Datasets:

musmb
/

CPAZMaL

@@ -17,7 +17,7 @@ size_categories:
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 # CPAZMAL DATASET
-Machine learning-ready cryospheric SAR dataset combining ascending et descending PAZ acquisitions over Mont-Blanc massif glaciers (2020–2023). The dataset contains dual-polarization (HH+HV but not for the all time serie cf Acquisition Timeline) time-series recorded with timestamps and organized by geographic groups to support GroupKFold evaluation. It consists of 8 main classes, with an average of 8 groups per class. An additional class, STUDY, can be used for evaluation and segmentation tasks (ground truth to come) with 2–4 glacier surface subclasses. Each of the 70 groups corresponds to an irregular polygon of variable size. A dataloader is provided, allowing selection of temporal windows with options for orbit type, percentage/type of geometric distortions, and extraction of all windows of the requested size from all 70 groups.
 **Example**: Request a time-series window of size 16×16 with up to 10% layover in ascending orbit images between January 2020 and December 2020 → produces a dataset of size (3508, 16, 16, 21, 2) with 51 unique groups et 21 timestamps and the following classes repartition:
 |             | 0   | 1   | 2   | 3   | 4   | 5   | 6   | 7    |

 ---
 # CPAZMAL DATASET
+Machine learning-ready cryospheric SAR dataset combining ascending et descending PAZ acquisitions over Mont-Blanc massif glaciers (2020–2023). The dataset contains dual-polarization (HH+HV but not for the all time serie cf Acquisition Timeline) time-series recorded with timestamps and organized by geographic groups to support GroupKFold evaluation. It consists of 8 main classes, with an average of 8 groups per class. An additional class, STUDY, can be used for evaluation and segmentation tasks (ground truth to come) with 2–4 glacier surface subclasses. Each of the 70 groups corresponds to an irregular polygon of variable size. Preprocessing on the original data included radiometric calibration, incidence angle and NEBN noise corrections, geometric transformation via IGN LiDAR HD (1m). A dataloader is provided, allowing selection of temporal windows with options for orbit type, percentage/type of geometric distortions, and extraction of all windows of the requested size from all 70 groups.
 **Example**: Request a time-series window of size 16×16 with up to 10% layover in ascending orbit images between January 2020 and December 2020 → produces a dataset of size (3508, 16, 16, 21, 2) with 51 unique groups et 21 timestamps and the following classes repartition:
 |             | 0   | 1   | 2   | 3   | 4   | 5   | 6   | 7    |