Lucas Prado Osco

@unoeste.br

Faculty of Engineering and Architecture and Urbanism.
University of Western São Paulo (UNOESTE)

Lucas Prado Osco


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https://researchid.co/lucasosco
Currently is a Professor at the University of Western São Paulo (UNOESTE).

EDUCATION

Environmental Engineer, Ph.D. (Environmental Technologies) and Post-Doctoral (Natural Resources) from the Federal University of Mato Grosso do Sul (UFMS).

RESEARCH, TEACHING, or OTHER INTERESTS

Environmental Science, Earth and Planetary Sciences, Computer Vision and Pattern Recognition
46

Scopus Publications

3982

Scholar Citations

30

Scholar h-index

39

Scholar i10-index

Scopus Publications

  • Remote sensing of invasive Australian acacia species: State of the art and future perspectives
    André Große-Stoltenberg, Ivan Lizarazo, Giuseppe Brundu, Vinicius Paiva Gonçalves, Lucas Prado Osco, Cecilia Masemola, Jana Müllerová, Christiane Werner, Ian Kotze, Jens Oldeland
    Wattles Australian Acacia Species Around the World, 2023
    Remote sensing is a rapidly advancing technology with a wide range of applications in ecosystem management. This chapter presents a literature review focusing on ecological applications of remote sensing in the context of invasions of Australian Acacia species (‘wattles’) at the global level. Of ten studied species worldwide, only half, namely A. cyclops, A. dealbata, A. longifolia, A. mearnsii and A. saligna, were studied more than once. Research hotspots are South Africa and Portugal, while large gaps exist elsewhere. The most common study objective is mapping the distribution of invasive wattles using machine learning. Novel approaches using deep learning and citizen science are still largely untapped resources, and comparative approaches to test the transferability of these novel techniques are rare. Coastal dunes and forests are frequently studied, while agroforestry systems, for example, are neglected despite a high interest in using wattles in these habitats. Beyond mapping, remote sensing is used for impact assessments, for example to map effects on nitrogen cycling and water balance, and suggestions have been made on how to include environmental heterogeneity in impact models. However, research in this field is scarce, and further studies as well as conceptual work are required. Other applications include monitoring of invasion after (bio)control, analysing the importance of land use/land cover in the invasion process and modelling invasion dynamics. Phenological information has high potential for mapping wattles, but this possibility needs to be explored further, particularly in combination with environmental impact assessments. The global nature of wattle invasions and recent technological advancements in remote sensing analyses enable both local-scale studies as well as worldwide comparisons to assess context dependency from both a (technical) remote sensing angle and an ecological perspective. We envision that the increased popularity of remote sensing studies on invasive wattles can be projected into the future to fill these research gaps and to inspire remote sensing-based monitoring systems as the backbone of invasion management.
  • 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
    Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model’s performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM’s potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations, encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model’s proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain.
  • 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
    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, Ana Paula Marques Ramos
    Environmental Earth Sciences, 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
    Pantanal is the largest continuous wetland in the world, but its biodiversity is currently endangered by catastrophic wildfires that occurred in the last three years. The information available for the area only refers to the location and the extent of the burned areas based on medium and low-spatial resolution imagery, ranging from 30 m up to 1 km. However, to improve measurements and assist in environmental actions, robust methods are required to provide a detailed mapping on a higher-spatial scale of the burned areas, such as PlanetScope imagery with 3–5 m spatial resolution. As state-of-the-art, Deep Learning (DL) segmentation methods, in specific Transformed-based networks, are one of the best emerging approaches to extract information from remote sensing imagery. Here we combine Transformers DL methods and high-resolution planet imagery to map burned areas in the Brazilian Pantanal wetland. We first compared the performances of multiple DL-based networks, namely Segformer and DTP Transformers methods with CNN-based networks like PSPNet, FCN, DeepLabV3+, OCRNet, and ISANet, applied in Planet imagery, considering RGB and near-infrared within a large dataset of 1282 image patches (512 × 512 pixels). We later verified the generalization capability of the model for segmenting burned areas in different areas, located in the Brazilian Amazon, which is also worldwide known due to its environmental relevance. As a result, the two transformers based-methods, SegFormer (F1-score equals 95.91%) and DTP (F1-score equals 95.15%), provided the most accurate results in mapping burned forest areas in Pantanal. Results show that the combination of SegFormer and RGB+NIR image with pre-trained weights is the best option (F1-score of 96.52%) to distinguish burned from not-burned areas. When applying the generated model in two Brazilian Amazon forest regions, we achieved F1-score averages of 95.88% for burned areas. We conclude that Transformer-based networks are fit to deal with burned areas in two of the most relevant environmental areas of the world using high-spatial-resolution imagery.
