Conrad M. Albrecht

Orcid: 0009-0009-2422-7289

According to our database1, Conrad M. Albrecht authored at least 29 papers between 2015 and 2024.

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

Timeline

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Bibliography

2024
AIO2: Online Correction of Object Labels for Deep Learning With Incomplete Annotation in Remote Sensing Image Segmentation.
IEEE Trans. Geosci. Remote. Sens., 2024

Task Specific Pretraining with Noisy Labels for Remote sensing Image Segmentation.
CoRR, 2024

2023
Biomass Estimation and Uncertainty Quantification From Tree Height.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing.
CoRR, 2023

DeCUR: decoupling common & unique representations for multimodal self-supervision.
CoRR, 2023

Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment.
CoRR, 2023

DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience.
CoRR, 2023

DeepLCZChange: A REMOTE SENSING DEEP LEARNING MODEL ARCHITECTURE FOR URBAN CLIMATE RESILIENCE<sup>CRediT</sup>.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

Semi-Supervised Learning for Hyperspectral Images by Non Parametrically Predicting View Assignment<sup>CRediT</sup>.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

Above Ground Carbon Biomass Estimate with Physics-Informed Deep Network.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

2022
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation.
CoRR, 2022

Aboveground carbon biomass estimate with Physics-informed deep network.
CoRR, 2022

Self-supervised Learning in Remote Sensing: A Review.
CoRR, 2022

Self-Supervised Vision Transformers for Joint SAR-Optical Representation Learning.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Towards Global Forest Biomass Estimators from Tree Height Data.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Monitoring Urban Forests from Auto-Generated Segmentation MAPS.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection.
Proceedings of the IEEE International Conference on Big Data, 2022

Peaks Fusion assisted Early-stopping Strategy for Overhead Imagery Segmentation with Noisy Labels.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
Quantification of Carbon Sequestration in Urban Forests.
CoRR, 2021

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Change Detection from Remote Sensing to Guide OpenStreetMap Labeling.
ISPRS Int. J. Geo Inf., 2020

Next-generation geospatial-temporal information technologies for disaster management.
IBM J. Res. Dev., 2020

Map Generation from Large Scale Incomplete and Inaccurate Data Labels.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019
N-dimensional geospatial data and analytics for critical infrastructure risk assessment.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Learning and Recognizing Archeological Features from LiDAR Data.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2017
Event clustering & event series characterization on expected frequency.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016
Toward large-scale crop production forecasts for global food security.
IBM J. Res. Dev., 2016

IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015
PAIRS: A scalable geo-spatial data analytics platform.
Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015


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