We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations.
Ranked #1 on Semantic Segmentation on PASTIS
We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds.
We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on3D data.
In the case of classification tasks, the severity of errors can be summarized under the form of a cost matrix, which assesses the gravity of confusing each pair of classes.
The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike.
Ranked #1 on Time Series Classification on s2-agri
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.
Ranked #2 on Time Series Classification on s2-agri
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints.
Ranked #7 on Semantic Segmentation on S3DIS
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series.
We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points.
Ranked #5 on Semantic Segmentation on Semantic3D