  • 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
    Tree species mapping is an important type of information demanded in different study fields. However, this task can be expensive and time-consuming, making it difficult to monitor extensive areas. Hence, automatic methods are required to optimize tree species mapping. Here, we propose a deep learning-based mobile application tool for tree species classification in high-spatial-resolution RGB images. Several deep learning architectures were evaluated, including mobile networks and traditional models. A total of 2,349 images were used, of which 1,174 images consisted of the Dipteryx alata species and 1,175 images of other local species. These images were manually annotated and randomly divided into training (70%), validation (20%), and testing (10%) subsets, considering the five-fold cross-validation. We evaluated the accuracy and speed (GPU and CPU) of all the implemented deep learning architectures. We found out that the traditional networks have the best performance in terms of F1 score; however, mobile networks are faster. Inception V3 model achieved the best accuracy (F1 score of 97.4%), and MobileNet the worst (F1 score of 83.84%). The MobileNet obtained the best classification speed for CPU (with a mean execution time of 102.8 ms) and GPU (72.4 ms) units. For comparison, Inception V3 achieved a mean execution time of 1058.3 ms for CPU and 634.5 ms for GPU. We conclude that the mobile application proposed can be successfully used to run mobile networks and traditional networks for image classification, but the balance between accuracy and execution time needs to be carefully assessed. This mobile app is a tool for researchers, policymakers, non-governmental organizations, and the general public who intends to assess the tree species, providing a GUI-based platform for non-programmers to access the capabilities of deep learning models in complex classification tasks.
  • 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
  • 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
  • Three-dimensional spatial modelling of traffic-induced urban air pollution using the Graz Lagrangian model and GIS
    Farimah Bakhshizadeh, Sarah Fatholahi, Lucas Prado Osco, José Marcato Junior, Jonathan Li
    Geomatica, 2022
  • Discovering Associative Patterns in Healthcare Data
    Diego de Castro Rodrigues, Vilson Siqueira, Fabiano Tavares, Márcio Lima, Frederico Oliveira, Lucas Osco, Wilmar Junior, Ronaldo Costa, Rommel Barbosa
    Lecture Notes in Networks and Systems, 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
  • Machine learning and SLIC for Tree Canopies segmentation in urban areas
    José Augusto Correa Martins, Geazy Menezes, Wesley Gonçalves, Diego André Sant’Ana, Lucas Prado Osco, Veraldo Liesenberg, Jonathan Li, Lingfei Ma, Paulo Tarso Oliveira, Gilberto Astolfi, Hemerson Pistori, José Marcato Junior
    Ecological Informatics, 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
  • Convolutional neural networks to estimate dry matter yield in a guineagrass breeding program using uav remote sensing
    Gabriel Silva de Oliveira, José Marcato Junior, Caio Polidoro, Lucas Prado Osco, Henrique Siqueira, Lucas Rodrigues, Liana Jank, Sanzio Barrios, Cacilda Valle, Rosângela Simeão, Camilo Carromeu, Eloise Silveira, Lúcio André de Castro Jorge, Wesley Gonçalves, Mateus Santos, Edson Matsubara
    Sensors, 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
  • 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
  • Readily dispersible clay in soils from different Brazilian regions by visible, near, and mid-infrared spectral data
    Isabela Mello Silva, Danilo Jefferson Romero, Clécia Cristina Barbosa Guimarães, Marcelo Rodrigo Alves, Lucas Prado Osco, Arnaldo Barros e Souza, Alvaro Pires da Silva, José A.M. Demattê
    International Journal of Remote Sensing, 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
  • Deep learning applied to phenotyping of biomass in forages with uav-based rgb imagery
    Wellington Castro, José Marcato Junior, Caio Polidoro, Lucas Prado Osco, Wesley Gonçalves, Lucas Rodrigues, Mateus Santos, Liana Jank, Sanzio Barrios, Cacilda Valle, Rosangela Simeão, Camilo Carromeu, Eloise Silveira, Lúcio André de Castro Jorge, Edson Matsubara
    Sensors Switzerland, 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
  • Bacillus subtilis can modulate the growth and root architecture in soybean through volatile organic compounds
    Lorrayne Guimarães Bavaresco, Lucas Prado Osco, Ademir Sergio Ferreira Araujo, Lucas William Mendes, Aurenivia Bonifacio, Fábio Fernando Araújo
    Theoretical and Experimental Plant Physiology, 2020
  • A novel deep learning method to identify single tree species in UAV-based hyperspectral images
    Gabriela Takahashi Miyoshi, Mauro dos Santos Arruda, Lucas Prado Osco, José Marcato Junior, Diogo Nunes Gonçalves, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara, Wesley Nunes Gonçalves
    Remote Sensing, 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
  • 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

