. O artigo objetiva analisar a percepção ambiental no ambiente universitário e entender como a legibilidade do espaço ajuda na percepção ambiental. Trata-se de uma pesquisa aplicada e exploratória, utilizando trabalho de campo de abordagem qualitativa, os dados foram obtidos através das entrevistas e observação participante. Os entrevistados foram divididos em três classes: aluno, professor e colaborador. Eles foram questionados sobre o significado do Campus, elementos distintivos do local, indicações do percurso mais comum feito pelos entrevistados e justificativas das indicações dos elementos distintivos. Predominaram definições positivas nas classificações sobre o significado do Campus, sendo: amplo e arborização/árvores, citadas por 25% dos entrevistados. Os resultados obtidos indicam que os mapas mentais foram instrumentos importantes e eficazes, para identificar a construção do conhecimento espacial e legibilidade por parte dos estudantes, professores e servidores do campus. Palavras-chave: Paisagem. Espaço urbano. Universidade. Areas verdes.

  • 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,et al.
    Elsevier BV

  • 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,et al.
    Springer Science and Business Media LLC
    AbstractAccurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.

  • 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,et al.
    MDPI AG
    In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.

  • 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, and Paulo Henrique Sales Guimarães
    Elsevier BV
    Abstract Coffee leaf rust (CLR) is one of the most devastating leaf diseases in coffee plantations. By knowing the symptoms, severity, and spatial distribution of CLR, farmers can improve disease management procedures and reduce losses associated with it. Recently, Unmanned Aerial Vehicles (UAVs)-based images, in conjunction with machine learning (ML) techniques, helped solve multiple agriculture-related problems. In this sense, vegetation indices processed with ML algorithms are a promising strategy. It is still a challenge to map severity levels of CLR using remote sensing data and an ML approach. Here we propose a framework to detect CLR severity with only vegetation indices extracted from UAV imagery. For that, we based our approach on decision tree models, as they demonstrated important results in related works. We evaluated a coffee field with different infestation classes of CLR: class 1 (from 2% to 5% rust); class 2 (from 5% to 10% rust); class 3 (from 10% to 20% rust), and; class 4 (from 20% to 40% rust). We acquired data with a Sequoia camera, producing images with a spatial resolution of 10.6 cm, in four spectral bands: green (530–570 nm), red (640–680 nm), red-edge (730–740 nm), and near-infrared (770–810 nm). A total of 63 vegetation indices was extracted from the images, and the following learners were evaluated in a cross-validation method with 10 folders: Logistic Model Tree (LMT); J48; ExtraTree; REPTree; Functional Trees (FT); Random Tree (RT), and; Random Forest (RF). The results indicated that the LMT method contributed the most to the accurate prediction of early and several infestation classes. For these classes, LMT returned F-measure values of 0.915 and 0.875, thus being a good indicator of early CLR (2 to 5% of rust) and later stages of CLR (20 to 40% of rust). We demonstrated a valid approach to model rust in coffee plants using only vegetation indices and ML algorithms, specifically for the disease's early and later stages. We concluded that the proposed framework allows inferring the predicted classes in remaining plants within the sampled area, thus helping the identification of potential CLR in non-sampled plants. We corroborate that the decision tree-based model may assist in precision agriculture practices, including mapping rust in coffee plantations, providing both an efficient non-invasive and spatially continuous monitoring of the disease.

RECENT SCHOLAR PUBLICATIONS

  • MAPEAMEAMENTO E ANLISE MULTITEMPORAL DE CASOS NOTIFICADOS DE HANSENASE NA 11 REDE REGIONAL DE ATENO DE SADE DO ESTADO DE ASO PAULO
    TS de Sousa, APM Ramos, MVP Rodrigues, AR de Azevedo Arana, ...
    Revista Polticas Pblicas & Cidades 14 (1), e1661-e1661 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 Gonalves, JM Junior, MS de Arruda, VJM Fernandes, APM Ramos, ...
    Heliyon 10 (23) 2024

  • HOSPITALIZAES POR INSUFICINCIA RENAL NO ESTADO DE SO PAULO: TENDNCIAS TEMPORAIS E PADRES ESPACIAIS, 2008-2021
    AB Silva, M Souza, A Solera, AP Favareto, R Rossi, E Pugliesi, AP Ramos
    Estudos Geogrficos: Revista Eletrnica de Geografia 22 (2), 62-78 2024

  • Water Resources Monitoring in a Remote Region: Earth Observation-Based Study of Endorheic Lakes
    J Garnier, RE Cicerelli, T de Almeida, JCR Belo, J Curto, APM Ramos, ...
    Remote Sensing 16 (15), 2790 2024

