Lucas Prado Osco
Orcid: 0000-0002-0258-536X
According to our database1,
Lucas Prado Osco
authored at least 32 papers
between 2019 and 2023.
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Bibliography
2023
The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot.
Int. J. Appl. Earth Obs. Geoinformation, November, 2023
samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM).
J. Open Source Softw., October, 2023
Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery.
Int. J. Appl. Earth Obs. Geoinformation, February, 2023
2022
Expert Syst. Appl., 2022
Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network.
Comput. Electron. Agric., 2022
Int. J. Appl. Earth Obs. Geoinformation, 2022
Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping.
Int. J. Appl. Earth Obs. Geoinformation, 2022
2021
Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.
Sensors, 2021
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data.
Remote. Sens., 2021
Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning.
Remote. Sens., 2021
Ecol. Informatics, 2021
Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models.
Comput. Electron. Agric., 2021
Int. J. Appl. Earth Obs. Geoinformation, 2021
Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
Proceedings of Sixth International Congress on Information and Communication Technology, 2021
2020
Sensors, 2020
Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.
Sensors, 2020
A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements.
Remote. Sens., 2020
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques.
Remote. Sens., 2020
A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images.
Remote. Sens., 2020
A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery.
Remote. Sens., 2020
A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery.
CoRR, 2020
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices.
Comput. Electron. Agric., 2020
2019
Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery.
Remote. Sens., 2019
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks.
Remote. Sens., 2019
Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images.
Int. J. Appl. Earth Obs. Geoinformation, 2019