26 citations · 31 across the 12 of their papers we have counts for
13 papers · 1 filter
Hierarchical Semi-Supervised Active Learning for Remote Sensing
Wei Huang, Zhitong Xiong, Chenying Liu +1
The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is cos…
LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping
Chenying Liu, Wei Huang, Xiao Xiang Zhu
Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a specific modality and a fixed cla…
Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
Chenying Liu, Gianmarco Perantoni, Lorenzo Bruzzone +1
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). Howeve…
Towards a Unified Copernicus Foundation Model for Earth Vision
Yi Wang, Zhitong Xiong, Chenying Liu +8
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…
AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing
Chenying Liu, Hunsoo Song, Anamika Shreevastava +1
Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by h…
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Chenying Liu, Conrad Albrecht, Yi Wang +1
We explore the potential of large-scale noisily labeled data to enhance feature learning by pretraining semantic segmentation models within a multi-modal framework for geospatial a…
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +2
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projec…
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang +1
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level informa…
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham +3
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning. However, most existing approaches learn only common representations ac…
Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Yi Wang, Chenying Liu, Arti Tiwari +4
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys…
DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
Wenlu Sun, Yao Sun, Chenying Liu +1
Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-drive…
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong +3
Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or m…
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Conrad M Albrecht, Chenying Liu, Yi Wang +2
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 interac…