  While the volume of remote sensing data is increasing daily, deep learning in
Earth Observation faces lack of accurate annotations for supervised
optimization. Crowdsourcing projects such as OpenStreetMap distribute the
annotation load to their community. However, such annotation inevitably
generates noise due to insufficient control of the label quality, lack of
annotators, frequent changes of the Earth's surface as a result of natural
disasters and urban development, among many other factors. We present
Adaptively trIggered Online Object-wise correction (AIO2) to address annotation
noise induced by incomplete label sets. AIO2 features an Adaptive Correction
Trigger (ACT) module that avoids label correction when the model training
under- or overfits, and an Online Object-wise Correction (O2C) methodology that
employs spatial information for automated label modification. 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. Moreover, O2C is implemented online without the
need to store updated labels every training epoch. We validate our approach on
two building footprint segmentation datasets with different spatial
resolutions. Experimental results with varying degrees of building label noise
demonstrate the robustness of AIO2. Source code will be available at
https://github.com/zhu-xlab/AIO2.git.
