Search Results for author:
Found 12 papers, 7 papers with code
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
AIO2 utilizes a mean teacher model to enhance training robustness with noisy labels to both stabilize the training accuracy curve for fitting in ACT and provide pseudo labels for correction in O2C.
Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing
Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet-S1 (official test set)
(using extra training data)
DeCUR: decoupling common & unique representations for multimodal self-supervision
We propose Decoupling Common and Unique Representations (DeCUR), a simple yet effective method for multimodal self-supervised learning.
Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface.
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
Urban land use structures impact local climate conditions of metropolitan areas.
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Self-supervised pre-training bears potential to generate expressive representations without human annotation.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet (official test set)
(using extra training data)
Self-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
Ranked #3 on
Multi-Label Image Classification
on BigEarthNet
Monitoring Urban Forests from Auto-Generated Segmentation Maps
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction.
Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
Experimental results employing the BigEarthNet-MM dataset demonstrate the benefits of both, the ViT backbones and the proposed multimodal SSL algorithm DINO-MM.
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
A key challenge of supervised learning is the availability of human-labeled data.
Learning and Recognizing Archeological Features from LiDAR Data
We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e. g. IBM PAIRS Geoscope.
