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Found 10 papers, 4 papers with code
AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing
The results indicate the potential of AutoLCZ as promising avenue for large-scale LCZ mapping from RS data.
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
We study the potential of noisy labels y to pretrain semantic segmentation models in a multi-modal learning framework for geospatial applications.
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.
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations.
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
The increasing availability of multi-sensor data sparks wide interest in 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.
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)
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.
Naive Gabor Networks for Hyperspectral Image Classification
Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase.
