Marcos Benedito Schimalski

Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

Camile Söthe, Cláudia Maria de Almeida, Marcos Benedito Schimalski et al. · GIScience & Remote Sensing · 2020 · 166 citations · Full text

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Camile Söthe, Cláudia Maria de Almeida, Veraldo Liesenberg et al. · Remote Sensing · 2017 · 132 citations · Full text

Earth Observation, Environmental Monitoring, Engineering +24

Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

Camile Söthe, Michele Dalponte, Cláudia Maria de Almeida et al. · Remote Sensing · 2019 · 125 citations · Full text

Precision Agriculture, Tree Species Classification, Engineering +18

ATSS Deep Learning-Based Approach to Detect Apple Fruits

Leonardo Josoé Biffi, Edson Aparecido Mitishita, Veraldo Liesenberg et al. · Remote Sensing · 2020 · 68 citations · Full text

Convolutional Neural Network, Precision Agriculture, Engineering +17

Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data

Laura Elena Cué La Rosa, Camile Söthe, Raul Queiroz Feitosa et al. · ISPRS Journal of Photogrammetry and Remote Sensing · 2021 · 49 citations · Full text

Image Analysis, Machine Learning, Data Science +12