RECENT SCHOLAR PUBLICATIONS

  • Empowering Remote Sensing Image Analysis with Automated Segmentation using the Segment Anything Model
    Q Wu, LP Osco
    AGU Fall Meeting Abstracts 2023, GC23B-01 , 2023
    2023
    Citations: 1
  • Remote Sensing of Invasive Australian Acacia Species: State of the Art and Future Perspectives
    A Große-Stoltenberg, I Lizarazo, G Brundu, V Paiva Gonçalves, ...
    Wattles: Australian acacia species around the world, 474-495 , 2023
    2023
    Citations: 6
  • samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
    Q Wu, LP Osco
    Journal of Open Source Software 8 (89), 5663 , 2023
    2023
    Citations: 126
  • 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
    2023
    Citations: 30
  • 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
  • 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 Gonçalves, ...
    arXiv e-prints, arXiv: 2306.16623 , 2023
    2023
    Citations: 492
  • 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
  • A deep learning-based mobile application for tree species mapping in RGB images
    M de Araújo Carvalho, JM Junior, JAC Martins, P Zamboni, CS Costa, ...
    International Journal of Applied Earth Observation and Geoinformation 114 … , 2022
    2022
    Citations: 27
  • Using a convolutional neural network for fingerling counting: A multi-task learning approach
    DN Gonçalves, PR Acosta, APM Ramos, LP Osco, DEG Furuya, ...
    Aquaculture 557, 738334 , 2022
    2022
    Citations: 19
  • An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods
    LP Osco, DEG Furuya, MTG Furuya, DV Correa, WN Goncalvez, ...
    Infrared Physics & Technology 123, 104203 , 2022
    2022
    Citations: 18
  • Counting and locating high-density objects using convolutional neural network
    MS de Arruda, LP Osco, PR Acosta, DN Goncalves, JM Junior, ...
    Expert Systems with Applications 195, 116555 , 2022
    2022
    Citations: 37
  • 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
  • Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network
    MJ de Melo, DN Gonçalves, MNB Gomes, G Faria, J de Andrade Silva, ...
    Computers and Electronics in Agriculture 195, 106818 , 2022
    2022
    Citations: 27
  • Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements
    APM Ramos, FDG Gomes, MMF Pinheiro, DEG Furuya, WN Gonçalvez, ...
    Precision Agriculture 23 (2), 470-491 , 2022
    2022
    Citations: 38
  • Three-dimensional spatial modelling of traffic-induced urban air pollution using the Graz Lagrangian model and GIS
    F Bakhshizadeh, S Fatholahi, L Prado Osco, J Marcato Junior, J Li
    Geomatica 75 (4), 253-268 , 2022
    2022
    Citations: 2
  • 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
  • Machine learning and SLIC for Tree Canopies segmentation in urban areas
    JAC Martins, G Menezes, W Goncalves, DA Sant’Ana, LP Osco, ...
    Ecological informatics 66, 101465 , 2021
    2021
    Citations: 22
  • 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
  • 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
  • Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
    LSD Arce, LP Osco, MS Arruda, DEG Furuya, APM Ramos, C Aoki, A Pott, ...
    Scientific Reports 11 (1), 19619 , 2021
    2021
    Citations: 13

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: 637
  • 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 Gonçalves, ...
    arXiv e-prints, arXiv: 2306.16623 , 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 De Arruda, JM Junior, NB Da Silva, APM Ramos, ...
    ISPRS Journal of Photogrammetry and Remote Sensing 160, 97-106 , 2020
    2020
    Citations: 226
  • 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
  • samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
    Q Wu, LP Osco
    Journal of Open Source Software 8 (89), 5663 , 2023
    2023
    Citations: 126
  • A novel deep learning method to identify single tree species in UAV-based hyperspectral images
    GT Miyoshi, MS Arruda, LP Osco, J Marcato Junior, DN Gonçalves, ...
    Remote Sensing 12 (8), 1294 , 2020
    2020
    Citations: 124
  • Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery
    LP Osco, K Nogueira, APM Ramos, MMF Pinheiro, DEG Furuya, ...
    Precision Agriculture, 1-18 , 2021
    2021
    Citations: 102
  • Deep learning applied to phenotyping of biomass in forages with UAV-based RGB imagery
    W Castro, J Marcato Junior, C Polidoro, LP Osco, W Gonçalves, ...
    Sensors 20 (17), 4802 , 2020
    2020
    Citations: 101
  • 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 Gonçalves, 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
  • Bacillus subtilis can modulate the growth and root architecture in soybean through volatile organic compounds
    LG Bavaresco, LP Osco, ASF Araujo, LW Mendes, A Bonifacio, FF Araujo
    Theoretical and Experimental Plant Physiology 32 (2), 99-108 , 2020
    2020
    Citations: 52