Rasmus N. Jørgensen
Orcid: 0000-0002-1329-1674
According to our database1,
Rasmus N. Jørgensen
authored at least 31 papers
between 2013 and 2022.
Collaborative distances:
Collaborative distances:
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Bibliography
2022
CoRR, 2022
2021
Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks.
Sensors, 2021
2020
Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data.
Proceedings of the 23rd Euromicro Conference on Digital System Design, 2020
2019
A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images.
Remote. Sens., 2019
Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN.
Remote. Sens., 2019
Preprocessed Sentinel-1 Data via a Web Service Focused on Agricultural Field Monitoring.
IEEE Access, 2019
Proceedings of the ICVISP 2019: 3rd International Conference on Vision, 2019
The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019
2018
A Novel Locating System for Cereal Plant Stem Emerging Points' Detection Using a Convolutional Neural Network.
Sensors, 2018
Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation.
Frontiers Robotics AI, 2018
Current potentials and challenges using Sentinel-1 for broadacre field remote sensing.
CoRR, 2018
CoRR, 2018
Collaborative model based design of automated and robotic agricultural vehicles in the Crescendo Tool.
CoRR, 2018
2017
Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks.
Sensors, 2017
Sensors, 2017
Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis.
J. Imaging, 2017
CoRR, 2017
2016
Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops.
Sensors, 2016
DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.
Sensors, 2016
Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture.
J. Imaging, 2016
Proceedings of the Image Analysis and Recognition - 13th International Conference, 2016
2015
Robotics, 2015
Object Detection and Terrain Classification in Agricultural Fields Using 3D Lidar Data.
Proceedings of the Computer Vision Systems - 10th International Conference, 2015
2014
Robotics, 2014
2013
Sensors, 2013