68 citations · 161 across the 6 of their papers we have counts for
6 papers
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…
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training
Qingyu Li, Yilei Shi, Xiao Xiang Zhu
Accurate and reliable building footprint maps are vital to urban planning and monitoring, and most existing approaches fall back on convolutional neural networks (CNNs) for buildin…
Instance segmentation of buildings using keypoints
Qingyu Li, Lichao Mou, Yuansheng Hua +4
Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods oft…
Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)
Qingyu Li, Yilei Shi, Xin Huang +1
Building footprint maps are vital to many remote sensing applications, such as 3D building modeling, urban planning, and disaster management. Due to the complexity of buildings, th…
Building Segmentation through a Gated Graph Convolutional Neural Network with Deep Structured Feature Embedding
Yilei Shi, Qingyu Li, Xiao Xiang Zhu
Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for th…
Building Footprint Generation Using Improved Generative Adversarial Networks
Yilei Shi, Qingyu Li, Xiao Xiang Zhu
Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a c…