Ana Paula Marques Ramos

Orcid: 0000-0001-6633-2903

According to our database1, Ana Paula Marques Ramos authored at least 27 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Water Resources Monitoring in a Remote Region: Earth Observation-Based Study of Endorheic Lakes.
Remote. Sens., August, 2024

Prototypical Contrastive Network for Imbalanced Aerial Image Segmentation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023
The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot.
Int. J. Appl. Earth Obs. Geoinformation, November, 2023

The Potential of Visual ChatGPT for Remote Sensing.
Remote. Sens., July, 2023

Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery.
Int. J. Appl. Earth Obs. Geoinformation, February, 2023

2022
Counting and locating high-density objects using convolutional neural network.
Expert Syst. Appl., 2022

Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network.
Comput. Electron. Agric., 2022

A deep learning-based mobile application for tree species mapping in RGB images.
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
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

ATSS Deep Learning-Based Approach to Detect Apple Fruits.
Remote. Sens., 2021

A Deep Learning Approach Based on Graphs to Detect Plantation Lines.
CoRR, 2021

Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models.
Comput. Electron. Agric., 2021

A review on deep learning in UAV remote sensing.
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

Integration of Photogrammetry and Deep Learning in Earth Observation Applications.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
Storm-Drain and Manhole Detection Using the RetinaNet Method.
Sensors, 2020

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method.
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 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


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