Even though data annotation is extremely important for interpretability, research, and development of artificial intelligence solutions, annotating data remains costly. Research efforts such as active learning or few-shot learning alleviate the cost by increasing sample efficiency, yet the problem of annotating data more quickly has received comparatively little attention. Leveraging a predictor has been shown to reduce annotation cost in practice but has not been theoretically considered. We ask the following question: to annotate a binary classification dataset with N samples, can the annotator answer less than N yes/no questions? Framing this question-and-answer (Q&A) game as an optimal encoding problem, we find a positive answer given by the Huffman encoding of the possible labelings. Unfortunately, the algorithm is computationally intractable even for small dataset sizes. As a practical method, we propose to minimize a cost function a few steps ahead, similarly to lookahead minimization in optimal control. This solution is analyzed, compared with the optimal one, and evaluated using several synthetic and real-world datasets. The method allows a significant improvement (23-86%) in the annotation efficiency of real-world datasets.

We explore an iterative annotation strategy adapted to recurrent multispectral imagery provided by constellations such as Sentinel-2 and applied to the monitoring of events that develop over time. Our key example is the tracking of the progress in the installation of solar power plants. This problem has four difficulties that seem hard to tackle with automatic methods: an unknown and variable spectrum for the solar panels, a variable background, a variable orientation of the panels causing variable cover of the ground, and a variability of lighting and atmosphere transparency due to cloud shadows and water vapor density. We found that each site is different and that only the interactive annotation of the time series can give an acceptable segmentation of the panels. At this point, we describe a weakly monitored interactive annotation tool that predicts the annotation of new paneled zones from an annotation at a previous date. In that way, the human operator intervention is aided and limited to a few corrections from date to date.

Shape from shading (SfS) enables 3D reconstruction of stockpiles from a single image. However, this method requires proper boundary conditions to work properly. Obtaining such Dirichlet and Neumann conditions is equivalent to a segmentation of the heaps. To get a fast and accurate 3D reconstruction, we propose a simple and interactive segmentation method. SfS is then applied on 0.5-meter resolution Synthetic Aperture Radar (SAR) images with more precise boundary conditions. The results show that prior segmentation is preferable to no segmentation for the stockpiles volume estimation problem. Furthermore, we show that the proposed interactive segmentation method reduces the annotation time needed for such a prior segmentation