  • MAPEAMENTO TEMPORAL DA NEOPLASIA DE MAMA EM SO PAULO: INFLUNCIA DA PANDEMIA COVID-19 E DETERMINANTES SOCIODEMOGRFICOS.
    L Baccaro, M Souza, AP Favareto, R Rossi, E Pugliesi, AP Ramos
    Revista Tamoios 20 (2) 2024

  • A deep learning approach based on graphs to detect plantation lines
    DN Gonalves, JM Junior, MS de Arruda, VJM Fernandes, APM Ramos, ...
    Heliyon 10 (11) 2024

  • MACHINE LEARNING INTEGRATION, REMOTE SENSING DATA PREPROCESSING TECHNIQUES TO MAP PESTS COTTON CROPS
    D Correa, F Echer, L Osco, AP Ramos
    Colloquium Agrariae. ISSN: 1809-8215 20 (1) 2024

  • MACHINE LEARNING INTEGRATION, REMOTE SENSING DATA PREPROCESSING TECHNIQUES TO MAP PESTS COTTON CROPS.
    D Veras Correa, F Rafael Echer, L Prado Osco, AP Marques Ramos
    Colloquium Agrariae 20 (1) 2024

  • MAPEAMENTO DE RIOS EM IMAGENS RGB COM APRENDIZAGEM DE MQUINA SUPERVISIONADA.
    MK Gonalves de Souza, MM Faita Pinheiro, DE Garcia Furuya, ...
    Revista Tamoios 20 (1) 2024

  • Assessment of UAV-Based Deep Learning for Corn Crop Analysis in Midwest Brazil
    JAC Martins, AYH Higuti, AO Pellegrin, RS Juliano, AM de Arajo, ...
    Agriculture 14 (11), 2029 2024

  • ANLISE ESPAO-TEMPORAL DO PADRO DE DISTRIBUIO DA MORTALIDADE POR INSUFICINCIA RENAL NO ESTADO DE SO PAULO NO PERODO ENTRE 2008 E 2020
    ALB Solera, MKG de Souza, ABA da Silva, APA Favareto, RC Rossi, ...
    Hygeia: Revista Brasileira de Geografia Médica e da Saúde 20, e2004 2024

  • Prototypical Contrastive Network for Imbalanced Aerial Image Segmentation
    K Nogueira, MM Faita-Pinheiro, APM Ramos, WN Gonalves, JM Junior, ...
    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer 2024

  • Distribuio dos casos de cardiopatias congnitas em um hospital do Oeste Paulista
    BMCB de Melo, SM Costa, R Giuffrida, APA Favareto, APM Ramos, ...
    Medicina (Ribeiro Preto) 56 (4) 2023

  • The segment anything model (sam) for remote sensing applications: From zero to one shot
    LP Osco, Q Wu, EL De Lemos, WN Gonalves, APM Ramos, J Li, ...
    International Journal of Applied Earth Observation and Geoinformation 124 2023

  • ANLISE ESPAO-TEMPORAL DE DOENA PULMONAR OBSTRUTIVA CRNICA EM UMA REGIO DO ESTADO DE SO PAULO.
    G Guilmar Rocha, AN Soller Pires, RC Rossi Silva, ...
    Revista Tamoios 19 (2) 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
    MTG Furuya, DEG Furuya, LYD de Oliveira, PA da Silva, RE Cicerelli, ...
    Environmental Earth Sciences 82 (13), 325 2023

  • The potential of visual chatgpt for remote sensing
    LP Osco, EL Lemos, WN Gonalves, APM Ramos, J Marcato Junior
    Remote Sensing 15 (13), 3232 2023

  • The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
    L Prado Osco, Q Wu, E Lopes de Lemos, W Nunes Gonalves, ...
    arXiv e-prints, arXiv: 2306.16623 2023

  • Defining priorities areas for biodiversity conservation and trading forest certificates in the Cerrado biome in Brazil
    SF Schwaida, RE Cicerelli, T De Almeida, EE Sano, CH Pires, ...
    Biodiversity and Conservation 32 (6), 1807-1820 2023

  • The Potential of Visual ChatGPT For Remote Sensing
    L Prado Osco, E Lopes de Lemos, W Nunes Gonalves, ...
    arXiv e-prints, arXiv: 2304.13009 2023

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
    Citations: 434

  • A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
    APM Ramos, LP Osco, DEG Furuya, WN Gonalves, DC Santana, ...
    Computers and Electronics in Agriculture 178, 105791 2020
    Citations: 251

  • The segment anything model (sam) for remote sensing applications: From zero to one shot
    LP Osco, Q Wu, EL De Lemos, WN Gonalves, APM Ramos, J Li, ...
    International Journal of Applied Earth Observation and Geoinformation 124 2023
    Citations: 198

  • 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
    Citations: 169

  • 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
    Citations: 139

  • 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
    Citations: 131

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